code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __magic_name__ :
def __init__( self , snake_case) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict =data
... | 446 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMS... | 687 | 0 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCAmelCase__ : str =numpy.array([0, 0])
lowerCAmelCase__ : Dict =numpy.array([0.5, 0.8_6_6_0_2_5_4])
lowerCAmelCase__ : Tuple ... | 101 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase_( __magic_name__ : str , __magic_name__ : str ):
"""simple docstring"""
_lowerCAmelCase :Optional[int] = list(__magi... | 687 | 0 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_a : str ... | 56 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Eva... | 687 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 485 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
... | 687 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepE... | 573 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 687 | 0 |
def __lowerCAmelCase ( A_ : list ) -> List[Any]:
__UpperCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__UpperCAmelCase = True
for i in range(0 , len(A_ ) - 1 , 2 ): # iterating over... | 221 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCAmelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
lowerCamelCase : Optio... | 687 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagToken... | 94 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a = """"""
a = """"""
a = """"""
a = 1 # (0 is vertical, 1 is horizontal)
def UpperCamelCase_( ):
"""simple docstring"""... | 687 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Union[str, Any] = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFor... | 429 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
a = logging.get_logger(__name__)
def UpperCamelCase_( __magic_name__ : Optional[int] , __magic_name__ : Union[str, Any] ... | 687 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMix... | 575 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a = 6_3_7_8_1_3_7.0
a = 6_3_5_6_7_5_2.3_1_4_2_4_5
a = 6_378_137
def UpperCamelCase_( __magic_name__ : float , __magic_name__ : floa... | 687 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase_ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
... | 338 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
class UpperCAmelCase_ (snake_case__ ):
"""simple docstring"""
lowerCamelCase : Dict = 'encoder-decoder'
lowerCamelCase :... | 687 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 600 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backb... | 687 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'vocab_file': 'vocab.txt',
'merges_file': 'bp... | 446 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
a = HfApi()
a = {}
# fmt: off
a = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
1.2_3_4... | 687 | 0 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __lowercase (snake_case__ ):
"""simple docstring"""
def __init__( self ... | 101 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self: int ):
_lowerC... | 687 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identif... | 56 |
def UpperCamelCase_( __magic_name__ : int ):
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not..... | 687 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
'configuration_speech_to_text': ['SPEEC... | 485 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self: List[Any] , _UpperCAmelCase: int , _UpperCAmelCase: MutableSequence[float] ):
if len(_UpperCAmelCase ) != degree... | 687 | 0 |
"""simple docstring"""
from typing import Any
def UpperCAmelCase ( A : list ):
'''simple docstring'''
if not input_list:
return []
_UpperCAmelCase = [input_list.count(A ) for value in input_list]
_UpperCAmelCase = max(A )... | 573 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}
try:
if not i... | 687 | 0 |
from typing import Any
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self: int , __lowerCAmelCase: Any ) -> Dict:
'''simple docstring'''
__UpperCAmelCase = data
__UpperCAmelCase = None
class Up... | 221 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase_( __magic_name__ : str , __magic_name__ : float | Decimal , __magic_name__ : float = 10**-10 ):
"""simple docstrin... | 687 | 0 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
SCREAMING_SNAKE_CASE = Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE = Mapping[str, An... | 94 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class... | 687 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
fr... | 429 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the ref... | 687 | 0 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onn... | 575 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : Optional[str] = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be trained.'} )... | 687 | 0 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : int = 600851475143 ):
try:
a__ : int = int(lowerCAmelCase__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
... | 688 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
a__ : List[str] = len(lowerCAmelCase__ )
a__ : int = [[0] * n for i in range(lowerCAmelCase__ )]
for i in rang... | 688 | 1 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __a ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : float ):
# For applying gaussian function for each element in matrix.
a__ : str = math.sqrt(lowerCAmelCase__ )
... | 688 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64... | 688 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE = get_tests_... | 688 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_util... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 688 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import ... | 688 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common im... | 688 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@... | 688 | 1 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__SCREAMING_SNAKE_CASE ... | 688 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, ... | 688 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 688 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = 3
class low... | 688 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE = logging.get_lo... | 688 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git w... | 688 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, ... | 688 | 1 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = R'\n Args:\n ... | 688 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : ... | 688 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 688 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
if __name__ ... | 688 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCAmelCase__ :
"""simple docstring"""
... | 688 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
__UpperCamelCase = (KDP... | 688 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 688 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : bytes ):
return "".join([hex(lowerCAmelCase__ )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase__ )] )
def __a ( lowerCAmelCase__ : str ):
# Check data validity, following RFC3548
... | 688 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( lowerCAmelCase__ : List[Any] ):
# encoder.embeddings are do... | 688 | 1 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ) -> Any:
'''simple docstring'''
a__ ... | 688 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = 3
class low... | 688 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
clas... | 688 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for te... | 688 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = ... | 688 |
'''simple docstring'''
import enum
import shutil
import sys
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = shutil.get_terminal_size()
__SCREAMING_SNAKE_CASE = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowerCAmelCase__ ( enum.Enum ):
"""... | 688 | 1 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
f... | 688 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 1 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_... | 688 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 688 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAM... | 688 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelera... | 688 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYa... | 688 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {'vo... | 688 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if... | 688 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class lowerCAmelCase__ :
"""simple... | 688 | 1 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def __a ( lowerCAmelCase__ : int ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
a__ : List[str] = F'Input value of [number={number}] must be an integer'
raise... | 688 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
a__ : List[str] = len(lowerCAmelCase__ )
a__ : int = [[0] * n for i in range(lowerCAmelCase__ )]
for i in rang... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Dict , A__ : int | None = None ) -> Optional[Any]:
'''simple docstring'''
... | 688 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : str = "The quick brown fox jumps over the lazy dog" , ):
a__ : int = set()
# Replace all the whitespace in our sentence
a__ : Any = input_str.replace(''' ''' , '''''' )
for alpha ... | 688 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_util... | 688 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E... | 688 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import ... | 688 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowerCAmelCase... | 688 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@... | 688 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from t... | 688 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 688 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : int ):
a__ : int = x
a__ : List[str] = y
for step in range(lowerCAm... | 688 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 688 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
# TODO Update this
__SCREAMING_SNAKE_CASE = {
'facebook/es... | 688 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE = logging.get_lo... | 688 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase__ ( lowerCAmelC... | 688 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, ... | 688 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase__ ( lowerC... | 688 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : ... | 688 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransf... | 688 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
if __name__ ... | 688 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common impo... | 688 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
__UpperCamelCase = (KDP... | 688 | 1 |
'''simple docstring'''
import math
def __a ( lowerCAmelCase__ : int ):
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return ... | 688 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _... | 688 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 688 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( lowerCAmelCase__ : List[Any] ):
# encoder.embeddings are do... | 688 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Tuple ) -> int:
'''simple docstring'''
a__ : Opti... | 688 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = 3
class low... | 688 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def __lowerCAmelCase ( A__ : ArgumentParser ) -> Lis... | 688 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for te... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : int | float | str ):
try:
a__ : Dict = float(lowerCAmelCase__ )
except ValueError:
raise ValueError('''Please enter a valid number''' )
a__ : Optional[int] = decimal... | 688 |
'''simple docstring'''
import enum
import shutil
import sys
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = shutil.get_terminal_size()
__SCREAMING_SNAKE_CASE = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowerCAmelCase__ ( enum.Enum ):
"""... | 688 | 1 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 1 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __a ( lowerCAmelCase__ : Optional[int] ):
a__ : Any = np.max(lowerCAmelCase__ , axis=-1 , keepdims=lowerCAmelCase__ )
a__ : Dict = np.exp(outputs - maxes... | 688 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , ):
a__ : List[str] = [redshift, radiation_density, matter_density, dark_e... | 688 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelera... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def __a ( lowerCAmelCase__ : Callable[[int | float], int | float] , lowerCAmelCase__ : int | float , lowerCAmelCase__ : int | float , lowerCAmelCase__ : int = 100 ... | 688 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {'vo... | 688 | 1 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __a ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Tuple ):
a__ : Tuple = {
'''en''': '''Machine lea... | 688 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class lowerCAmelCase__ :
"""simple... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : int ):
if digit_amount > 0:
return round(number - int(lowerCAmelCase__ ) , lowerCAmelCase__ )
return number - int(lowerCAmelCase__ )
if __name__ == "__main__":
prin... | 688 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
a__ : List[str] = len(lowerCAmelCase__ )
a__ : int = [[0] * n for i in range(lowerCAmelCase__ )]
for i in rang... | 688 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
# TODO: upload to AWS
__SCREAMING_SNAKE_CASE = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/... | 688 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64... | 688 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester... | 688 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_util... | 688 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 688 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import ... | 688 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterM... | 688 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@... | 688 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@... | 688 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 688 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_g... | 688 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : int ):
a__ : List[str] = u
for i in range(1 , lowerCAmelCase__ ):
a__ : str = temp * (u - i)
... | 688 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE = logging.get_lo... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] | None = None , lowerCAmelCase__ : dict[str, float] | None = None , lowerCAmelCase__ : bool = False , ):
a__ : Any = cipher_... | 688 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, ... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( lowerCAmelCase__ : dict , lowerCAmelCase__ : str ):
a__ , a__ : int = set(lowerCAmelCase__ ), [start]
while stack:
a__ : List[str] = stack.pop()
... | 688 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : ... | 688 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__na... | 688 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
if __name__ ... | 688 | 1 |
'''simple docstring'''
from itertools import permutations
def __a ( lowerCAmelCase__ : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
a__ : Dict =... | 688 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
__UpperCamelCase = (KDP... | 688 | 1 |
'''simple docstring'''
from math import pi, sqrt, tan
def __a ( lowerCAmelCase__ : float ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __a ( lowerCAmelCase__ : float... | 688 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _... | 688 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __a... | 688 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( lowerCAmelCase__ : List[Any] ):
# encoder.embeddings are do... | 688 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_m... | 688 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__SCREAMING_SNAKE_CASE = 4
__SCREAMING_SNAKE_CASE = 3
class low... | 688 | 1 |
'''simple docstring'''
from collections.abc import Callable
def __a ( lowerCAmelCase__ : Callable[[float], float] , lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
a__ : float = a
a__ : float = b
if function(lowerCAmelCas... | 688 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for te... | 688 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [0, 2, 4, 6, 8]
__SCREAMING_SNAKE_CASE = [1, 3, 5, 7, 9]
def __a ( lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int ):
if remaining_length ==... | 688 |
'''simple docstring'''
import enum
import shutil
import sys
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = shutil.get_terminal_size()
__SCREAMING_SNAKE_CASE = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowerCAmelCase__ ( enum.Enum ):
"""... | 688 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'google/umt5-small': 'https:... | 688 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less ... | 688 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : str ):
a__ : Tuple = 0
# if input_string is "aba" than new_input_string become "a|b|a"
a__ : str = ''''''
a__ : int = ''''''
# append each character + "|" in new_strin... | 688 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelera... | 688 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
... | 688 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {'vo... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : str ):
a__ : Optional[int] = [int(lowerCAmelCase__ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(lowerCAmelCase__ ) == 4 and all(0 <= int(lowerCAmelCase__ ) <= 254 for octet ... | 688 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class lowerCAmelCase__ :
"""simple... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
a__ : List[str] = len(lowerCAmelCase__ )
a__ : int = [[0] * n for i in range(lowerCAmelCase__ )]
for i in rang... | 688 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
a__ : List[str] = len(lowerCAmelCase__ )
a__ : int = [[0] * n for i in range(lowerCAmelCase__ )]
for i in rang... | 688 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 688 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64... | 688 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.util... | 688 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_util... | 688 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Info... | 688 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import ... | 688 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def __a ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray | None = None , ):
a__ : Union[str, Any] = np.shape... | 688 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@... | 688 | 1 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__SCREAMING_SNAKE_CASE = models.Sequential(... | 688 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 688 | 1 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataColla... | 688 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 688 | 1 |
'''simple docstring'''
from statistics import mean, stdev
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : int = 3 ):
a__ : List[Any] = min(lowerCAmelCase__ )
a__ : Optional[int] = max(lowerCAmelCase__ )
# normalize data... | 688 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE = logging.get_lo... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : int ):
if num < 0:
return False
a__ : int = num
a__ : int = 0
while num > 0:
a__ : List[str] = rev_num * 10 + (num % 10)
num //= 10
retur... | 688 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, ... | 688 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
f... | 688 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : ... | 688 | 1 |
'''simple docstring'''
import numpy as np
__SCREAMING_SNAKE_CASE = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class lowerCAmelCase__ :
"""simple docstring"""
... | 688 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
if __name__ ... | 688 | 1 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__SCREAMING_SNAKE_CASE = collections.namedtuple... | 688 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
__UpperCamelCase = (KDP... | 688 | 1 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : int ):
a__ : str = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __a ( lowerCAmelCase__ : int = 100 ):
a__ : List[Any] = 1
... | 688 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _... | 688 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
if __name__ ... | 688 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( lowerCAmelCase__ : List[Any] ):
# encoder.embeddings are do... | 688 | 1 |
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