code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __magic_name__( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
if not is_accelerate_available():
return method
... | 718 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
snake_case__ = 0B101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bi... | 719 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fr... | 720 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class UpperCAmelCase__ ( __lowercase ):
'''simple docstring'''
A_ = field(default='questio... | 721 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_... | 700 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import Tensor... | 701 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 0 |
from __future__ import annotations
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]: # noqa: E741
'''simple docstring'''
while r - l > 1:
_lowerCamelCase = (l + r) // 2
... | 702 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name... | 703 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
snake_case__ = datasets.utils.logging.get_logger(__name__)
class UpperCamelCase ( folder_based_builder.FolderBasedBuilderConfig ):
'''simpl... | 704 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
... | 638 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class UpperC... | 705 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 638 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamel... | 706 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
snake_case__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def __magic_name__( __UpperCAmelCa... | 707 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcess... | 638 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils ... | 708 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __magic_name__( __UpperCAmelCase ) -> Any:
'''simple docstring'''
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.... | 709 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 710 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenize... | 711 | 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
... | 638 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 712 | import argparse
import json
from tqdm import tqdm
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__UpperCAmelCase ... | 638 | 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 Dat... | 713 | 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, CLIPTokenizerF... | 638 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
... | 714 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
snake_case__ = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
t... | 715 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | 0 |
import re
from filelock import FileLock
try:
import nltk
snake_case__ = True
except (ImportError, ModuleNotFoundError):
snake_case__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name__( __UpperCAmelCase ) ... | 716 | from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 717 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowercase ) , 'Tatoeba direc... | 638 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 718 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 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
... | 719 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
... | 720 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, E... | 721 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCamelCase_ ( A_ ) -> List[str]:
"""simple docstring"""
... | 700 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 0 |
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(__UpperCAm... | 701 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_m... | 702 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackbon... | 703 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`') | 704 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
... | 638 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.W... | 705 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 638 | 0 |
'''simple docstring'''
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_aut... | 706 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
snake_case__ = HfArgumentParser(InitializationArguments)
snake_case__ = parser.parse_args()
# Load codeparrot tokenizer trained for Python code toke... | 707 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcess... | 638 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'pr... | 708 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .be... | 709 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 0 |
from __future__ import annotations
import queue
class UpperCamelCase :
'''simple docstring'''
def __init__( self , A_ ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = data
_lowerCamelCase = None... | 710 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 0 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
... | 711 | 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
... | 638 | 0 |
import requests
from bsa import BeautifulSoup
def __magic_name__( __UpperCAmelCase = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
_lowerCamelCase = BeautifulSoup(requests.get(__UpperCAmelCase ).text , '''html.parser''' )
_lo... | 712 | import argparse
import json
from tqdm import tqdm
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__UpperCAmelCase ... | 638 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
snake_case__ = True
ex... | 713 | 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, CLIPTokenizerF... | 638 | 0 |
from typing import Any
class UpperCamelCase :
'''simple docstring'''
def __init__( self , A_ ) -> Dict:
"""simple docstring"""
_lowerCamelCase = data
_lowerCamelCase = None
def __repr__( se... | 714 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 0 |
'''simple docstring'''
def __magic_name__( __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = [0] * len(__UpperCAmelCase )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = 0
fo... | 715 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
snake_case__ = logging.get_logger(__name__)
snake_case__ = {'vocab_file': 'vocab... | 716 | from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | 0 |
def __magic_name__( __UpperCAmelCase ) -> int: # noqa: E741
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = 0
_lowerCamelCase = [0] * n
_lowerCamelCase = [False] * n
_lowerCamelCase = ... | 717 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowercase ) , 'Tatoeba direc... | 638 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'google/mobilenet_v2_1.4_2... | 718 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 719 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 0 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
... | 720 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 0 |
snake_case__ = range(2, 20 + 1)
snake_case__ = [10**k for k in range(ks[-1] + 1)]
snake_case__ = {}
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
_lowerCamelCase = ... | 721 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 0 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str = "AAPL" ) -> str:
'''simple docstring'''
_UpperCAmelCase = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCa... | 639 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tra... | 639 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_toke... | 639 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while second != 0:
_UpperCAmelCase = first & second
first ^= second
_UpperCAmelCase = c << 1
return first
if __name__ == "__main__... | 639 | 1 |
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 SequenceFeat... | 639 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A ( _UpperCAmelCase : str , _UpperCAmelCase : complex , _UpperCAmelCase : str = "x" , _UpperCAmelCase : float = 10**-10 , _UpperCAm... | 639 | 1 |
def A ( _UpperCAmelCase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(_UpperCAmelCase ) ):
series.append(F"1/{temp + 1}" if series else '1' )
return series
if __name_... | 639 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 639 | 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 __lowerCAmelCase ( A ... | 639 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
_UpperCAme... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 639 |
import qiskit
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q regis... | 639 | 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
)
UpperCAmelCa... | 639 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmel... | 639 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
UpperCAmelCase__ = TypeVar("KT")
UpperCAmelCase__ = TypeVar("VT")
class __lowerCAmelCase ( Generic[KT, VT] ):
def __init__( self : Optional[int] , A : KT | str = ... | 639 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_IMAGE_INP... | 639 | 1 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_... | 639 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
_Upper... | 639 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from ... | 639 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_UpperCAmelCase = str(bin(_UpperCAmelCase ) )[2:] # r... | 639 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Auto... | 639 |
from collections import Counter
from timeit import timeit
def A ( _UpperCAmelCase : str = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def A ... | 639 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 639 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 639 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) !... | 639 |
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 .tokenization_xlnet impo... | 639 | 1 |
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_subprocess_async,
ge... | 639 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
... | 639 | 1 |
def A ( _UpperCAmelCase : list[list[int | float]] ) -> int:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = len(matrix[0] )
_UpperCAmelCase = min(_UpperCAmelCase , _UpperCAmelCase )
for row ... | 639 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Auto... | 639 | 1 |
def A ( _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> str:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = F"Expected string as input, found {type(_UpperCAmelCase... | 639 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = "https://openaipublic.... | 639 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 639 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_avai... | 639 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/conf... | 639 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torc... | 639 | 1 |
from itertools import product
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_number * dice_number
_UpperCAmelCase = [0] ... | 639 |
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 A ( _UpperCAmelCase : Lis... | 639 | 1 |
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, ids_tensor, random_attention_mask
fro... | 639 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 639 | 1 |
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 __lowerCAmelCase ( A ):
UpperCamelCase ... | 639 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokeniz... | 639 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils imp... | 639 |
def A ( _UpperCAmelCase : list ) -> list:
'''simple docstring'''
if len(_UpperCAmelCase ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_UpperCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase... | 639 | 1 |
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_... | 639 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tra... | 639 | 1 |
import doctest
from collections import deque
import numpy as np
class __lowerCAmelCase :
def __init__( self : Tuple) -> None:
"""simple docstring"""
_UpperCAmelCase = [2, 1, 2, -1]
_UpperCAmelCase = [1, 2, 3, 4]
def _lowerCamelC... | 639 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while second != 0:
_UpperCAmelCase = first & second
first ^= second
_UpperCAmelCase = c << 1
return first
if __name__ == "__main__... | 639 | 1 |
from math import factorial
def A ( _UpperCAmelCase : int = 20 ) -> int:
'''simple docstring'''
_UpperCAmelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_UpperCAmelCase = n // 2
return int(factor... | 639 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A ( _UpperCAmelCase : str , _UpperCAmelCase : complex , _UpperCAmelCase : str = "x" , _UpperCAmelCase : float = 10**-10 , _UpperCAm... | 639 | 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
... | 639 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 639 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase ( A , unittest.TestCase ):
U... | 639 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
_UpperCAme... | 639 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CON... | 639 |
import qiskit
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q regis... | 639 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : Any , _UpperCAmelCase ... | 639 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmel... | 639 | 1 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self : List[Any] , A : int) -> None:
"""simple docstring"""
_UpperCAmelCase = order
# a_{0} ... a_{k}
_UpperCAmelCase = [1.0] + [0.0] * order
... | 639 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_IMAGE_INP... | 639 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowerCAmelCase ( A , A ):
UpperCamelCase = 1
... | 639 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
_Upper... | 639 | 1 |
from __future__ import annotations
import queue
class __lowerCAmelCase :
def __init__( self : Tuple , A : Tuple) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = data
_UpperCAmelCase = None
_UpperCAmelCase = None
... | 639 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_UpperCAmelCase = str(bin(_UpperCAmelCase ) )[2:] # r... | 639 | 1 |
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, torch_device
from diffusers.ut... | 639 |
from collections import Counter
from timeit import timeit
def A ( _UpperCAmelCase : str = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def A ... | 639 | 1 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def A ( _Up... | 639 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 639 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common... | 639 |
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 .tokenization_xlnet impo... | 639 | 1 |
import numpy as np
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : int ) -> Dict:
'''simple docstr... | 639 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
... | 639 | 1 |
from scipy.stats import spearmanr
import datasets
UpperCAmelCase__ = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply ... | 639 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Auto... | 639 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
Data... | 639 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = "https://openaipublic.... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file... | 639 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_avai... | 639 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common im... | 639 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torc... | 639 | 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,
)
fr... | 639 |
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 A ( _UpperCAmelCase : Lis... | 639 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/mai... | 639 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 639 | 1 |
import os
from distutils.util import strtobool
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
for e in env_keys:
_UpperCAmelCase = int(os.environ.get(_UpperCAmelCase , -1 ... | 639 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokeniz... | 639 | 1 |
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplic... | 639 |
def A ( _UpperCAmelCase : list ) -> list:
'''simple docstring'''
if len(_UpperCAmelCase ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_UpperCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase... | 639 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 639 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tra... | 639 | 1 |
import math
import sys
import cva
import numpy as np
def A ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
'''simple docstring'''
# For applying gaussian function for each element in matrix.
_UpperCAmelCase = m... | 639 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while second != 0:
_UpperCAmelCase = first & second
first ^= second
_UpperCAmelCase = c << 1
return first
if __name__ == "__main__... | 639 | 1 |
# 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 appli... | 639 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A ( _UpperCAmelCase : str , _UpperCAmelCase : complex , _UpperCAmelCase : str = "x" , _UpperCAmelCase : float = 10**-10 , _UpperCAm... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is... | 639 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 639 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_IMAGE_INP... | 639 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
_UpperCAme... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
... | 639 |
import qiskit
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q regis... | 639 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : int , A : str) -> str:
"""simple docstring"""
with open(A , encoding... | 639 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmel... | 639 | 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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, D... | 639 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_IMAGE_INP... | 639 | 1 |
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