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'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common impo... | 47 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers... | 90 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
UpperCamelCase__ : Dict = TypeVar("""T""")
class lowerCamelCase_ ( Generic[T] ):
def __init__( self : Dict ,__lowerCamelCase : T ):
'''sim... | 330 |
import re
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> str:
"""simple docstring"""
if len(re.findall('''[ATCG]''', snake_case_ ) ) != len(snake_case_ ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''AT... | 330 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggingface.co/microsoft... | 256 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeli... | 368 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( UpperCAmelCase__ ):
'''s... | 146 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ( ... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_dis... | 290 | 1 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> list:
'''simple docstring'''
_A = len(__lowercase )
for _ in range(__lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
... | 367 |
'''simple docstring'''
import operator
def __lowercase ( __lowercase , __lowercase = False , __lowercase = None ) -> list:
'''simple docstring'''
_A = operator.lt if reverse else operator.gt
_A = solution or []
if not arr:... | 174 | 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, PreTrainedTokenizerBa... | 48 |
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Any:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCamelCase : str = (boundary[1] - boundary[0]) / steps
lowerCamelCase : List[str] = boundary[0]... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise ... | 365 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformer... | 217 | 0 |
'''simple docstring'''
from __future__ import annotations
a_ : Optional[int] = list[list[int]]
# assigning initial values to the grid
a_ : List[str] = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0... | 75 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 | 0 |
'''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 CLIPModel,... | 364 | '''simple docstring'''
from collections import defaultdict
def _UpperCAmelCase ( _UpperCamelCase : int ) -> int:
A_ = 1
A_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(_UpperCamelCase )
if ret ... | 18 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import to... | 272 | '''simple docstring'''
from math import sqrt
def snake_case__ ( _A: int = 1000000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides ... | 272 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
lowercase_ = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false p... | 194 |
import warnings
from .generation import TFGenerationMixin
class A_ ( __UpperCamelCase ):
'''simple docstring'''
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transformers v5. Impor... | 194 | 1 |
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__ ):
"""simple docstring"""
... | 284 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
__A =3_00 # TEMPERATURE (unit = K)
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
if donor_conc <= ... | 163 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowerCamelCase ( _UpperCamelCase : list[float] ) -> Optional[Any]:
'''simple docstring'''
return np.maximum(0 , _UpperCamelCase )
if __name__ == "__main__":
print(np.array... | 354 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
... | 320 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
a_ = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase = self
... | 330 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 330 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torc... | 353 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models ... | 288 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A__ : Optional[Any] = logging.get_logger(__name__)
class __snake_case ( UpperCamelCase_ ):
def __init__( self : Any , *A_ : L... | 103 |
def _a ( SCREAMING_SNAKE_CASE : int = 1000000 ):
"""simple docstring"""
UpperCamelCase__ : Any = set(range(3 , SCREAMING_SNAKE_CASE , 2 ) )
primes.add(2 )
for p in range(3 , SCREAMING_SNAKE_CASE , 2 ):
if p not in primes:
continue
... | 146 | 0 |
'''simple docstring'''
from collections import namedtuple
SCREAMING_SNAKE_CASE_: Tuple =namedtuple('from_to', 'from_ to')
SCREAMING_SNAKE_CASE_: Union[str, Any] ={
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 10_00),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.... | 351 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_: int ={
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuratio... | 106 | 0 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
a : Optional[Any] = logging.getLogger(__name__)
@dataclass
class _a ( __A ):
... | 147 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_UpperCAmelCase : Any = {
"""configuration_trocr""": ["""TROCR... | 174 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class __A( __lowerCamelCase ):
"""simple docstring"""
# `task` is not a ClassVar since we want it to be part of the... | 178 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available()... | 178 | 1 |
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 SCREAMING_SNAKE_CASE__ ( unittest.Tes... | 24 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/r... | 217 | 0 |
def lowerCamelCase__ ( snake_case_ : Tuple , snake_case_ : Union[str, Any] ) -> Optional[int]:
__snake_case = 0
__snake_case = len(__lowerCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_co... | 371 |
import math
def lowerCamelCase__ ( snake_case_ : int ) -> list[int]:
__snake_case = []
__snake_case = 2
__snake_case = int(math.sqrt(snake_case_ ) ) # Size of every segment
__snake_case = [True] * (end + 1)
... | 238 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.ut... | 80 | def _snake_case ( lowerCAmelCase : list ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase )
for i in range(1 , lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : int = collection[i]
SCREAMING_SNAKE_CASE_ : Any = 0
SCRE... | 18 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-bas... | 55 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__snake_case =TypeVar("""T""")
class UpperCAmelCase_ ( Generic[T] ):
def __init__( self : int , UpperCAmelCase__ : T )... | 55 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(__snake_case, (list, tuple) ) or not all(
isinstance(__snake_case, __snake_case ) for number ... | 194 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def lowerCamelCase__ ( __snake_case ) -> bool:
... | 194 | 1 |
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_features_output_indices
SCREAMING_... | 124 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case... | 124 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from d... | 344 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
... | 320 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a ( UpperCAmelCase ):
def __in... | 189 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 189 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: Dict , *_S... | 269 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ... | 269 | 1 |
'''simple docstring'''
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class a__ ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Tuple = """philschmid/bart-large-cnn-samsum"""
_SCREAMING_SNAKE_CASE : str = (
"""... | 357 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data i... | 199 | 0 |
'''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
UpperCamelCase_ = logging.get_logger(__name__)
class a_ ... | 309 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar('''T''')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
lowerc... | 106 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __lowercase ( Tensor... | 127 |
import torch
from diffusers import StableDiffusionPipeline
lowerCAmelCase : Any = """path-to-your-trained-model"""
lowerCAmelCase : int = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
lowerCAmelCase : Union[str, Any... | 127 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 178 |
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_available():
from transform... | 178 | 1 |
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> Dict:
"""simple docstring"""
return math.sqrt(__a ) * math.sqrt(__a ) == num
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> Union[str, Any]:
"""simple docstring"""
UpperCame... | 350 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 60 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
if b == 0:
return (1, 0)
(UpperCamelCase__) : Any = extended_euclid(__lowerCamelCase , a % b )
UpperCam... | 189 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_P... | 238 | 0 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case ( ):
A__ = [randint(-1000 , 1000 ) for i in range(10 )]
A__ = ... | 358 |
"""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.... | 69 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ : List[str] = {"""configuration_plbart""": ["""PLBART_PRETRAINED_C... | 55 |
'''simple docstring'''
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, float... | 55 | 1 |
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
snake_case = str(bin(UpperCamelCase_ ) )[2:] # remove the lea... | 213 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE : str = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def UpperCAmelCase__ (UpperCamelCase_ = "mumbai" ):
... | 213 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : Optional[int] = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],... | 124 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import... | 124 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Tuple ... | 6 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def a_ ( __snake_case : Tuple ) -> str:
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force ,... | 6 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Optional[Any]:
if not head:
return True
# split the list to two parts
UpperCamelCase__ , UpperCamelCase__ : List[str] = head.next, head
while fast and fast.next:
UpperCamelCas... | 189 |
# 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
#
# U... | 189 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza,... | 308 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 308 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "tokenizer"]
__Up... | 91 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor,... | 199 | 0 |
import numpy as np
class _a :
def __init__( self : Tuple, lowerCAmelCase__ : Optional[Any]=None, lowerCAmelCase__ : Optional[Any]=None, lowerCAmelCase__ : List[Any]=None, lowerCAmelCase__ : Optional[Any]=None, lowerCAmelCase__ : List[Any]=None ... | 370 |
"""simple docstring"""
from __future__ import annotations
import math
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
... | 128 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMI... | 127 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_de... | 127 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/markuplm-large': 'https://huggingf... | 355 | class __snake_case :
def __init__( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =n
UpperCAmelCase : Any =[None] * self.n
UpperCAmelCase : Tuple =0 # index of the first e... | 78 | 0 |
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,
... | 20 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 366 |
from __future__ import annotations
__lowerCamelCase = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
... | 10 | 0 |
import argparse
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
__lowerCamelCase : List[str] = F"{file}_{class_name}_{test_name}"
done... | 73 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
import qiskit
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> qiskit.result.counts.Counts:
"""simple docstring"""
_snake_case = qiskit.Aer.get_backend('''aer_simulator''' )
_snake_case = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qu... | 278 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 | 1 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase__( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple=1 ):
if n_shave_prefix_segments >= 0:
r... | 213 | """simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassif... | 213 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A (__lowerCamelCase :int ):
_lowerCAmelCase = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.ve... | 365 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowercase = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7""... | 229 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : Optional[int] = logging.get_logger(__name__)
A : int = {'vocab_file': 'vocab.json', 'm... | 6 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> list:
__a = len(a__ )
__a = [[0] * n for i in range(a__ )]
for i in range(a__ ):
__a = y_points[i]
for i in range(2 , a__ ):
for j in range(a__ , a__ ):
... | 6 | 1 |
from typing import Any
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : Any ):
A = data
A = None
def __repr__(self : List[Any] ):
return F"""Node({self.data})"""
class __UpperCAmelCas... | 355 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
return [ord(UpperCAmelCase ) - 96 for elem in plain]
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return "".join... | 337 | 0 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import requir... | 308 |
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_dimen... | 308 | 1 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __snake_case ( lowerCamelCase__ ):
@require_torch
def UpperCAmelCase__ ( self ) -> List[str]:
... | 361 | import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.t... | 78 | 0 |
from __future__ import annotations
import math
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> List[Any]:
"""simple docstring"""
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(_lowerCAmelCase ) == 0:
raise ValueError(... | 15 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase : Dict =TypeVar("""T""")
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case__ ):
... | 128 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str , snake_case : bool = False )-> str:
if not isinstance(snake_case , snake_case ):
_lowerCamelCase = f'Expected string as input, found {type(snake_case )}'
... | 362 |
"""simple docstring"""
import numpy as np
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray )-> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray )-> np.ndarray:
return vector * sigmoid(snake_case )
if __na... | 80 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import... | 47 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case_ = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5On... | 78 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffuser... | 361 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod() | 135 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConf... | 61 |
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_verbosity_info()
__A ... | 10 | 0 |
"""simple docstring"""
def _a ( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__SCREAMING_SNAKE_CASE : Optional[Any] = generate_large_matrix()
__SCREAMING_SNAKE_CASE : Optional[Any] = (
[[4,... | 233 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class __A :
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : int ) ->None:
"""simple docstring"""
snake... | 233 | 1 |
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
_A = logging.get_logger(__name__)
_A = {
'''google/mobilenet_v1_1.0_224''': '''https://hu... | 278 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 384
... | 278 | 1 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallb... | 106 | '''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: ... | 106 | 1 |
"""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_manager import... | 292 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__nam... | 292 | 1 |
from __future__ import annotations
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(UpperCamelCase_ ) / len(UpperCamelCase_ )
if __name__ == "__main__":
impor... | 213 |
import argparse
import copy
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = {}
with open(UpperCamelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 213 | 1 |
def _lowercase ( lowercase__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(UpperCAmelCase__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 275 | '''simple docstring'''
import math
import unittest
def lowerCamelCase ( UpperCAmelCase__ : int ) -> bool:
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < ... | 239 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=a_ ):
"""simple docstring"""
A : Dict = ["onnx"]
def __init__( self : List[str] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_... | 354 | '''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, ... | 345 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
"""facebook/dat... | 14 |
"""simple docstring"""
class A_ :
"""simple docstring"""
def __init__( self :List[str] , lowercase_ :int , lowercase_ :Optional[int]=None , lowercase_ :List[str]=None ) -> str:
UpperCAmelCase = data
UpperCAm... | 78 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
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... | 160 |
'''simple docstring'''
import os
import sys
import unittest
lowercase : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, cr... | 160 | 1 |
A_ :Union[str, Any] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
A_ :Option... | 71 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Toke... | 80 | 0 |
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()
_lowercase: List[Any] = [
"word_embeddings_layernorm.weight... | 364 |
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
_lowercase: List[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 71 | 0 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( A_ : Optional[int] ) -> str:
A = Part... | 74 | """simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import... | 135 | 0 |
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 import ConfigTes... | 143 | # 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... | 143 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 233 |
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 Generatio... | 233 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False )-> Optional[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : ... | 367 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import g... | 349 | 0 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logg... | 106 |
"""simple docstring"""
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
__UpperCamelCase : int ... | 106 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ):
UpperCAmelCase_ = ArgumentParser(
description=(
"PyTorch TPU... | 350 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
... | 241 | 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
fr... | 213 | """simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from ... | 213 | 1 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ... | 194 |
import warnings
from .generation import TFGenerationMixin
class A_ ( __UpperCamelCase ):
'''simple docstring'''
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transformers v5. Impor... | 194 | 1 |
"""simple docstring"""
import numpy as np
def __a ( __lowerCamelCase ):
return 1 / (1 + np.exp(-vector ))
def __a ( __lowerCamelCase ):
return vector * sigmoid(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impor... | 279 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a_ : Tuple = datasets.load_iris()
a_ : List[str] = np.array(data["""data"""])
a_ : Union[str, Any] = np.array(... | 6 |
'''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_ : Any = logging.get_logger(__name__)
a_ : Option... | 6 | 1 |
"""simple docstring"""
import random
def __A ( a_ :int , a_ :float , a_ :bool = False) -> dict:
__a : dict = {i: [] for i in range(a_)}
# if probability is greater or equal than 1, then generate a complete graph
if probabili... | 160 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __lowercase (... | 160 | 1 |
'''simple docstring'''
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
__snake_case : Union[str, Any] =... | 357 | '''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__snake_case : Optional[Any] = logging.get_logger(__name__)
__snake_case : Tup... | 18 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from .... | 44 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ):
"""simple docstring"""
A = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __SCREAMING_SNAKE_CASE ... | 368 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_... | 57 | 0 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = os.path.join(args.tf_model_dir ,"""paramet... | 289 |
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
_UpperCAmelCase : int = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *A : int , **A : Optional[int]... | 142 | 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 352 | import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
... | 50 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/r... | 247 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
if length <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(lowercase_ )]
if __name__ == "__ma... | 247 | 1 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 2 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docs... | 2 | 1 |
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_allow_in_graph
class ... | 9 |
"""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`""")
| 241 | 0 |
from math import sqrt
def lowerCamelCase__ (__lowerCamelCase ):
assert isinstance(_lowerCamelCase, _lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
_SCREAMING_SNAKE_CASE : Optional[Any] = True
# 0 and 1 are none pr... | 363 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__lower... | 325 | 0 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase:
def __init__( self , __a=None) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = data
_UpperCamelCase = None
... | 194 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def lowerCamelCase__ ( __snake_case ) -> bool:
... | 194 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : Dict = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 25 |
'''simple docstring'''
def lowercase (_A = 1_0_0_0_0_0_0 ):
"""simple docstring"""
_lowerCAmelCase : Any = set(range(3 , _A , 2 ) )
primes.add(2 )
for p in range(3 , _A , 2 ):
... | 25 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
A : Union[str, Any] = datasets.load_iris()
A : int = np.array(data['data'])
A : Optional[Any] = np.array(data['target'])
A : int = data['target_names']
A ... | 6 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTes... | 6 | 1 |
def _A ( __magic_name__ ):
lowercase__ = len(__A )
while cur > 1:
# Find the maximum number in arr
lowercase__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowercase__ = arr[mi::-1] + arr[mi + 1 : len(__A )]
# Reverse whole list
... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDC... | 201 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a ={
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Blip2VisionConfig""",... | 73 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenizati... | 18 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_f... | 371 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 0 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[int] ) -> list[int]:
"""simple docstring"""
UpperCamelCase :List[str] = len(__magic_name__ )
for i in range(__magic_name__ ):
for j in range(i + 1 , __magic_name__ ):
if numbers[j] < numbers[i... | 38 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring''... | 57 | 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
_lowerCAmelCase = logging.getLogger(__name__)
if is_torch_t... | 357 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : int = ['''sentencepiece''']
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ... | 184 | 0 |
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