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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoder... | 60 |
'''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 | 0 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int | None = None , lowerCAmelCase_ : int | None = None ):
"""simple docstring"""
if start is None:
lowerCAmelCase__ = 0
if end is None:
... | 61 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 0 |
snake_case = 8.314462 # Unit - J mol-1 K-1
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL... | 62 |
'''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 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinis... | 63 |
'''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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[Any] = logging.get_logger(__name__)
lowercase_ : Dict = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at ... | 64 |
'''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 | 0 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCAmelCase ( __UpperCamelCase = 5000 ):
'''simple docstring'''
... | 65 |
'''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 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # ... | 66 |
'''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 | 0 |
def SCREAMING_SNAKE_CASE__ ( snake_case__ :int ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_lowercase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_lowercase = 1
if upper_limit ... | 67 |
'''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 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from trans... | 68 |
'''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 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
return abs(_UpperCAmelCase ) if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
def __UpperCAmelCase ( _UpperCAmelCase : int , _Uppe... | 69 |
'''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 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _SCREAMING_SNAKE_CASE ... | 70 |
'''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 | 0 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowerCamelCase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 71 |
'''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 | 0 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ... | 72 |
'''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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : List[Any] = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https... | 73 |
'''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 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available... | 74 |
'''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 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCamelCase__ = logging.getLogger(__name__)
@dataclass
class lowerCamelCase_ ... | 75 |
'''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 | 0 |
"""simple docstring"""
from collections.abc import Callable
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : float = a
__lowercase : float = b
if function(__UpperCamelCase ) == 0: ... | 76 |
'''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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration... | 77 |
'''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 | 0 |
'''simple docstring'''
from typing import Any
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : list , snake_case_ : dict , snake_case_ : dict , snake_case_ : dict , ) -> list:
'''simple docstring'''
... | 78 |
'''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 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
SCREAMING_SNAKE_CASE__ : Dict = """\
@misc{chen2021evaluating,
... | 79 |
'''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 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWi... | 80 |
'''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 | 0 |
import inspect
import unittest
from transformers import BitConfig
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 BackboneTesterMixin
from ...test_con... | 81 |
'''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 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
... | 82 |
'''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 | 0 |
"""simple docstring"""
from collections import defaultdict
def snake_case_ ( A_ : str, A_ : str ):
'''simple docstring'''
_lowerCamelCase : List[str] = first_str.lower().strip()
_lowerCamelCase : List[str] = ... | 83 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ... | 84 |
'''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 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from t... | 85 |
'''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 | 0 |
from math import isqrt
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 ,isqrt(__UpperCamelCase ) + 1 ) )
def __snake_case ( __UpperCamelCase : int = 10**6 ):
"... | 86 |
'''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 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned... | 87 |
'''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 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_com... | 88 |
'''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 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> list:
_lowercase : Optional[Any] = len(lowerCamelCase_ )
for i in range(1 , lowerCamelCase_ ):
_lowercase : Tuple = collection[i]
_lowercase : str = 0
_lowercase ... | 89 |
'''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 | 0 |
'''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_convbert import ConvBertTokenizer
__UpperCAmelCase =... | 90 |
'''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 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMS... | 91 |
'''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 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : list[list] ) -> list[list]:
lowercase : Dict =current_set.copy()
for row_index, row in enumerate(__magic_name__ ):
lowercase : Union[str, Any] =row[0]
for column_... | 92 |
'''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 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n... | 93 |
'''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 | 0 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 94 |
'''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 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
fr... | 95 |
'''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 | 0 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a ( ) -> List[Any]:
with offline(Offline... | 96 |
'''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 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 |
'''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 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
... | 98 |
'''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 | 0 |
from __future__ import annotations
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , __A ):
__a = data
__a = None
__a = None
def a (lowerCAmelCase__ ): # In Order traversal of the tree
... | 99 |
'''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 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mod... | 100 |
'''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 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common impor... | 101 |
'''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 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return sum(c * (x**i) for i, c in enumerate(SCREAMING_SNAKE_CASE ) )
def UpperCamelCase (SCREAMING_SNAKE_CASE , ... | 102 |
'''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 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Config... | 103 |
'''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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"""configuration_perceiver""": ["""PE... | 104 |
'''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 | 0 |
from __future__ import annotations
class lowerCAmelCase_ :
def __init__( self ,snake_case__ = 0 ):
SCREAMING_SNAKE_CASE_ : Optional[int] = key
def snake_case ( self ,snake_case__ ,snake_case__ ):
assert isinstance(snake_case__ ... | 105 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 0 |
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase_ ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[str]=None , **lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
A = [x.s... | 106 |
'''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 | 0 |
'''simple docstring'''
import functools
def _SCREAMING_SNAKE_CASE ( __snake_case : list[int] , __snake_case : list[int] ):
# Validation
if not isinstance(__snake_case , __snake_case ) or not all(isinstance(__snake_case , __snake_case ) for day in days ):... | 107 |
'''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 | 0 |
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,
EulerAncestralDiscreteScheduler,
EulerDiscre... | 108 |
'''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 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
a = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-... | 109 |
'''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 | 0 |
import pprint
import requests
lowercase_ = '''https://zenquotes.io/api'''
def __lowerCAmelCase ( ) -> int:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __lowerCAmelCase ( ) -> Dict:
return requests.get(API_ENDPOINT_URL + """/ran... | 354 |
'''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 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCamelCase__ : str ) -> List[str]:
return " ".join(
"".join(word[::-1] ) if len(lowerCAmelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 572 |
'''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 | 0 |
'''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_... | 368 |
'''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 | 0 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : 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 in octets)
if __name__ == "__main__":
a_ = input().... | 25 |
'''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 | 0 |
"""simple docstring"""
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __UpperCAmelCase ( __UpperCamelCase=None ):
if subparsers is not None:
__lowercase : Any = subparsers.add_parser('''test''' )
else... | 76 |
'''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 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ ):
def __lt__( self : Optional[int] , a_ ... | 69 |
'''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 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A_ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def __Up... | 604 |
'''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 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
Diff... | 652 |
'''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 | 0 |
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 snake_case_ :
'''simple docstring'''
@property
def UpperCAmelCase ( ... | 375 |
'''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 | 0 |
def UpperCamelCase ( _A = 1000000 ):
"""simple docstring"""
__magic_name__ : Tuple = [i - 1 for i in range(limit + 1 )]
for i in range(2, limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i, limit + 1, ... | 324 |
'''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 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
__lowerCamelCase : Any = logging.getLogg... | 323 |
'''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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
... | 354 |
'''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 | 0 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
fr... | 572 |
'''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 | 0 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
def _snake_c... | 368 |
'''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 | 0 |
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 HeunDiscreteScheduler
from ..... | 25 |
'''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 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Dict = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b
... | 76 |
'''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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Any = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 69 |
'''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 | 0 |
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 import DDIMScheduler, DDPM... | 604 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_to... | 652 |
'''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 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 375 |
'''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 | 0 |
def UpperCamelCase ( _A ):
"""simple docstring"""
return "".join([hex(lowerCAmelCase__ )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase__ )] )
def UpperCamelCase ( _A ):
"""simple docstring"""
if (len(lowerCAmel... | 324 |
'''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 | 0 |
from maths.prime_factors import prime_factors
def lowerCamelCase_(lowerCamelCase_ ) -> str:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase = F'Input value of [number={number}] must be an integer'
raise TypeError(lowerCAmelCase__ ... | 323 |
'''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 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 354 |
'''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 | 0 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCo... | 572 |
'''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 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __lowerCamelCase ( UpperCAmelCase_ ) ->int:
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() ... | 368 |
'''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 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCamelCase__ ( _a , _a , _a , _a , _a , _a):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
SCREAMING_SNAKE_CASE : Any ... | 25 |
'''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 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( lowerCAmelCase_ ):
UpperCamelCase =(KDPMaDiscreteScheduler,)
UpperCamelCase ... | 76 |
'''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 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...... | 69 |
'''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 | 0 |
from itertools import permutations
def __UpperCAmelCase ( UpperCAmelCase )-> Optional[Any]:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 604 |
'''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 | 0 |
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
from transformer... | 652 |
'''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 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> Union[str, Any]:
__lowercase = set(lowerCAmelCase__ ), [start]
while stack:
__lowercase = stack.pop()
explored.add(lowerCAmelCase__ )
# D... | 375 |
'''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 | 0 |
from __future__ import annotations
from random import random
class snake_case__ :
def __init__( self , lowerCAmelCase__ = None ) -> Optional[Any]:
__magic_name__ : Union[str, Any] = value
__magic_name__ : Optional[Any] = random()
__magic_name... | 324 |
'''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 | 0 |
import string
from math import logaa
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
UpperCAmelCase = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
UpperCAmelCase = do... | 323 |
'''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 | 0 |
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 __lowerCAmelCase ( __lowerCamelCase : List[Any] ) -> List[Any]:
# encoder.embeddin... | 354 |
'''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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ : Dict = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
... | 572 |
'''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 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 368 |
'''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 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
s... | 25 |
'''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 | 0 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
a_ = 'us-east-1' # defaults region
@dataclass
class UpperCAmelCase_ :
UpperCamelCase =42
UpperCamelCase ="arn:aws:iam::558105141721:role/sagemaker_execution_role"
UpperCamelC... | 76 |
'''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 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobe... | 69 |
'''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 | 0 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 604 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class A_ ( lowerCAmelCase_ ):
def __init__( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ ... | 652 |
'''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 | 0 |
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_ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : int = r'''\n Args:\n ... | 375 |
'''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 | 0 |
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 Version
__magic_name__: List[str] =... | 324 |
'''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 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self : Optional[int] , UpperCamelCase__ : Tuple="" , UpperCamelCase__ : Union[str, Any]="train" ) -> Union[str, A... | 323 |
'''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 | 0 |
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()
def __lowerCAme... | 354 |
'''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 | 0 |
"""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 ...t... | 572 |
'''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 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''microsoft/unispeech-sat-ba... | 368 |
'''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 | 0 |
import numpy as np
from transformers import Pipeline
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Any = np.max(lowerCAmelCase__ , axis=-1 , keepdims=lowerCAmelCase__)
SCREAMING_SNAKE_CASE : Dict = np.exp(outputs - maxes)
return shifted_exp / shifted_ex... | 25 |
'''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 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGe... | 76 |
'''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 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( _UpperCAmelCase : list[float] ) -> str:
if len(lowerCAmelCase__ ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i in n... | 69 |
'''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 | 0 |
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