code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 273 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A_ (datasets.BuilderConfig ):
UpperCAmelCase__ = None
class A_ (datasets.ArrowBasedBuilder ):
... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A_ :
@property
def _lowercase ( self ):
'''simple docstring'''... | 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__A : Union[str, Any] = get_... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_availab... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> str:
'''simple docstring'''
UpperCAmelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase = ''''''
UpperCAmelCase = ''''''
# append each character + "|" in new_stri... | 273 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__=None ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = N... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 1 |
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 ModelTesterMixin, ids_t... | 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 1 |
# 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 b... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A : ... | 273 |
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 ...tokeniz... | 273 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__A : Union[str, Any] = logging.get_logger(__name... | 273 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
assert x is not None
assert y is not None
UpperCAmelCase = len(UpperCamelCase__ )
UpperCAmelCase = len(UpperCamelCase__ )
# declaring the array fo... | 273 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ (a_ ):
UpperCAmelCase__ = (DEISMultistepScheduler,)
Uppe... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 1 |
import numpy as np
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1E-12 , UpperCamelCase__ = 100 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(UpperCamelCase__ )[0] == np.shape(UpperCamelCase__ )[1]
... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__A : Optional[Any]... | 273 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__A : Any = logging.get_logger(__name__)
__A :... | 273 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A_ (a_ , unittest.TestCase ):
UpperCAmelCase__ = TransfoXLTokenizer
UpperCAmelCase__ ... | 273 |
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 ConfigTester... | 273 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 273 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .to... | 273 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1024 ) -> Union[str, Any]:
'''simple docstring'''
... | 273 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTes... | 273 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class ... | 273 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 1 |
from __future__ import annotations
from typing import Any
class A_ :
def __init__( self , _A ):
'''simple docstring'''
UpperCAmelCase = num_of_nodes
UpperCAmelCase = []
UpperCAmelCase = {}
def _lowercase ( self , _A ... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProce... | 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A_ :
UpperCAmelCase__ = 42
UpperCAmelCase__ = None
UpperCAmelCase__ = None
__A : Union[str, Any] = namedtuple("CoinsDistribResult", "moves excess")
... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.s... | 273 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A_ (a_ , a_ , ... | 273 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> str:
'''simple docstring'''
def decorator(UpperCamelCase__ ):
UpperCAmelCase = getattr(UpperCamelCase__ , '''handle_key''' , [] )
handle... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 1 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__A : str = "scheduler_config.json"
class A_ (a_ ):
UpperCAmelCase__ = 1
UpperCAmelCase__ = 2
Uppe... | 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 1 |
from ...configuration_utils import PretrainedConfig
__A : str = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-fin... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.util... | 273 |
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 ...tokeniz... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A : Dict = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig",... | 273 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list:
'''simple docstring'''
UpperCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
UpperCAmelCase = True
for i in range(0 , len(UpperCamelCase__ ) - 1 , ... | 273 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 1 |
import os
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
__A : int = logging.get_logger(__name__)
__A : Tuple = {"vocab_file"... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 1 |
from __future__ import annotations
import math
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less t... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 273 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 1 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 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 ConfigTester... | 273 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Optional[Any] ={
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',... | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 2 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Any = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig... | 3 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCAmelCase_ :
def __init__( self : List[str] , UpperCAmelCase__ : bytes ) -> None:
lowerCAmelCase = data
# Initialize hash values
... | 4 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTes... | 273 | 0 |
from __future__ import annotations
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_lowercase =len(__snake_case )
# If row is equal to the ... | 5 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A : Tuple = 'src/diffusers'
# Matches is_xxx_available()
A : Any = re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx... | 6 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ... | 7 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://hugg... | 8 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 0 |
from bisect import bisect
from itertools import accumulate
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Tuple = sorted(zip(lowercase__ , lowercase__ ) , key=lambda... | 9 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
... | 10 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import... | 11 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 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, PreTrainedTokenizerBase, TensorType... | 12 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 0 |
import functools
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
# Validation
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for day in days ):
raise ValueError("The parameter days should be ... | 13 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 0 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowerCamelCase : Tuple = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
clas... | 14 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNA... | 15 |
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 ...tokeniz... | 273 | 0 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase_ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])... | 16 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 0 |
"""simple docstring"""
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, BertTok... | 17 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common im... | 18 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 0 |
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative"... | 19 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 0 |
import math
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
if (
not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor m... | 20 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_re... | 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__SCREAMING_SNAKE_CASE :Any = 50000
__SCREAMING_SNAKE_CASE :List[str] = 5000
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Dict = ... | 22 |
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 ConfigTester... | 273 | 0 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ... | 23 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCamelCase__ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONN... | 24 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 0 |
"""simple docstring"""
from math import sqrt
def lowercase_ ( _snake_case ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples ... | 25 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import... | 26 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTes... | 273 | 0 |
'''simple docstring'''
import requests
__lowercase : Tuple = '' # <-- Put your OpenWeatherMap appid here!
__lowercase : Tuple = 'https://api.openweathermap.org/data/2.5/'
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "Chicago" , _SCREAMING_SNAKE_CASE ... | 27 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin,... | 28 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIV... | 29 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
__a = ... | 30 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 0 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = (UnCLIPScheduler,)
def _A ( self : Li... | 31 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : List[str] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_... | 32 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 0 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = CustomTokenizer
pass
| 33 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 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... | 34 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 0 |
'''simple docstring'''
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 TokenizerTe... | 35 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 0 |
from math import ceil
def A ( _lowerCamelCase = 1_001 ):
'''simple docstring'''
_lowerCAmelCase : int = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
_lowerCAmelCase : List[Any] = 2 * i + 1
_lowerCAmelC... | 36 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 0 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nn... | 37 |
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 ...tokeniz... | 273 | 0 |
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int ) -> bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(__magic_name__ ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : ... | 38 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = "x" , __lowerCAmelCase = 10**-10 , __lowerCAmelCase = 1 , )-> complex:
"""simple docstring"""
_UpperCAm... | 39 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.nump... | 40 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 0 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> ... | 41 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import l... | 42 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 43 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ) -> int:
return DownloadCommand(args.model ,args.cache_dir ,args.force ,args.trust_remote_code )
clas... | 44 |
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 ConfigTester... | 273 | 0 |
"""simple docstring"""
import socket
def lowercase ( ) -> int:
__a = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__a = socket.gethostname()
__a = 12312
sock.connect((host, port) )
sock.send(b'''Hello server!''' ... | 45 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, ... | 46 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 0 |
'''simple docstring'''
import unittest
import numpy as np
def _lowerCAmelCase ( _UpperCamelCase : np.ndarray , _UpperCamelCase : np.ndarray , _UpperCamelCase : np.ndarray , _UpperCamelCase : np.ndarray | None = None , ) -> np.ndarra... | 47 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 0 |
import datasets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 48 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTes... | 273 | 0 |
import math
def __snake_case ( _UpperCAmelCase = 100 ):
__a = sum(i * i for i in range(1 , n + 1 ) )
__a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'{soluti... | 49 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 0 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
lowerCamelCase__ : Tuple = {
'en': 'Machine learning is great, isn\'t it?',
... | 50 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
snake_case_ : Optional[Any] = 3
def A (__A : int ) -> int:
"""simple docstring"""
print('''Generating primitive root of p... | 51 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 0 |
class A__ :
def __init__( self , A_ , A_=None , A_=None ):
'''simple docstring'''
UpperCamelCase : List[str] = data
UpperCamelCase : Optional[int] = previous
UpperCamelCase : Optional[Any] ... | 52 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : int = 10**9 ) -> int:
"""simple docstring"""
__UpperCamelCase = 1
__UpperCamelCase = 2
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 0... | 53 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 0 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1024 ):
'''simple docstring'''
... | 54 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case ( ctypes.Structure ):
"""simple docstring"""
_lowerCamelCa... | 55 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[str] = get_tests_dir('fi... | 56 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 0 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a="" , __a="train" ):
assert os.path.isdir(__a )
... | 57 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
class a_ ( snake_case_ , snake_case_ ... | 58 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 0 |
import os
from pathlib import Path
def UpperCamelCase ( ):
from torch.utils.cpp_extension import load
snake_case : str = Path(__lowerCamelCase ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
snake_case : int = [
... | 59 |
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 ...tokeniz... | 273 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case__ : str = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''... | 60 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.