code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__lowerCAmelCase : Any =Mapping[str, np.ndarray]
__lowerCAmelCase : ... | 197 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
... | 56 | 0 |
UpperCamelCase = 256
# Modulus to hash a string
UpperCamelCase = 100_0003
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = len(lowerCAmelCase_ )
lowerCA... | 221 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase = '__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase = 'Dummy User'
UpperCamelCase = 'hf_hZEmnoOEY... | 221 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
fr... | 45 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a_ :Optional[Any] = logging.getLogger(__n... | 277 | 0 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
f... | 361 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple do... | 273 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
lowerCAmelCase__ : Union[str, Any] =False
try:
lowerCAm... | 257 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase_ ( ):
lowerCamelCase_ , lowerCamelCase_ = 9, 1_4 # noqa: F841
lowerCamelCase_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7... | 19 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the roo... | 224 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
if numbers[j] < numbers[i]:
lowercase__ , lowercase__ =... | 224 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase = logging.get_logger(__name__)
class _A ( ... | 40 |
'''simple docstring'''
import argparse
import os
import re
__a = "src/transformers"
# Pattern that looks at the indentation in a line.
__a = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that ... | 35 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ : int = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/ma... | 197 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ : Dict = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_ava... | 197 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compar... | 110 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class __magic_name__ ( __UpperCAmelCase ):
def __init__( self : List[Any] , ... | 172 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase = '''\
@misc{chen2021evaluating,
title={Evalu... | 172 | 1 |
'''simple docstring'''
def snake_case__ ( _A: int = 100 ) -> int:
'''simple docstring'''
lowerCAmelCase = n * (n + 1) * (2 * n + 1) / 6
lowerCAmelCase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f'{soluti... | 272 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__( unittest.TestCase ):
... | 272 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A ( __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transforme... | 209 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """Speech2TextFeatureExtractor"""
lowerCAmelCase_ = """Speech2TextTokenizer... | 209 | 1 |
"""simple docstring"""
class _lowerCamelCase :
def __init__( self : Union[str, Any] , UpperCamelCase : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = val
lowerCAmelCase__ : Union[str, Any] = No... | 212 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_term... | 212 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__lowerCamelCase = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Mat... | 221 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class U... | 221 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class UpperCAmelCase :
_lowercase: torch.Tensor # [batch_size x 3]
_lowercase: torch.Tensor # [batch_size x 3]
_low... | 220 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch... | 220 | 1 |
'''simple docstring'''
import requests
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> None:
__lowerCamelCase = {'''Content-Type''': '''application/json'''}
__lowerCamelCase = requests.post(UpperCamelCase__ , json={'''text''': message_body} , headers=UpperCa... | 67 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
import math
def lowerCamelCase__ (__lowerCamelCase ):
assert isinstance(__lowerCamelCase, __lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or n... | 355 |
import numpy as np
import datasets
UpperCamelCase__ ='\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P.... | 325 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.tes... | 311 |
'''simple docstring'''
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 311 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( snake_case_ = 2_0 ) -> int:
"""simple docstring"""
a = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
a = n // 2
return int(factorial(snake_... | 330 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> str | Literal[False]:
"""simple docstring"""
a = list(snake_case_ )
a = lis... | 330 | 1 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, ... | 197 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=lowerCAmelCase ):
snake_case__ : str = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
... | 197 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A__ , unittest.TestCase ):
"""s... | 218 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 1
while repunit:
SCREAMING_SNAKE_CASE__ = (10 * repunit + 1) % divisor
... | 218 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowercase )
class UpperCamelCase ( lowercase ):
UpperCAmelCase ... | 172 | """simple docstring"""
import os
import pytest
from attr import dataclass
_a : Optional[int]= "us-east-1" # defaults region
@dataclass
class UpperCamelCase :
UpperCAmelCase : str
UpperCAmelCase : Any = """arn:aws:iam::558105141721:role/sagemaker_exe... | 172 | 1 |
"""simple docstring"""
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
lowerCAmelCase_ : Optional[int] =... | 248 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return np.maximum(0 , lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 248 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 243 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_UpperCamelCase: Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable(... | 53 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
_UpperCamelCase: Any = 'naver-clova-ix/donut-base'
class a__ ( unittest.TestCase ):
def lowercase ( self : Optional[Any] ) -> Tuple:
... | 53 | 1 |
def __lowerCAmelCase ( a__ ) -> int:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__a = len(a__ )
__a = max(a__ )
__a = min(a__ )
# create the counting ... | 6 |
from collections import defaultdict
from math import gcd
def _a ( UpperCamelCase_ : int = 1_500_000 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = defaultdict(UpperCamelCase_ )
lowerCAmelCase__ = 2
while 2 * euclid_m ... | 340 | 0 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase = False ):
"""simple docstring"""
if r... | 184 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipelin... | 184 | 1 |
import random
from typing import Any
def UpperCamelCase_( lowerCamelCase_ ) -> list[Any]:
for _ in range(len(lowerCamelCase_ ) ):
_lowercase : Optional[int] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
_lowercase : str = random... | 21 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transfo... | 244 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = TaConf... | 354 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 75 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.u... | 309 |
import doctest
from collections import deque
import numpy as np
class snake_case__ :
def __init__( self ) -> None:
__magic_name__ : Any = [2, 1, 2, -1]
__magic_name__ : Tuple = [1, 2, 3, 4]
def __magic_name__ ( self ) ... | 342 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase__ : Dict = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.js... | 358 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase__ : Dict = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.js... | 330 | 0 |
from math import factorial
def a__ ( _UpperCamelCase : int = 20 ):
__lowerCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowerCamelCase = n // 2
return int(factorial(_UpperCamelCase ) / (facto... | 330 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 330 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : List[str] = logging.get_logger(__name... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 120 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowercase ( A_ )-> List[str]:
'''simple docstring'''
... | 40 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : list ) -> list:
def merge(_UpperCamelCase : list, _UpperCamelCase : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] ... | 18 | '''simple docstring'''
from statistics import mean, stdev
def _UpperCAmelCase ( _UpperCamelCase : list, _UpperCamelCase : int = 3 ) -> list:
A_ = min(_UpperCamelCase )
A_ = max(_UpperCamelCase )
# normalize data
... | 18 | 1 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case : List[str] = get... | 292 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def A ( lowercase ) -> str:
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natu... | 222 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase ) -> bool:
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
UpperCAmelCase__ : Union[str, Any] = str(lowerCAmelCase )
UpperCAmelCase_... | 166 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 166 | 1 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def _low... | 53 |
'''simple docstring'''
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Optional[int] , __A : list[list[int]] ):
__UpperCamelCase = TypeError(
'Matrices must be formed from a list of ... | 53 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCamelCase ( self ) -> Any:
... | 361 |
from math import sqrt
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00_00_00 ):
__UpperCamelCase : int = 0
__UpperCamelCase : int = 0
__UpperCamelCase : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_si... | 171 | 0 |
# Copyright 2023 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 require... | 184 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__)
class _lowercase ( lowercase__):
"""simple docstring"""
A__ ... | 184 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : List[str] = (UnCLIPScheduler,)
def __lowerCamelCase ( self : List[str] , **A... | 265 |
from math import factorial
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , A : Dict , A : Any ) ->Optional[Any]:
lowerCamelCase__ : Tuple = real
if isinstance(A , A ):
... | 265 | 1 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLI... | 46 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints,... | 75 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase__ :
'''simple docstring'''
def __init__(self ,__lowerCamelCase ) -> None:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = value
lower... | 360 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase__ ( lowerCamelCase_ : ndarray):
'''simple docstring'''
return np.dot(lowerCamelCase_ ,lowerCamelCase_)
class lowerCamelCase__ :
'''simple docstri... | 94 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCamelCase ( _A ):
"""simple docstring"""
__magic_name__ : Tuple = args.pruning_method
__magic_name__ :... | 342 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lo... | 330 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSeq... | 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : Any ): # noqa: E741
'''simple docstring'''
lowerCAmelCase : Optional[int] = len(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Any = 0
lowerCAmelCase : Optional[Any] ... | 108 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__A : Any ... | 120 | 0 |
'''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
A_ : Dict = logging.get_logger(__name__)
A_ : List[st... | 358 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
... | 349 | 0 |
def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : str = " " ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = []
SCREAMING_SNAKE_CASE_ : List[str] = 0
for index, char in enumerate(lowerCAmelCase ):
if char == separator:
... | 18 | from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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, ra... | 18 | 1 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a : Any = TypeVar("""T""")
class UpperCamelCase_ ( Generic[T] ):
lowercase = 42 # Cache store of keys
lowercase = 42 ... | 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 0 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if not sentence:
return ""
__lowercase =dict(zip(_lowerCAmelCase , _lowerCAmelCase ) )
return lower_... | 166 |
'''simple docstring'''
from typing import List
import numpy as np
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ={key: len(_lowerCAmelCase ) for key, value in gen_kwargs.items() if isinstance(_lowerCAmelCase , _lowerCAmelCase )}
... | 166 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart impor... | 367 |
import math
def lowerCamelCase__ ( a__ : float , a__ : float ) -> float:
if (
not isinstance(a__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float... | 261 | 0 |
from collections import deque
from .hash_table import HashTable
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : int , *_UpperCamelCase : int , **_UpperCamelCase : Tuple ) ->Tuple:
super().__init__(... | 8 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_A = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_... | 171 | 0 |
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowercase_ : Optional[Any] ,lowercase_ : List[str] ):
lowerCAmelCase__ : Optional[Any... | 367 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sq... | 74 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.trai... | 237 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controln... | 237 | 1 |
'''simple docstring'''
from math import isqrt, loga
def _lowerCAmelCase ( lowercase ) -> list[int]:
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercase... | 362 |
'''simple docstring'''
import random
def _lowerCAmelCase ( lowercase ) -> bool:
__lowerCAmelCase = num - 1
__lowerCAmelCase = 0
while s % 2 == 0:
__lowerCAmelCase = s // 2
t += 1
for _ in range(5 ):
__lowerCAmelCase ... | 46 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.ut... | 8 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 94 | 0 |
import math
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :float , SCREAMING_SNAKE_CASE :float ) -> float:
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid ... | 232 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers... | 232 | 1 |
from PIL import Image
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = image.size
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : Optional[Any] = image.lo... | 9 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase (_SCRE... | 27 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : Callable[[float], float] , _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
'''simple docstring'''
UpperCamelCase__ =... | 356 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_... | 31 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_... | 3 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ : Optional[Any] = logging.getLogger(__name__)
class UpperCAmelCase__ :
de... | 349 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( _lowerCamelCase : list , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
A__ = []
A__, A__ = input_list[low:mid], input_list[mid : high + 1]
w... | 123 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils... | 123 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowerCAmelCase__ ( a)... | 11 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if i... | 338 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {
"huggingface/informer-tourism-monthly": (
"ht... | 370 |
def UpperCamelCase ( _A : int , _A : int )-> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( )-> None:
"""simple docstring"""
assert and_gate(0 , 0 ) ... | 198 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : list ):
if any(not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(SCREAMING_SNAKE_CASE_ ... | 92 | """simple docstring"""
from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnP... | 261 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, ... | 313 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = None
lowercase__ = False
lowercase__ = False
lowercase__ ... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opti... | 82 |
"""simple docstring"""
import argparse
import struct
import unittest
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Tuple ,A_ : bytes ) -> None:
A = data
# Initialize hash values
A = [
... | 74 | 0 |
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, ImagePipelineOutpu... | 362 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class _UpperCAmel... | 207 | 0 |
"""simple docstring"""
__UpperCamelCase = '''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_di... | 113 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWaterm... | 46 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_a = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/mode... | 365 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _A ( ) -> Dict:
'''simple docstring'''
__lowercase = {
"repo_name": ["test_repo1", "test_repo2", "test_... | 144 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import Genera... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=lowerCAmelCase ):
snake_case__ : List[Any] = ['transformers', 'torch', 'note_seq']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ... | 32 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase : List[Any] ={
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 32 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTester... | 291 | '''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_s... | 31 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : int ) -> int:
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
... | 350 |
'''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.or... | 3 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[int] = {
"configuration_distilbert": [
"DISTILBERT_PRETRAI... | 123 |
from __future__ import annotations
_snake_case : Any = "Muhammad Umer Farooq"
_snake_case : Optional[int] = "MIT"
_snake_case : Union[str, Any] = "1.0.0"
_snake_case : Optional[Any] = "Muhammad Umer Farooq"
_snake_case : List[Any] = "contact@muhammadu... | 123 | 1 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __SCREAMING_SNAKE_CASE ( __UpperCA... | 357 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""h... | 336 | 0 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase_ ( a__ ):
'''simple docstring'''
a__ ... | 88 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[int] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[Any] = 0
while number > 0:
lowercase_... | 198 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transf... | 371 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a = True
except (ImportError, ModuleNotFoundError):
_a = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _A ( UpperCamelCase_ ... | 144 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter... | 201 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 201 | 1 |
def UpperCAmelCase_ ( __lowerCamelCase : int ,__lowerCamelCase : int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase_ :Dict = str(bin(A__ ) )[2:] # remove the leading "0b"
lowercase_ :Unio... | 357 |
'''simple docstring'''
# Copyright 2023 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/license... | 147 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCAmelCase_ ( unittest.TestCase ):
def __magic_name__ ( self : Dict ) -> Tuple:
'''simple docs... | 247 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from .... | 207 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def UpperCamelCase_ ( A__ : Sequence[float] , A__ : int , A__ : ... | 362 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[str] = logging.get_logg... | 89 | 0 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers... | 150 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ : Tuple = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_... | 144 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.... | 306 |
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_availa... | 306 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class SCREAMING_SNAKE_CASE__ ( pl.LightningModule ):
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ ... | 32 |
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... | 32 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ... | 246 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_mobilebert": [
"MOBI... | 246 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, Im... | 0 |
'''simple docstring'''
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
A : Any = None
A : Optio... | 3 | 0 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 363 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_t... | 112 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_... | 322 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils imp... | 300 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __UpperCamelCase ( a__ ):
lowerCamelCase : str ="""Speech2TextFeatureExtractor"""
lowerCamelCase : str ="""Spe... | 79 |
"""simple docstring"""
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
a : Optional[Any] = "https://downloadgram.net/wp-json/wppress/video-downloader... | 79 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
... | 249 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : List[Any] , lowerCamelCase__ : str ) -> str:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
... | 144 | 0 |
"""simple docstring"""
from math import factorial
def _A ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
... | 215 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ : Union[str,... | 215 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
... | 26 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, ra... | 147 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
),
}
clas... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
a : List[Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"... | 311 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> np.ndarray:
_a : Un... | 89 | 0 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING... | 74 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCamelCase : Union[str, Any] = namedtuple(
... | 74 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ) -> Tuple:
"""simple docstring"""
_S... | 306 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class lowercase_ :
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=0.2 , __UpperCamelCase=0.2 ):... | 365 |
from __future__ import annotations
def lowerCamelCase__ ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in... | 261 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self :Optional[Any] , *snake_case :str , **snake_case :Optional[int] ):
'''simple docstr... | 300 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCAmelCase : int = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author=... | 300 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 368 |
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def __lowerCAme... | 182 | 0 |
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