code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import to... | 513 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 513 | 1 |
"""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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformer... | 579 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE_ = logging.get_logger(__na... | 579 | 1 |
"""simple docstring"""
import warnings
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
_lowerCAmelCase : List[str] = logging.get_logger(_... | 289 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE__ : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase : str = 0
for ch in input_str:
_UpperCAmelCase : Optional[int] = ord(SCREAMING_SNAKE_... | 289 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultist... | 711 | """simple docstring"""
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
stooge(__UpperCAmelCase ,0 ,len(__UpperCAmelCase ) - 1 )
return arr
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"... | 283 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def UpperCamelCase ( lowercase_ ) -> Any:
'''simple... | 12 |
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase__ : str = mf_knapsack(i - 1 , lowercase_ , ... | 12 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class snake_case :
'''simple docstring'''
_A : ... | 374 |
"""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 Ba... | 374 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
lo... | 422 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, s... | 71 | 0 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class __UpperCAmelCase ( _UpperCAmelCase ):
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ):
super().__init__(*lowercase__ , **lowercase__ )
... | 704 | '''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case_ ... | 606 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 63 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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... | 600 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def A ( a_ ,a_ ,a_ = False ) -> list[float]:
if radian_mode:
return [magnitude * cos(a_ ), magnitude * sin(a_ )]
r... | 706 |
from __future__ import annotations
A_ :Union[str, Any] = '''#'''
class __A :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
__UpperCamelCase : dict ={}
def __lowercas... | 154 | 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 lowerCAmelCase__ ( _lowerCamelCa... | 106 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeni... | 106 | 1 |
def _UpperCAmelCase ( A = 1000 ):
'''simple docstring'''
return sum(e for e in range(3 , A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 713 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
UpperCamelCase_ = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import... | 510 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils imp... | 309 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from acce... | 553 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase ... | 251 |
"""simple docstring"""
__UpperCAmelCase = {
'''meter''': '''m''',
'''kilometer''': '''km''',
'''megametre''': '''Mm''',
'''gigametre''': '''Gm''',
'''terametre''': '''Tm''',
'''petametre''': '''Pm''',
'''exametre''': '''Em''',
'''zettametre''': '''Zm''',
'''yottametr... | 251 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 81 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : Any = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
... | 81 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase : int = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_M... | 706 | """simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 645 | 0 |
from ...processing_utils import ProcessorMixin
class A ( __UpperCAmelCase ):
__snake_case = 'WhisperFeatureExtractor'
__snake_case = 'WhisperTokenizer'
def __init__( self, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
super()... | 431 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureEx... | 431 | 1 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_A = 'scheduler_config.json'
class lowe... | 538 | """simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_A = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase = "mumbai" ... | 538 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcess... | 448 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 0 |
"""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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
... | 275 | """simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( lower... | 275 | 1 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED... | 522 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : Optional[int] = 'SpeechT5FeatureExtractor'
_A : List[Any] = 'SpeechT... | 522 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not is_to... | 707 |
"""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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 109 | 0 |
'''simple docstring'''
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
fr... | 150 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock imp... | 34 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Tuple = {
"""fac... | 67 | """simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class snake_case__ ( snake_case_ ):
def a__ ( self , lowerCamelCase ):
with open(lowerCamelCase , encoding="utf-8" ) as input_file:
... | 67 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CAC... | 489 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : list[list[int]] ) -> Tuple:
_UpperCamelCase =TypeError(
'''Matrices must be ... | 404 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREA... | 712 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
def A__ ( self , snake_case_ ) -> Optional[int]:
... | 573 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ : List[str] =logging.get_logger(__name__)
__magic_name__ : Optional[Any] =[
['attention... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_... | 700 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : int , lowercase : int ) ->str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase__ = st... | 318 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCamelCase (_a ):
_lowercase = ["""image_processor""", """tokenizer"""]
_lowercase = """CLIPImageProcessor"""
_lowe... | 1 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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 ..pipeline_params import TEXT_GUIDED_IM... | 488 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 453 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCas... | 453 | 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 = {
"facebook/deit-base-distilled-patch16-22... | 475 |
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 lowercase__ ( uni... | 475 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
UpperCamelCase_ = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .loggin... | 508 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers... | 508 | 1 |
import numpy as np
def lowerCAmelCase_ ( __a ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_ ( __a ) -> np.array:
"""simple docstring"""
... | 258 |
"""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 transfor... | 174 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokeniza... | 173 | """simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_c... | 173 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCAmelCase__ ():
'''simple docstring'''
with offline(OfflineSimul... | 682 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
... | 616 | 0 |
from __future__ import annotations
import time
A__ = list[tuple[int, int]]
A__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
... | 219 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A__ = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}... | 219 | 1 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : O... | 31 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( se... | 31 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
if not is_t... | 75 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __UpperCamelCase ( _A : np.ndarray ) ->np.ndarray:
"""simple docstring"""
return input_array.reshape((input_array.size, 1) )... | 75 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_UpperCamelCase = TypeVar('''T''')
_UpperCamelCase = TypeVar('''U''')
class _lowerCamelCase ( Generic[T, U] ):
"""simple docstring"""
def __init__( ... | 243 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
def __init__( self : ... | 472 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...token... | 472 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration... | 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/... | 653 | 0 |
from math import sqrt
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase__ : int = True
# 0 and 1 are n... | 106 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_util... | 106 | 1 |
'''simple docstring'''
from timeit import timeit
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError("the value of input must not be negative" )
__UpperCAmelCase : Dict ... | 168 | '''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,
)
... | 168 | 1 |
from __future__ import annotations
def __lowercase ( __lowerCAmelCase : list[int] ): # This function is recursive
a__ = len(__lowerCAmelCase )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if ar... | 657 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 657 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE__ = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDe... | 532 | '''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> str:
'''simple docstring'''
_UpperCamelCase : str = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCamelCase : List[Any] = 1
for i in ra... | 435 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFI... | 717 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCamelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu ... | 152 | 0 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase : int ) -> None:
"""simple docstring"""
__UpperCamelCas... | 279 |
# 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 req... | 279 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 721 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 608 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( __snake_case ):
"""simple docstring"""
a = ["image_processor", "tokenizer"]
a = "ViTImageProcessor"
... | 338 | """simple docstring"""
from __future__ import annotations
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )-> None:
_SCREAMING_SNAKE_CASE : Optional[Any] = le... | 338 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_nump... | 710 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_comm... | 542 | 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: Any = {
"""bert-base-uncased"""... | 20 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 683 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a__ = ["image_processor", "tokenizer"]
a__ = "CLIPImageProcessor"
a__ = ("CLI... | 704 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subpr... | 17 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCamelCase( _a ):
lowercase_ : Dict = """"""
lowercase_ : str = (
None # p... | 89 |
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 TokenizerTesterMixin
@require_t... | 37 | 0 |
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 import DataLoader, Rand... | 704 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ = None ) -> None:
if components is None:
__U... | 682 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 32 |
"""simple docstring"""
import os
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCase : str = len(grid[0] )
lowerCAmelCase : List[str] = len(SCREAMING_SNAKE_CASE )
lowerCAmelCase : Optional[int] = ... | 645 | 0 |
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... | 488 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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 ..pipeline_params import TEXT_GUIDED_... | 488 | 1 |
import socket
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: Union[str, Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
UpperCAmelCase_: Optional[int] = socket.gethostname()
UpperCAmelCase_: List[... | 556 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_SCREAMING_SNAKE_CASE =[0] * (upper_limit + 1)
# Base cas... | 405 | 0 |
"""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,
resize... | 217 | """simple docstring"""
import math
def __a ( ) ->None:
a__: int = input('Enter message: ' )
a__: List[str] = int(input(F'Enter key [2-{len(_SCREAMING_SNAKE_CASE ) - 1}]: ' ) )
a__: int = input('Encryption/Decryption [e/d]: ' )
if mode.... | 217 | 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 CLIPModel, CL... | 660 |
'''simple docstring'''
def lowerCamelCase_ ( __UpperCamelCase : int , __UpperCamelCase : int ) -> int:
"""simple docstring"""
while second != 0:
_A = first & second
first ^= second
_A = c << 1
... | 292 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__A : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__A : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
def lowerCamelC... | 714 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__A : ... | 450 | 0 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = int(_lowercase )
if n_element < 1:
UpperCamelCase = ValueError('''a should be a positive number''' )
raise my_error
UpperCamelC... | 34 |
import numpy
# List of input, output pairs
snake_case__ : Optional[Any] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
snake_case__ : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9),... | 392 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONA... | 707 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case ( __lowercase ):... | 628 | 0 |
'''simple docstring'''
import cva
import numpy as np
class _A :
def __init__( self : Any , __magic_name__ : float , __magic_name__ : int ) -> Optional[int]:
"""simple docstring"""
if k in (0.04, 0.06):
__snake_c... | 26 |
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_ima... | 217 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""... | 717 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _snake_case ( lowercase__):
def A__ ( self : Optional[Any], __lowercase : str ):
with open(__lowercase, encoding="utf-8" ) as ... | 37 | 0 |
def __UpperCAmelCase ( a_):
return str(a_) == str(a_)[::-1]
def __UpperCAmelCase ( a_):
return int(a_) + int(str(a_)[::-1])
def __UpperCAmelCase ( a_ = 1_00_00):
snake_case_ = []
for num in range(1 , a_):
snake_case_ ... | 198 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
lowerCAmelCase = None
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = F... | 198 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 616 |
"""simple docstring"""
def A ( snake_case__ ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
SCREAMING_SNAKE_CASE__ = sum(snake_case__ ) / len(snake_case__ ) # Calculat... | 616 | 1 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import ... | 64 | 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.huggingface... | 64 | 1 |
import doctest
from collections import deque
import numpy as np
class lowercase__ :
def __init__( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = [2, 1, 2, -1]
SCREA... | 34 | # 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 a... | 34 | 1 |
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 import lo... | 576 | import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common... | 576 | 1 |
'''simple docstring'''
def UpperCAmelCase_ (__a : Optional[Any] , __a : Dict , __a : Any ):
"""simple docstring"""
def count_of_possible_combinations(__a : Optional[Any] ) -> int:
if target < 0:
return 0
if target == 0:
ret... | 711 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggi... | 319 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : str = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-... | 653 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co... | 653 | 1 |
"""simple docstring"""
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( UpperCamelCase_):
'''simple docstring'''
__magic_name__ : Union[str, Any] = (CMStochasticIterativeSchedule... | 712 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avail... | 595 | 0 |
'''simple docstring'''
def lowercase (_A = 1_0_0_0 ):
"""simple docstring"""
_lowerCAmelCase : List[str] = 2**power
_lowerCAmelCase : List[Any] = str(__snake_case )
_lowerCAmelCas... | 444 | '''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 274 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 702 | """simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__SCREAMING_SNAK... | 477 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase :... | 652 |
import math
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> list:
__lowerCamelCase : Union[str, Any] = [True] * n
__lowerCamelCase : List[Any] = False
__lowerCamelCase : int = False
__lowerCamelCase : An... | 652 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase (_lowerCAmelCase ):
return x + 2
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstri... | 719 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig''']... | 573 | 0 |
from math import factorial, radians
def lowercase_ ( __snake_case : float , __snake_case : int = 18 , __snake_case : int = 10 ) -> float:
'''simple docstring'''
snake_case__ :Optional[int] = angle_in_degrees - ((a... | 241 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _snake_case ( _A ):
@require_torch
def lowerCAmelCase_ ( self ) -> Tupl... | 241 | 1 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class snake_case__ ( unittest.TestCase):
def A ( self : Dict ) -> Any:
debug_launcher(test_scri... | 216 |
'''simple docstring'''
def __UpperCAmelCase ( A : int ) -> str:
UpperCAmelCase_ : Dict = int(A )
if decimal in (0, 1): # Exit cases for the recursion
return str(A )
UpperCAmelCase_ , UpperCAmelCase_ : Any = divmod(A , 2 ... | 216 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 61 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __lowerCamelCase ( __a :int ) ... | 176 | 0 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__magic_name__ = {
'''iou_prediction_he... | 530 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__magic_name__ ... | 530 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_dev... | 28 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
... | 593 | 0 |
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 accelerate import Accel... | 115 |
# limitations under the License.
# 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 .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
dep... | 115 | 1 |
'''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 transformers.utils ... | 435 | '''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTe... | 435 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# -... | 719 |
"""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 transformer... | 348 | 0 |
from __future__ import annotations
def UpperCamelCase_ ( __a ) -> bool:
a__ : str = str(__a )
return n == n[::-1]
def UpperCamelCase_ ( __a = 1_000_000 ) -> str:
a__ : Any = 0
for i in range(1 , __a ):
if is_palindrome(... | 37 |
class A__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] ):
a__ : str = name
a__ : Optional[int] = value
a__ : Dict = we... | 37 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acce... | 702 |
from __future__ import annotations
__magic_name__ : str = []
def lowercase__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase) -> bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase)):
if board[row]... | 410 | 0 |
SCREAMING_SNAKE_CASE = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
SCREAMING_SNAKE_CA... | 99 |
from collections.abc import Callable
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__a = a
__a = b
if function(lowerCAmelCase__ ) == 0: # one of the a or b is a root for the function
return a
elif function(lowerCAmelCase__ ) == 0:
... | 99 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def UpperCamelCase__ ( a_):
def wrapper(*a_ , **a_):
snake_case_ = timeit.default_timer()
snake_case_ ... | 708 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICA... | 607 | 0 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
A : Any = l... | 516 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( snake_case__ : int ) -> Dict:
UpperCamelCase ... | 40 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
"""simple docstring"""
def snake_case_( self )-> List[An... | 710 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class __A ( a ):
"""simple docstring"""
def __init__( self ... | 318 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is... | 144 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
... | 144 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCamelCase = datasets.utils.logging.get_logger(__name__)
class lowercase_ (folder_based_builder.FolderBasedBu... | 612 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCAmelCase ( UpperCamelCase_: Dict ) -> Any:
'''simple docstring'''
_a = os.path.jo... | 612 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..... | 53 |
from __future__ import annotations
def a_ ( lowerCAmelCase_ : list[float] ):
if len(lowerCAmelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be g... | 53 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ (snake_case_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( _a:ArgumentParser ):
raise NotImplementedError()
... | 208 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> str | Literal[False]:
snake_case__ = list(__lowerCAmelCase )
snake_case__ ... | 208 | 1 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : int ) -> int:
_UpperCAmelCase : Optional[int] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Lis... | 238 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Union[str, Any] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 594 | 0 |
import os
import string
import sys
_lowerCamelCase : Optional[int] = 1 << 8
_lowerCamelCase : Union[str, Any] = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARR... | 721 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_M... | 361 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's e... | 610 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Token... | 610 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ :List[str] = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve... | 483 |
'''simple docstring'''
import math
def __lowercase () -> None:
"""simple docstring"""
__lowerCamelCase : Optional[int] = input("""Enter message: """ )
__lowerCamelCase : Optional[Any] = int(input(f"Enter key [2-{len(_lowercase ) - 1}]: ... | 483 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_he... | 502 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
... | 252 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
A__ : Dict =TypeVar('T')
class __A ( Generic[T] ):
def __init__( self : Any , lowerCamelCase : T ):
"""simple docstring"""
__A : List[str] = data
_... | 718 |
'''simple docstring'''
A__ : int =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def A_ ( __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float , __SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
if moles < 0 ... | 499 | 0 |
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