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 |
|---|---|---|---|---|
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_A : Optional[int] = """\
"""
_A : Any = """
Perplexity (PPL) is one of the most common metrics for evaluatin... | 100 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNA... | 107 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgu... | 101 | '''simple docstring'''
_UpperCAmelCase : Any = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from ... | 107 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 102 | '''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ft... | 107 | 0 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transfor... | 103 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 107 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common im... | 104 | '''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine... | 107 | 0 |
UpperCamelCase__ : Optional[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __UpperCAmelCase ( lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : ... | 105 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers imp... | 107 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available... | 106 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_UpperCAmelCase : List[Any] = False
class lowercase_ ... | 107 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
__a: Optional[int] = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resamp... | 108 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 107 | 0 |
'''simple docstring'''
import cva
import numpy as np
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : float ,lowerCamelCase : int ):
'''simple docstring'''
if k in (0.04, 0.06):
__SCREAMING_SNAKE_CASE = ... | 109 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 107 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Optional[Any] = [
'encoder.version',
'decoder.version',
'mo... | 110 | '''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] ) -> Dict:
_A = [
... | 107 | 0 |
from datetime import datetime as dt
import os
from github import Github
UpperCAmelCase : str = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def _SCREAMING_SNAKE_CASE ( ) -... | 239 | '''simple docstring'''
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ):
from torch.utils.cpp_extension import load
_A = Path(__snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
_A = [
root / filename
for filena... | 107 | 0 |
from scipy.stats import pearsonr
import datasets
_lowercase : Optional[Any] ='''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the... | 364 | '''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tran... | 107 | 0 |
'''simple docstring'''
def _a (lowercase__ : list[list] ) -> List[str]:
"""simple docstring"""
__snake_case = current_set.copy()
for row_index, row in enumerate(__snake_case ):
__snake_case = row[0]
for column_index, colu... | 56 | '''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
_A = []
_A = 2
_A = int(math.sqrt(__snake_case ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
i... | 107 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
fro... | 365 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConf... | 107 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , A , A , A , A=None , A=None ) -> Any:
A: str = start
A: Tuple = ... | 135 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Lis... | 107 | 0 |
"""simple docstring"""
def _a ( ) -> Optional[int]:
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(__snake_case , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
... | 482 | '''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str , __snake_case : List[Any]=False ):
if isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case ):
_A = len(set_a.intersec... | 107 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class snake_case ( _UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = """MCTCTFeatureExtractor"""
UpperCAmelCase : str = """Aut... | 393 | '''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 107 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
snake_case__ : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yon... | 278 | '''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_UpperCAmelCase : int = logging.getLogger(__name__)
class lowercase_ :
"""si... | 107 | 0 |
import argparse
import datetime
def __lowerCAmelCase ( __snake_case ):
__lowerCAmelCase = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
... | 367 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_layoutlmv2''': ['''LAYOUT... | 107 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__magic_name__ : List[str] = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
... | 615 | '''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_O... | 107 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Dict ... | 362 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : str = {
'''configuration_vision_encoder_decoder''': ['''VisionEnco... | 107 | 0 |
import unittest
from transformers import LiltConfig, 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
from ...test_modeling_common import ModelTesterMixin, ... | 239 | '''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case : int | str ):
_A = str(__snake_case )
return n == n[::-1]
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_0_0_0_0_0_0 ):
_A = 0
f... | 107 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 364 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNA... | 107 | 0 |
'''simple docstring'''
def _a (lowercase__ : list[list[int]] , lowercase__ : int , lowercase__ : int , lowercase__ : set ) -> Union[str, Any]:
"""simple docstring"""
__snake_case , __snake_case = len(__snake_case )... | 56 | '''simple docstring'''
_UpperCAmelCase : Any = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from ... | 107 | 0 |
'''simple docstring'''
import heapq
def _lowercase ( UpperCamelCase__ : dict ):
__A : Union[str, Any] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq work... | 365 | '''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ft... | 107 | 0 |
'''simple docstring'''
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 (
... | 135 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 107 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , Up... | 482 | '''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine... | 107 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 393 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers imp... | 107 | 0 |
import os
from datetime import datetime as dt
from github import Github
snake_case__ : List[Any] = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def __lowerCamelCase ( ) -> List[str]:
lowerCamelCase_ : int = Github(os.environ["""GITHUB_TO... | 278 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_UpperCAmelCase : List[Any] = False
class lowercase_ ... | 107 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[int] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCH... | 367 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 107 | 0 |
from __future__ import annotations
__magic_name__ : Optional[int] = []
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
... | 615 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 107 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 362 | '''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] ) -> Dict:
_A = [
... | 107 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> int:
__A : Dict = 1.5
__A ... | 239 | '''simple docstring'''
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ):
from torch.utils.cpp_extension import load
_A = Path(__snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
_A = [
root / filename
for filena... | 107 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokeniz... | 364 | '''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tran... | 107 | 0 |
'''simple docstring'''
class _lowercase :
def __init__( self : Optional[int] ) -> None:
__snake_case = {} # Mapping from char to TrieNode
__snake_case = False
def a ( self : List[str] , SCREAMING_SNAKE_CAS... | 56 | '''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
_A = []
_A = 2
_A = int(math.sqrt(__snake_case ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
i... | 107 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowercase : Optional[Any] = (DDIMParallelScheduler,)
__lowercase : List... | 365 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConf... | 107 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, log... | 135 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Lis... | 107 | 0 |
"""simple docstring"""
from __future__ import annotations
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> List[str]:
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate ... | 482 | '''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str , __snake_case : List[Any]=False ):
if isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case ):
_A = len(set_a.intersec... | 107 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCAmelCase ( UpperCAmelCase ,UpperCAme... | 393 | '''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 107 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
snake_case__ : Any = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.... | 278 | '''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_UpperCAmelCase : int = logging.getLogger(__name__)
class lowercase_ :
"""si... | 107 | 0 |
class _UpperCamelCase :
def __init__( self , __UpperCamelCase )-> None:
__lowerCAmelCase = size
__lowerCAmelCase = [0] * size
__lowerCAmelCase = [0] * size
@staticmethod
d... | 367 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_layoutlmv2''': ['''LAYOUT... | 107 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils... | 615 | '''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_O... | 107 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class lowercase ( _UpperCamelCase ):
def __init__( self , *snake_case , **snake_case ):
warnings... | 362 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : str = {
'''configuration_vision_encoder_decoder''': ['''VisionEnco... | 107 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCAmelCase : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCAmelCase : str = [file for file in fil... | 239 | '''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case : int | str ):
_A = str(__snake_case )
return n == n[::-1]
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_0_0_0_0_0_0 ):
_A = 0
f... | 107 | 0 |
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ):
from torch.utils.cpp_extension import load
lowerCamelCase_ : Dict = Path(__snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
lowerCamelCase_ : Any = [
... | 364 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNA... | 107 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.model... | 56 | '''simple docstring'''
_UpperCAmelCase : Any = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from ... | 107 | 0 |
'''simple docstring'''
import baseaa
def _lowercase ( UpperCamelCase__ : str ):
return baseaa.aaaencode(string.encode('utf-8' ) )
def _lowercase ( UpperCamelCase__ : bytes ):
return baseaa.aaadecode(__snake_case ).decode('utf-8' ... | 365 | '''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ft... | 107 | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf,... | 135 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 107 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class A__( _UpperCamelCase ):
lowerCAmelCase = field(default='''langua... | 482 | '''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine... | 107 | 0 |
from ...processing_utils import ProcessorMixin
class snake_case ( _UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = """WhisperFeatureExtractor"""
UpperCAmelCase : str = """WhisperTokenizer"""
def __init__( self : Any ... | 393 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers imp... | 107 | 0 |
import cmath
import math
def __lowerCamelCase ( A__ : float , A__ : float , A__ : float , A__ : float ) -> int:
lowerCamelCase_ : Union[str, Any] = math.radians(__snake_case )
lowerCamelCase_ : Optional[int] = math.rad... | 278 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_UpperCAmelCase : List[Any] = False
class lowercase_ ... | 107 | 0 |
def __lowerCAmelCase ( __snake_case , __snake_case ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 107 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, sa... | 615 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 107 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=_UpperCamelCase ):
__SCREAMING_SNAKE_CASE : str = ['''torch''', '''scipy''']
def __init__( self , *snake_case , **snake_case ):
requires_backends(self , ['torch', 'scip... | 362 | '''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] ) -> Dict:
_A = [
... | 107 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Any = {name: getattr(transformers, name + '''Fast'... | 239 | '''simple docstring'''
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ):
from torch.utils.cpp_extension import load
_A = Path(__snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
_A = [
root / filename
for filena... | 107 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase : Tuple =logging.get_logger(__name__)
... | 364 | '''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tran... | 107 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp impor... | 56 | '''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
_A = []
_A = 2
_A = int(math.sqrt(__snake_case ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
i... | 107 | 0 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_availabl... | 365 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConf... | 107 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : Any ):
'''simple docstring'''
A: Optional[Any] = len(__snake_case )
while cur > 1:
# Find the maximum number in arr
A: Optional[int] = arr.index(max(arr[0:cur] ... | 135 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Lis... | 107 | 0 |
"""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
... | 482 | '''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str , __snake_case : List[Any]=False ):
if isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case ):
_A = len(set_a.intersec... | 107 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to c... | 393 | '''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 107 | 0 |
def __lowerCamelCase ( A__ : str ) -> int:
lowerCamelCase_ : Optional[int] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCamelCase_ : Optional[int] = """"""
lowerCamelCase_ : Dict = """"""
# append each character + "|" in n... | 278 | '''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_UpperCAmelCase : int = logging.getLogger(__name__)
class lowercase_ :
"""si... | 107 | 0 |
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
lowerCamelCase : Any = ... | 367 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_layoutlmv2''': ['''LAYOUT... | 107 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
... | 615 | '''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_O... | 107 | 0 |
import random
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ):
'''simple docstring'''
snake_case_ = {i: [] for i in range(__snake_case )}
# if probability is greater or equal than 1, then generate a co... | 362 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : str = {
'''configuration_vision_encoder_decoder''': ['''VisionEnco... | 107 | 0 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _SCREAMING_SNAKE_CASE ( a , a=7 ) -> Union[str, Any]:
__A : Any = None
if token is not None:
__A : Union[str, Any] ... | 239 | '''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case : int | str ):
_A = str(__snake_case )
return n == n[::-1]
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_0_0_0_0_0_0 ):
_A = 0
f... | 107 | 0 |
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
lowerCamelCase_ : Optional[int] = len(__snake_case )
for i in range(length - 1 ):
lowerCamelCase_ : Any = i
for k in range(i + 1 ,__snake_case ):
if collection[k] < collection... | 364 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNA... | 107 | 0 |
'''simple docstring'''
def _a (lowercase__ : int ) -> Union[str, Any]:
"""simple docstring"""
__snake_case = abs(__snake_case )
__snake_case = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _a (l... | 56 | '''simple docstring'''
_UpperCAmelCase : Any = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from ... | 107 | 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 FlaxXLMRobertaModel
@require_sentencepi... | 365 | '''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ft... | 107 | 0 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
__SCREAMING_SNAKE_CASE : Any ='''path-to-your-trained-model'''
__SCREAMING_SNAKE_CASE : Optional[int] =StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
... | 135 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 107 | 0 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> List[Any]:
__SCREAMING_SNAKE_CASE = min(__snake_case ) # min() finds the minimum value
__SCREAMING_SNAKE_CASE = max(__snake_case ) # max() finds the maximum value
__SCREAMING_SNAKE_CASE ... | 482 | '''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine... | 107 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A_ = 3_0_0 # TEMPERATURE (unit = K)
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,)-> List[Any]:
'''simple docstring'''
if donor_conc <= 0:
rais... | 393 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers imp... | 107 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
... | 278 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_UpperCAmelCase : List[Any] = False
class lowercase_ ... | 107 | 0 |
import inspect
import unittest
from transformers import YolosConfig
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_common import ConfigTester
from ...test... | 367 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 107 | 0 |
import os
def a_ ( __lowerCAmelCase = "matrix.txt" ):
with open(os.path.join(os.path.dirname(__snake_case ) , __snake_case ) ) as in_file:
lowerCAmelCase__ = in_file.read()
lowerCAmelCase__ = [[int(__snake_case ) for cell in row.split(''',''' ... | 615 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 107 | 0 |
from sklearn.metrics import fa_score
import datasets
_UpperCAmelCase : Dict = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
_UpperCAmelCase : str = '''
Args:
pre... | 362 | '''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] ) -> Dict:
_A = [
... | 107 | 0 |
import functools
def _SCREAMING_SNAKE_CASE ( a , a ) -> Union[str, Any]:
# Validation
if not isinstance(__snake_case , __snake_case ) or not all(isinstance(__snake_case , __snake_case ) for day in days ):
raise ValueError('The parameter days should be a... | 239 | '''simple docstring'''
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ):
from torch.utils.cpp_extension import load
_A = Path(__snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
_A = [
root / filename
for filena... | 107 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 364 | '''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tran... | 107 | 0 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 56 | '''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
_A = []
_A = 2
_A = int(math.sqrt(__snake_case ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
i... | 107 | 0 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transform... | 365 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConf... | 107 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : Dict ={
'''configuration_perceiver''': [''... | 135 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Lis... | 107 | 0 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> str:
if edge <= 0 or not isinstance(__snake_case , __snake_case ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _a ( U... | 482 | '''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str , __snake_case : List[Any]=False ):
if isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case ):
_A = len(set_a.intersec... | 107 | 0 |
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
A_ = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def UpperCAmelCas... | 393 | '''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 107 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import c... | 278 | '''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_UpperCAmelCase : int = logging.getLogger(__name__)
class lowercase_ :
"""si... | 107 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : int = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 367 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_layoutlmv2''': ['''LAYOUT... | 107 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SN... | 615 | '''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_O... | 107 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCamelCase ( UpperCamelCase__... | 362 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : str = {
'''configuration_vision_encoder_decoder''': ['''VisionEnco... | 107 | 0 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
UpperCAmelCase : Tuple = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
UpperCAmelCase : int = _LazyModule(__name__, glob... | 239 | '''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case : int | str ):
_A = str(__snake_case )
return n == n[::-1]
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_0_0_0_0_0_0 ):
_A = 0
f... | 107 | 0 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 364 | '''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNA... | 107 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _a () -> List[Any]:
"""simple docstring"""
__snake_case , __snake_case = 9, 1_4 # noqa: F841
__snake_case = ... | 56 | '''simple docstring'''
_UpperCAmelCase : Any = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from ... | 107 | 0 |
'''simple docstring'''
def _lowercase ( UpperCamelCase__ : int, UpperCamelCase__ : int ):
while b:
__A ,__A : Optional[Any] = b, a % b
return a
def _lowercase ( UpperCamelCase__ : int, UpperCamelCase__ : int ... | 365 | '''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ft... | 107 | 0 |
'''simple docstring'''
from collections import deque
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , A , A , A ) -> None:
A: Any = process_name # process name
A: List[Any] = arrival_time # arrival time of th... | 135 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 107 | 0 |
"""simple docstring"""
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowerCAmelCase__ =re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class A__:
... | 482 | '''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine... | 107 | 0 |
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, RegNetYaagf, RegNetYaagf, RegNetYaaa... | 393 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers imp... | 107 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 278 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_UpperCAmelCase : List[Any] = False
class lowercase_ ... | 107 | 0 |
import colorsys
from PIL import Image # type: ignore
def __lowerCAmelCase ( __snake_case , __snake_case , __snake_case ):
__lowerCAmelCase = x
__lowerCAmelCase = y
for step in range(__snake_case ): # noqa: B007
... | 367 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 107 | 0 |
from collections.abc import Generator
from math import sin
def a_ ( __lowerCAmelCase ):
if len(__snake_case ) != 32:
raise ValueError('''Input must be of length 32''' )
lowerCAmelCase__ = B''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 ... | 615 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 107 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoT... | 362 | '''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] ) -> Dict:
_A = [
... | 107 | 0 |
def _SCREAMING_SNAKE_CASE ( a = 10 ) -> int:
if not isinstance(__snake_case , __snake_case ) or n < 0:
raise ValueError('Invalid input' )
__A : Optional[int] = 10**n
__A : Any = 2_84_33 * (pow(2 , 7_83_04_57 , __snake_c... | 239 | '''simple docstring'''
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ):
from torch.utils.cpp_extension import load
_A = Path(__snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
_A = [
root / filename
for filena... | 107 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_config... | 364 | '''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tran... | 107 | 0 |
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