code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise O... | 61 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 61 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise Op... | 61 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
lowerCAmelCase__ = size
lowerCAmelCase__ = [0] * size
lowerCAmelCase__ = ... | 61 | 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_... | 61 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fi... | 61 | 1 |
from __future__ import annotations
UpperCamelCase = '#'
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
lowerCAmelCase__ = {}
def a ( self : Any , SCREAMING_SNAKE_CASE__ : str ... | 61 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.in... | 61 | 1 |
import math
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0 ) -> Optional[Any]: # a graph with Node 0,1,...,N-1
lowerCAmelCase__ = n
lowerCAmelCase__ ... | 61 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'sentencepi... | 61 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 61 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 61 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTes... | 61 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 61 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'fac... | 61 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]=1000 ):
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n ... | 61 |
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 ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
lowe... | 61 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = [
['attention', 'attn'],
['encod... | 61 |
import random
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
"""simple docstring"""
lowerCAmelCase__ = a[left_index]
lowerCAmelCase__ = left_index + 1
for j in range(left_index + 1 ... | 61 | 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_dimen... | 61 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 1 |
from __future__ import annotations
from collections import deque
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : list[str] ) -> Optional[int]:
lowerCAmelCase__ = []
self.a... | 61 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transf... | 61 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at htt... | 61 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = (KDPMaDiscreteSchedu... | 61 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : List[A... | 61 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : ... | 61 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_... | 61 | 1 |
from itertools import permutations
def _A ( lowerCAmelCase_ : tuple ):
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowerCA... | 61 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
... | 61 | 1 |
UpperCamelCase = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_avai... | 61 |
from __future__ import annotations
UpperCamelCase = '#'
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
lowerCAmelCase__ = {}
def a ( self : Any , SCREAMING_SNAKE_CASE__ : str ... | 61 | 1 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE__ : list ) -> None:
lowerCAmelCase__ = set_counts
lowerCAmelCase__ = max(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ ... | 61 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 1 |
import numpy
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : numpy.ndarray , SCREAMING_SNAKE_CASE__ : numpy.ndarray ) -> None:
lowerCAmelCase__ = input_array
... | 61 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = ArgumentParser(
description=(
... | 61 | 1 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fixtures/spiece.mo... | 61 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
im... | 61 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is... | 61 | 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.I... | 61 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
... | 61 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 61 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@requ... | 61 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
lowerCAmelCase__ = size
lowerCAmelCase__ = [0] * size
lowerCAmelCase__ = ... | 61 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase = version.parse(version.pars... | 61 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fi... | 61 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'... | 61 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.in... | 61 | 1 |
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
UpperCamelCase ... | 61 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'sentencepi... | 61 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _A ( ):
"""simple docstring"""
raise RuntimeError("CUDA out of memory." )
class ... | 61 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 61 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase = logging.getLogger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
... | 61 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 61 | 1 |
UpperCamelCase = range(2, 20 + 1)
UpperCamelCase = [10**k for k in range(ks[-1] + 1)]
UpperCamelCase = {}
def _A ( lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] ):
... | 61 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 1 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCamelCase = 1.0_5457_1817E-34 # unit of ℏ : J * s
UpperCamelCase = 3E8 # unit of c : m * s^-1
def _A ( lowerCAmel... | 61 |
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 ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
lowe... | 61 | 1 |
def _A ( lowerCAmelCase_ : Optional[Any] ):
"""simple docstring"""
lowerCAmelCase__ = [0] * len(lowerCAmelCase_ )
lowerCAmelCase__ = []
lowerCAmelCase__ = [1] * len(lowerCAmelCase_ )
for values in graph.values()... | 61 |
import random
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
"""simple docstring"""
lowerCAmelCase__ = a[left_index]
lowerCAmelCase__ = left_index + 1
for j in range(left_index + 1 ... | 61 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE__ : Collection[float] | None = Non... | 61 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 1 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
UpperCamelCase = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': ... | 61 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at htt... | 61 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _A ( lowerCAmelCase_ : Any ):
... | 61 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : List[A... | 61 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self ... | 61 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_... | 61 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimes... | 61 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
... | 61 | 1 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
... | 61 |
from __future__ import annotations
UpperCamelCase = '#'
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
lowerCAmelCase__ = {}
def a ( self : Any , SCREAMING_SNAKE_CASE__ : str ... | 61 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 61 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = "timm_backbone"
def __init... | 61 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = ArgumentParser(
description=(
... | 61 | 1 |
UpperCamelCase = {str(digit): digit**5 for digit in range(10)}
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase_ ) )
def _A ( ):
"""simple docstring"""
return... | 61 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def _A ( lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] ):
"""simple docstring"""
lowerCAmelCase__ ... | 61 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is... | 61 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
... | 61 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 | 1 |
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 __lowerCamelCase ( ... | 61 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 61 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
UpperCamelCase = logging.... | 61 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
lowerCAmelCase__ = size
lowerCAmelCase__ = [0] * size
lowerCAmelCase__ = ... | 61 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fi... | 61 | 1 |
import sys
UpperCamelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403... | 61 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.in... | 61 | 1 |
from __future__ import annotations
import math
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
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 e... | 61 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'sentencepi... | 61 | 1 |
import functools
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = len(lowerCAmelCase_ )
lowerCAmelCase__ = len(lowerCAmelCase_ )
@functools.cache
def min_dista... | 61 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 61 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'fac... | 61 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 61 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase = logging.get_logger(__name__)
U... | 61 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowerCamelCase :
"""simple docstring"""
def __init__( ... | 61 |
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 ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
lowe... | 61 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = (EulerDiscreteSchedu... | 61 |
import random
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
"""simple docstring"""
lowerCAmelCase__ = a[left_index]
lowerCAmelCase__ = left_index + 1
for j in range(left_index + 1 ... | 61 | 1 |
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int = 0 ):
"""simple docstring"""
lowerCAmelCase__ = length or len(lowerCAmelCase_ )
lowerCAmelCase__ = False
for i in range(length - 1 ):
if list_data[i] > list_... | 61 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 1 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.in... | 61 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve... | 61 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at htt... | 61 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord imp... | 61 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : List[A... | 61 | 1 |
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _A ( lowerCAmelCase_ : s... | 61 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_... | 61 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPip... | 61 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
... | 61 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://hu... | 61 |
from __future__ import annotations
UpperCamelCase = '#'
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
lowerCAmelCase__ = {}
def a ( self : Any , SCREAMING_SNAKE_CASE__ : str ... | 61 | 1 |
from math import pi, sqrt, tan
def _A ( lowerCAmelCase_ : float ):
"""simple docstring"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def _A ( lowerCAmelCase_ : floa... | 61 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
UpperCamelCase = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def _A ( lowerCAmelCase_ : str = "mumbai" ):
"""simple docstring"""
... | 61 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = ArgumentParser(
description=(
... | 61 | 1 |
from __future__ import annotations
import time
import numpy as np
UpperCamelCase = [8, 5, 9, 7]
UpperCamelCase = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
UpperCamelCase = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5,... | 61 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precisi... | 61 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is... | 61 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase = 10
def _A ( lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCa... | 61 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCH... | 61 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 61 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def a ( self : Optional[Any] ) -> Optional[int]:
lowerCAmelCa... | 61 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
lowerCAmelCase__ = size
lowerCAmelCase__ = [0] * size
lowerCAmelCase__ = ... | 61 | 1 |
import math
import sys
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = ""
try:
with open(lowerCAmelCase_ , "rb" ) as binary_file:
lowerCAmelCase__ = binary_file.read()
... | 61 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fi... | 61 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fi... | 61 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.in... | 61 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __lowerCamelCase ( ... | 61 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'sentencepi... | 61 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _A ( lowerCAmelCase_ ... | 61 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 61 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : str , ... | 61 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 61 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( UpperCamelCase__ ... | 61 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCamelCase = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for ... | 61 |
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 ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
lowe... | 61 | 1 |
import random
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
"""simple docstring"""
lowerCAmelCase__ = a[left_index]
lowerCAmelCase__ = left_index + 1
for j in range(left_index + 1 ... | 61 |
import random
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
"""simple docstring"""
lowerCAmelCase__ = a[left_index]
lowerCAmelCase__ = left_index + 1
for j in range(left_index + 1 ... | 61 | 1 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase_... | 61 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 1 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list[int] , lowerCAmelCase_ : list[int] , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = list(range(len(lowerCAmelCase_ ) ) )
lowerCAmelCase__ ... | 61 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 | 1 |
from __future__ import annotations
from collections.abc import Callable
def _A ( lowerCAmelCase_ : Callable[[int | float], int | float] , lowerCAmelCase_ : int | float , lowerCAmelCase_ : int | float , lowerCAmelCase_ : int = 100 , ):
"""simple docstri... | 61 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at htt... | 61 | 1 |
from __future__ import annotations
from random import random
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : int | None = None ) -> Optional[int]:
lowerCAmelCase__ = value
... | 61 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : List[A... | 61 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_me... | 61 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_... | 61 | 1 |
from functools import lru_cache
@lru_cache
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main_... | 61 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
... | 61 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
... | 61 |
from __future__ import annotations
UpperCamelCase = '#'
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
lowerCAmelCase__ = {}
def a ( self : Any , SCREAMING_SNAKE_CASE__ : str ... | 61 | 1 |
def _A ( lowerCAmelCase_ : list[int] , lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = int(lowerCAmelCase_ )
# Initialize Result
lowerCAmelCase__ = []
# Traverse through all denomination
for den... | 61 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 1 |
def _A ( lowerCAmelCase_ : int = 10 , lowerCAmelCase_ : int = 1000 , lowerCAmelCase_ : bool = True ):
"""simple docstring"""
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase... | 61 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = ArgumentParser(
description=(
... | 61 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __lowerCamelCase ( ... | 61 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __lowerCamelCase ( UpperCamelCase__ ):
... | 61 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is... | 61 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'microsoft/focalnet-tiny': 'https:/... | 61 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 | 1 |
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 ( lowerCAmelCase_ : ... | 61 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 61 | 1 |
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = 0
for ch in input_str:
lowerCAmelCase__ = ord(lowerCAmelCase_ )
lowerCAmelCase__ = pow(2 , lowerCAmelCase_ )
... | 61 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
lowerCAmelCase__ = size
lowerCAmelCase__ = [0] * size
lowerCAmelCase__ = ... | 61 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCamelCase = datasets.utils.log... | 61 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fi... | 61 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.in... | 61 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __lowerCamelCase :
"""simple docstring"""
snake_case__ = 42
snake_case__ = None
snake_case__ = None
UpperCamelCase ... | 61 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'sentencepi... | 61 | 1 |
UpperCamelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCamelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( lowerCAmelCase_ : dict[int, list[int]] , lowerCAmelCase_ : int , lowerCAmelCase_ : list[bool] ):
"""simple do... | 61 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 61 | 1 |
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 ... | 61 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 61 | 1 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 61 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 1 |
from math import factorial
def _A ( lowerCAmelCase_ : int = 20 ):
"""simple docstring"""
lowerCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase__ = n // 2
ret... | 61 |
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 ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
lowe... | 61 | 1 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 |
import random
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
"""simple docstring"""
lowerCAmelCase__ = a[left_index]
lowerCAmelCase__ = left_index + 1
for j in range(left_index + 1 ... | 61 | 1 |
import re
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
if len(re.findall("[ATCG]" , lowerCAmelCase_ ) ) != len(lowerCAmelCase_ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC"... | 61 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 | 1 |
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