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 |
|---|---|---|---|---|
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fro... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCamelCase_ : int = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def A... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
snake_case : Any = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action=... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( A_ ):
'''simple docstring'''
__Uppe... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 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 .benchmark... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class _UpperCamelCase ( a__ ):
'''simple docstring'''
__UpperCamelCase : Tuple ... | 710 |
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,
AutoModelForMultipleChoice,
... | 670 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A__ ( lowerCamelCase ) -> Optional[... | 711 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSo... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
from collections.abc import Callable
import numpy as np
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> np.array:
UpperCamelCase_: Tuple = int(np.ceil((x_end - xa) / step_size ) )
Up... | 713 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 670 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Union[str, Any] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , low... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Tokeniz... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
__UpperCamelCase : Dict = 42
_... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Any = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
from __future__ import annotations
def A__ ( lowerCamelCase ) -> Union[str, Any]:
UpperCamelCase_: str = str(snake_case_ )
return n == n[::-1]
def A__ ( lowerCamelCase = 1_00_00_00 ) -> List[Any]:
UpperCamelCase_: Tuple = ... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
lowerCamelCase_ : str = namedtuple(
"""_TestCommandArgs""",
[
""... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
from __future__ import annotations
def A__ ( lowerCamelCase ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(lowerCamelCas... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCamelCase_ : Union[str, Any] ... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_ : Optional[int] = logging.getLogger()
def A__ ( ... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 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 TokenizerTest... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : int = {
"""shi-labs/dinat-mini-i... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
import operator
def A__ ( lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None ) -> list:
UpperCamelCase_: Union[str, Any] = operator.lt if reverse else operator.gt
UpperCamelCase_: List[Any] = solution or []
if not a... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ) -> Dict:
UpperCamelCase_: Optional[Any] = 9
UpperCamelCase_: Dict = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, ... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
def A__ ( lowerCamelCase ) -> list[int]:
UpperCamelCase_: List[Any] = [0 for i in range(len(lowerCamelCase ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase_: str = 0, 0
for i in range(1 , len(lowerCamelCase ... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
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,
AutoModelForMultipleChoice,
... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
lowerCamelCase_ : List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAv... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWith... | 710 |
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,
AutoModelForMultipleChoice,
... | 670 | 0 |
import math
def A__ ( ) -> None:
'''simple docstring'''
UpperCamelCase_: Optional[Any] = input("""Enter message: """ )
UpperCamelCase_: str = int(input(F'''Enter key [2-{len(lowerCamelCase ) - 1}]: ''' ) )
UpperCamelCase_: Li... | 711 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONA... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
lowerCamelCase_ : Union[str, Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def A__ ( lowerCamelCase ) -> int:
UpperCamelCase_: List[str] = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
UpperCamelC... | 713 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ : Any = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
i... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arr... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
Upp... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _UpperCamelCase ( _A , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Op... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQ... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def A__ ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.ut... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> List[Any]:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def A__ ( lowerCamelCase , lowerCamelCase=0 ) -> Optional[Any]:
return sorted(lowerCamelCase , key=lambda lowerCam... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase_ : Opti... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : Optional[int] = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAI... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : List[Any] ):
UpperCamelCase_: Optional[Any] = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: List[str] = Rectangl... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enab... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
import operator as op
def A__ ( lowerCamelCase ) -> Optional[int]:
UpperCamelCase_: Optional[int] = []
UpperCamelCase_: Optional[int] = lambda lowerCamelCase , lowerCamelCase : int(x / y ) # noqa: E731 integer division operation
UpperCame... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCamelCase_ : Tuple = """__DUMMY_TRANSFORMERS_USER__"""
lowerCamelCase_ : List[Any] = """Dummy User"""
lowerCamelCas... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
def A__ ( lowerCamelCase ) -> list:
UpperCamelCase_: Union[str, Any] = int(lowerCamelCase )
if n_element < 1:
UpperCamelCase_: Dict = ValueError("""a should be a positive number""" )
raise my_error
UpperCamelCase_: int = [1]
... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
from collections import defaultdict
def A__ ( lowerCamelCase ) -> int:
UpperCamelCase_: List[str] = 1
UpperCamelCase_: Optional[Any] = True
for v in tree[start]:
if v not in visited:
ret += dfs(lowerCamelCase )
if ret % 2... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=_A ):
'''simple docstring'''
__UpperCamelCase : Optional[Any] = ["""flax"""]
def __init__( self : Any , *snake_case_ : Union[str, Any] , **snake_case_ : ... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME... | 710 |
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,
AutoModelForMultipleChoice,
... | 670 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _UpperCamelCase ( _A ):
'''simple docstring'''
... | 711 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
lowerCamelCase_ : str = {
"""vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
# See all GLPN... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 713 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 670 | 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:
UpperCamelCase_: Optional[Any] = ... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
import random
def A__ ( lowerCamelCase , lowerCamelCase ) -> tuple:
UpperCamelCase_: Tuple = [], [], []
for element in data:
if element < pivot:
less.append(lowerCamelCase )
elif element > pivot:
greater.... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ : List[Any] = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CON... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
from math import pow, sqrt
def A__ ( *lowerCamelCase ) -> bool:
UpperCamelCase_: Tuple = len(lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def A__ ( lowerCamelCase , lowerCamelCase ) -> float | ... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class _UpperCamelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any , snake_case_ : int , snake_case_ : Any , snake_case_ : Dict , snake_case_ : ... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Optional[Any] , snake_case_ : List[str] ):
UpperCamelCase_: Optional[Any] = total # total no of tasks (N)
... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_u... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImagePro... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( lowerCamelCase ) -> tuple:
return (data["data"], ... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while a != 0:
UpperCamelCase_: Dict = b % a, a
return b
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
if gcd(lowerCamelCase , lowerCamelCase ) != 1:
... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase_ : Union[str, Any] = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation a... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
lowerCamelCase_ : Optional[Any] = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k"... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
import re
import string
import numpy as np
import datasets
lowerCamelCase_ : Optional[int] = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
lowerCamelCase_ : Optional[Any]... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : List[str] = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
lowerCamelCase_ : Union[str, Any] = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json"... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ : Optional[Any] = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_x... | 710 |
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,
AutoModelForMultipleChoice,
... | 670 | 0 |
def A__ ( lowerCamelCase ) -> list:
'''simple docstring'''
def merge(lowerCamelCase , lowerCamelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from lef... | 711 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
# 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 applicab... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase_ : Union[str, Any] = """src/transforme... | 713 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 670 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def A__ ( lowerCamelCase , lowerCamelCase ) -> Generator[tuple[str, ...], None, None]:
UpperCamelCase_: Tuple = iter(lowerCamelCase )
while True:
UpperCamelCase_: Opt... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowerCamelCase_ : Optional[Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowerCamelCase_ : Tuple = typing.Union[np.floataa, int, float] # no... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ : int = logging.get_logger(__name__)
lowerCamelCase_ : int = {
"""SenseTime/deformable-detr""": """https://huggingface.co/sensetime/def... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 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_convbert import ConvBertTokenizer
lowerCamelCase_ : int = logging.get_logger(__name__)
lowerCamelCase... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def A__ ( lowerCamelCase ) -> str:
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Any = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBar... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
def A__ ( lowerCamelCase ) -> bool:
return str(lowerCamelCase ) == str(lowerCamelCase )[::-1]
def A__ ( lowerCamelCase ) -> int:
return int(lowerCamelCase ) + int(str(lowerCamelCase )[::-1] )
def A__ ( lowerCamel... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
lowerCamelCase_ : str = logging.get_logger(__name__... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> int:
if height >= 1:
move_tower(height - 1 , lowerCamelCase , lowerCamelCase , lowerCamelCase )
move_disk(lowerCamelCase , lowerCamelCase ... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils ... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCamelCase_ : List[Any] = logging.get_logger(__name__)
lowerCamelCase_ : str = Or... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
snake_case : Di... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
import math
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
return math.pow(lowerCamelCase , 2 ) - a
def A__ ( lowerCamelCase ) -> float:
return 2 * x
def A__ ( lowerCamelCase ) -> float:
U... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
import numpy
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : str , snake_case_ : numpy.ndarray , snake_case_ : numpy.ndarray ):
UpperCamelCase_: str = input_array
# Random initial weights are assigned where fir... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase ) -> Tuple:
#... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
def A__ ( lowerCamelCase ) -> int:
if not isinstance(lowerCamelCase , lowerCamelCase ):
UpperCamelCase_: Optional[int] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCamelCase )
if number < 1:
UpperCa... | 710 |
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,
AutoModelForMultipleChoice,
... | 670 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 711 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
from __future__ import annotations
import math
def A__ ( lowerCamelCase ) -> bool:
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 of 3 are not pr... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
import torch
from transformers import AutoModel
class _UpperCamelCase ( torch.nn.Module ):
def __init__( self : Optional[Any] , snake_case_ : int="sayef/fsner-bert-base-uncased" ):
super(snake_case_ , self ).__init__()
UpperCamelCase_: Opt... | 713 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 670 | 0 |
import os
from math import logaa
def A__ ( lowerCamelCase = "base_exp.txt" ) -> int:
UpperCamelCase_: float = 0
UpperCamelCase_: str = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase ) , lowerCamelCase ) ) ... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
from __future__ import annotations
from typing import TypedDict
class _UpperCamelCase ( _A ):
'''simple docstring'''
__UpperCamelCase : str
__UpperCamelCase : int
def A__ ( lowerCamelCase ) -> list[str]:
if no... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
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