code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__a : Optional[Any] = logging.get_logger... | 397 |
from math import factorial
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> float:
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes... | 397 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case :Dict = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.mod... | 60 |
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,
EulerDiscreteS... | 60 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( ... | 33 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase = logging.getLogger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''maske... | 573 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, ... | 706 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processing_mctct": ["MC... | 202 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCAmelCase__ = logging.getLogger(__name__)
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : ... | 83 |
"""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_common import Mo... | 83 | 1 |
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_torc... | 714 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegas... | 166 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jn... | 15 |
"""simple docstring"""
def __lowercase ( snake_case_ : list ) ->float:
'''simple docstring'''
__A : Tuple = 0
while len(snake_case_ ) > 1:
__A : List[Any] = 0
# Consider two files with minimum cost to be merged
for _ in ran... | 177 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A : str = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outpu... | 720 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | 141 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def A ( lowercase__ : list , lowercase__ : list , lowercase__ : list , lowercase__ : list , lowercase__ : list ) -> f... | 45 |
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def A__ ( A : Dict , A : Any , A : Tuple , A : Tuple):
'''simple docstring'''
UpperCamelCase : Tuple = {
"en": "Mac... | 173 | 0 |
import sys
def A ( __UpperCAmelCase ) -> str:
'''simple docstring'''
UpperCAmelCase_ = len(__UpperCAmelCase )
UpperCAmelCase_ = [[0 for x in range(__UpperCAmelCase )] for x in range(__UpperCAmelCase )]
UpperCAmelCase_ = [[0 for x i... | 561 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs''': 1}, [ra... | 561 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytessera... | 25 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 333 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase : Union[str, Any] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormer... | 710 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from... | 341 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
def decorator(_lowerCamelCase ):
__snake_case : str = getattr... | 26 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 662 | 0 |
import requests
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> None:
UpperCAmelCase_ = {"Content-Type": "application/json"}
UpperCAmelCase_ = requests.post(__SCREAMING_SNAKE_CASE , json={"text": message_body} , headers=__SCREAMING_SNAKE... | 23 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requi... | 23 | 1 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.point(lowerCamel... | 112 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 112 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 709 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a : List[str] = logging.get_logger(__name__)... | 672 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
UpperCamelCase__ =datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCAmelCase__( dataset... | 249 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 249 | 1 |
from __future__ import annotations
def a ( A__ , A__ = None ) -> list[list[str]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = word_bank or []
# create a table
SCREAMING_SNAKE_CASE__ : int = len(A__ ) + 1
SCRE... | 716 |
def a ( A__ , A__ , A__ ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if... | 250 | 0 |
__a = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def _UpperCamelCase ( lowerCAmelCase_ , ... | 377 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->Any:
UpperCAmelCase = 0
UpperCAmelCase = len(lowerCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_invers... | 377 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
_lowercase: int = [0] * len(_UpperCamelCase )
_lowercase: int = []
_lowercase: Union[str, Any] = []
_lowercase: Union[str, Any] = 0
for values in... | 716 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A__ : List[Any] = datase... | 272 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availa... | 502 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMi... | 502 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase_ : List[Any] = logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Dict = """masked_bert"""
def __init__( self : List[str] , ... | 711 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
UpperCamelCase :List[Any] = sum(__magic_name__ ) / len(__magic_n... | 590 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_availab... | 656 | '''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_lowerCAmelCase :Any = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_lowerCAmelCase :Any ... | 251 | 0 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 713 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 264 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils ... | 43 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 500 | 0 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ : List[str] = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": op... | 719 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 352 | 0 |
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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> int:
'''simple docstr... | 481 |
from math import factorial
_a = {str(digit): factorial(digit) for digit in range(10)}
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
if not isinstance(__snake_case ,__snake_case ):
raise TypeError('''Parameter number must be int''' )
if number <... | 481 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCamelCase ( _A , _A=7 ):
lowerCAmelCase_ = None
if token is not None:
lowerCAmelCase_ = {'''Accept''': '''application/vn... | 325 |
# 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 Version
_A = ... | 325 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a = logging.get_logger(__name__)
__a = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-l... | 97 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tenso... | 653 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 475 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __magic_n... | 475 | 1 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCA... | 660 | 1 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from acce... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if num < 0:
return False
A_ = num
A_ = 0
while num > 0:
A_ = rev_num * 10 + (num % 10)
num //= 10
return nu... | 667 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowerCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case : List[str] = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<a... | 178 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : List[str] = (KDPMaDiscreteScheduler,)
__Upp... | 178 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase_ = 10
def _UpperCAmelCase ( A , A , A , ... | 510 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-... | 510 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/l... | 142 |
"""simple docstring"""
__lowercase : Union[str, Any] = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Z... | 142 | 1 |
'''simple docstring'''
import argparse
import os
import re
a_ = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_ = re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+... | 701 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optional... | 115 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class UpperCAmelCase_ ( __A , __A ):
"""simple docstring"""
... | 94 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
fro... | 236 | 0 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transforme... | 313 |
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = int(np.ceil((x_end - xa) / h ) )
lowerCamelCase_ = np.zeros((n + 1,) )
lower... | 313 | 1 |
"""simple docstring"""
import numpy as np
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :int = int(np.ceil((x_end - xa) / h ) )... | 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer... | 653 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ... | 708 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
trans... | 692 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _lowerCAmelCase ( snake_case_ ):
pass
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ ) -> int:
'''simple docstring'''
snake_ca... | 178 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""microsoft/focalnet... | 453 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
snake_case_ : int = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Met... | 191 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_re... | 191 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowercase : List[Any] =Lock()
def a__ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ... | 54 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 298 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from trans... | 720 |
import csv
import tweepy
# Twitter API credentials
lowerCamelCase :Optional[int] = ''
lowerCamelCase :Tuple = ''
lowerCamelCase :Tuple = ''
lowerCamelCase :Optional[Any] = ''
def __snake_case ( _UpperCamelCase ) -> None:
# authorize twitter, initialize... | 346 | 0 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
fro... | 319 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if height >= 1:
move_tower(height - 1 , lowercase_ , lowercase_ , lowercase_ )
move_disk(lowercase_ , lowercase_ )
move_tower(height - 1 ... | 720 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( u... | 695 | 0 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecode... | 22 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_g... | 539 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase (datasets.BeamBasedBuilder ):
"""simple docstring"""
def A_ ... | 718 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> str | Literal[False]:
'''simple docstring'''
SCREAM... | 157 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_UpperCAmelCase = input('Enter image url: ').strip()
print(f'''Downloading image from {url} ...''')
_UpperCAmelCase = BeautifulSoup(requests.get(url).content, 'html.parser')... | 504 |
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 transformers import (
Eff... | 504 | 1 |
_UpperCAmelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
_UpperCAmelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
def UpperCamelCase ( lowercase... | 702 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''google/pix2struct-textcaps-base''': (
'''ht... | 145 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
"""simple docstring"""
def __init__( self , *lowerCAmelCase__ ... | 155 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
snake_case : str = {'v... | 566 | 0 |
"""simple docstring"""
import re
import subprocess
import sys
__SCREAMING_SNAKE_CASE : Optional[Any] = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__SCREAMING_SNAKE_CASE : Tuple = subprocess.check_output(F"""git diff --name-only {fo... | 700 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE : List[str] = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE : Optional[Any] = numpy.array([0.5, ... | 623 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 1 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resi... | 194 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCAmelCase = 'examples/'
__UpperCAmelCase = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version... | 194 | 1 |
from heapq import heappop, heappush
import numpy as np
def a ( A__ , A__ , A__ , A__ , ) -> tuple[float | int, list[tuple[int, int]]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] =... | 35 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
fro... | 259 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_a... | 702 |
import fire
from utils import calculate_rouge, save_json
def _UpperCAmelCase ( UpperCamelCase: Any , UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any]=None , **UpperCamelCase: Optional[int] ):
"""simple docstring"""
__lowerCAmelCase = ... | 376 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : Any = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SqueezeBertConfig''... | 53 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'''kakaobrain... | 149 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __snake_case ( UpperCamelCase__ ) -> Any:
"""simple docstring"""
if not is_accelerate_available():
return method
... | 718 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowe... | 91 | 0 |
'''simple docstring'''
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 : ... | 107 |
"""simple docstring"""
def lowercase__(A ) ->bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 218 | 0 |
import doctest
from collections import deque
import numpy as np
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ) -> None:
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3, 4]
def ... | 472 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = field(default="question-answering-extractive" , m... | 472 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowercase_ ... | 11 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = k_size // 2
__SCRE... | 131 |
import math
import random
def a__ ( snake_case , snake_case = False ):
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase_ = 0.02
def a__ ( snake_case , snake_case ... | 131 | 1 |
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_accelerate_available,
is_acce... | 315 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 315 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : Optional[int] = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json... | 43 | '''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase ( lowerCamelCase , unittest.... | 43 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.i... | 172 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_... | 172 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrain... | 715 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ : List[Any] = logging.get_logger(__name__)
lowercase_ : List[Any] = {
'facebook/xlm-roberta-xl': 'ht... | 107 | 0 |
'''simple docstring'''
import os
import numpy
import onnx
def UpperCamelCase ( a , a ) -> List[Any]:
'''simple docstring'''
__magic_name__ = a.name
__magic_name__ = b.name
__magic_name__ = ''''''
__magic_name__ = '''''... | 432 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Optional[int] = """ClapFeatureExtractor"""
__SCREAMING_SNAKE_CASE :List[Any] = ("""Robe... | 432 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__lowerCamelCase : int = False
class lowerCamelCase ( unittest.TestCase ... | 501 |
import sys
import turtle
def lowercase__ ( __A: tuple[float, float] ,__A: tuple[float, float] ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase__ ( __A: tuple[float, float] ,__A: tuple[float, float] ,__A: tuple[... | 501 | 1 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstri... | 104 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVeca... | 372 | 0 |
from __future__ import annotations
from cmath import sqrt
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a == 0:
raise ValueError("""Coeffici... | 390 | lowercase_ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_s... | 390 | 1 |
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, HfArgumentPars... | 637 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/p... | 701 |
"""simple docstring"""
from math import sqrt
def __lowercase ( lowerCamelCase_ : int ):
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , int(sqrt(lowerCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCamelCase_ ):
total += i + n // i
... | 112 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_av... | 107 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( A__ ):
__A : Dict ... | 32 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowerCamelCase = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
'models/... | 707 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerC... | 303 | 0 |
def lowerCAmelCase_ ( __a ) -> bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 59 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''... | 59 | 1 |
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase ):
snake_case__ = word.split()
def justify(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
snake_case__ = max_width - width
snake_case__ = len(A_ )
... | 710 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTra... | 530 | 0 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __snake_case (__SCREAMING_SNAKE_CASE ):
__a = """M-CLIP"""
def __init__( self: Optional[int] , A_: Dict=10_24 , ... | 281 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class A__ ( nn.Module):
def __init__( self , __magic_name__ = 1_6 , __magic_name__ = 8_8 , __magic_name__ = None , __magic_name__ = 1 ... | 681 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=_a ):
"""simple docstring"""
__lowerCAmelCase : Optional[Any] =['''sentencepiece''']
def __init__( self :int, *snake_case :Union[str, Any], **snake_case ... | 557 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE_ ( nn.Module ):
"""simple docstring"""
__lowerCAmelCase : int
... | 557 | 1 |
'''simple docstring'''
import math
import random
def A (__lowerCamelCase :float , __lowerCamelCase :bool = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_lowercase = 0.02
def A (__lowerCamelCase :int ... | 5 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase ( unittest.TestCase ):
def a ( self ):
snake_case_ = 10
def a ... | 362 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : List[Any] = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
... | 718 | # Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowercase_ : str = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class _lowerCamelCase :
... | 107 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {}
try:
if not is_sentencepiec... | 26 |
"""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,
... | 560 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 306 |
_lowerCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_lowerCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowercase ( _a ,_a ,_a ) -> list[int]:
UpperCAmelCase_: Tuple = True
UpperCAmelCase_: Optional[int] =... | 306 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _a ( _snake_case : ArgumentParser ):
"""simple docstring"""
... | 9 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.... | 674 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils impor... | 712 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verb... | 481 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Union[str, Any] = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 429 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __snake_case :
lowerCAmelCase__ = 42
lowerCAmelCase__ = None
... | 429 | 1 |
'''simple docstring'''
import argparse
import datetime
def UpperCamelCase_ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case_ : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
... | 710 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ =... | 92 | 0 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
... | 82 |
from string import ascii_uppercase
__A = {str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
if isinstance(UpperCamelCase__ ... | 469 | 0 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataColl... | 267 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 267 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _a ( SCREAMING_SNAKE_CASE ):
"""simpl... | 43 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 554 | 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"""):
UpperCAmelCase_ : Optional[int] = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bi... | 440 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutp... | 440 | 1 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_u... | 457 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase : List[str] = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase : List[str] = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one... | 372 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 653 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase... | 653 | 1 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 20 |
def _lowercase( __a : list[int] ):
a__ =len(__a )
for i in range(__a ):
for j in range(i + 1 , __a ):
if numbers[j] < numbers[i]:
a__ , a__ =numbers[j], numbers[i]
return numbers
... | 20 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtrac... | 720 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_fu... | 317 | 0 |
'''simple docstring'''
class __A :
def __init__( self , UpperCamelCase_ ):
__UpperCAmelCase : Any = val
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : str = None
... | 168 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
re... | 595 | 0 |
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( A : int = 1_0_0_0_0_0_0 ) -> int:
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : Optional[int] = 4_2
while num_cuboids <=... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : Optional[int] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfi... | 216 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenize... | 79 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common im... | 79 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
... | 714 |
'''simple docstring'''
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 UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Optional[Any] , ... | 145 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def lowercase ( __A : List[Any] , __A : Any , __A : Tuple , __A : Optional[Any] = 100 , ) -> float:
'''simple docstring'''
snake_case : Dict = x_start
... | 36 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
... | 596 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
... | 161 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ = logging.get_logge... | 161 | 1 |
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