code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import string
def _A ( _lowerCAmelCase ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
__lowercase =''
for symbol in message:
if symbol in string.ascii_uppercase:
... | 48 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 | 1 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone im... | 48 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : L... | 48 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCamelCase ( A ):
'''simple docstring'''
def __lowerCamelCase ( self : Optional[int] , _lowerCAmelCase : Tuple=None , _lowerCAme... | 48 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase = logging.get_logger(__name__)... | 48 |
'''simple docstring'''
import functools
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase ,... | 48 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import lo... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
l... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 48 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : Dict , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple):
... | 48 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 48 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCamelCase = logging.getLogger(__name__)
class _UpperCamelCase ( A ):
'''s... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 48 | 1 |
'''simple docstring'''
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , _lowerCAmelCase : Tuple):
'''simple docstring'''
__lowercase =val
__lowercase =None
__lowercase =... | 48 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.Test... | 48 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase ... | 48 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import... | 48 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 | 1 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/li... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_C... | 48 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : Dict , ... | 48 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =SwinConfig(image_size=192 )
... | 48 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_p... | 48 |
'''simple docstring'''
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 com... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =int(number**0.5 )
return number == sq * sq
def _A ( _lowerCAmelCase ... | 48 |
'''simple docstring'''
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 = logging.get_lo... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if numb... | 48 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.Test... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[]
__lowercase =set({'(', '[', '{'} )
__lowercase =set({')', ']', '}'} )
__lowercase ={'{': '}', '[': ']', '(': ')'}
for i in range(len(_low... | 48 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError('Input value must be ... | 48 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from trans... | 48 | 1 |
'''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils i... | 48 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCas... | 48 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowerCamelCase = log... | 48 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVec... | 48 | 1 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _A ( _lowerCAmelCase , _lowerCAmelCase=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join... | 48 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data im... | 48 | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.sp... | 48 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 | 1 |
'''simple docstring'''
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 = logging.get_logger(__name__)
lowerCamelCase = {"... | 48 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {"""processing_layoutxlm""": ["""LayoutXLMPr... | 48 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 | 1 |
'''simple docstring'''
from math import factorial, pi
def _A ( _lowerCAmelCase , _lowerCAmelCase = 30 ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float f... | 48 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
# We need to create solution object to save path.
__lowercase =[[0 for _ in range(_lowerCAmelCase )] for ... | 48 |
'''simple docstring'''
import functools
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase ,... | 48 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
l... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__lowercase =n - k
# Calculate C(n,k)
... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this impl... | 48 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 48 | 1 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase = logging.getLogger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = """masked_bert"""
def __init__( ... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 48 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(A ) , ... | 48 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase ... | 48 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase ... | 48 | 1 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 48 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCamelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_C... | 48 | 1 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 48 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =SwinConfig(image_size=192 )
... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase = 50 ):
"""simple docstring"""
__lowercase =[1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - t... | 48 |
'''simple docstring'''
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 com... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =[]
for i in range(len(_lowerCAmelCase ) - pat_len + 1 ):
__lowercase =True
... | 48 |
'''simple docstring'''
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 = logging.get_lo... | 48 | 1 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
lowerCamelCase = """
import os
"""
lowerCamelCase = """
def foo():
import os
return False
"""
lowerCamelCase = """
def foo():
def bar():
if True:
import os... | 48 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.Test... | 48 | 1 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance... | 48 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 48 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from trans... | 48 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =SwinConfig(image_size=192 )
... | 48 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCas... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVec... | 48 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Tensor... | 48 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data im... | 48 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if ... | 48 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 | 1 |
'''simple docstring'''
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
lowerCamelCase = version.parse(version... | 48 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transf... | 48 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in... | 48 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 | 1 |
'''simple docstring'''
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
fr... | 48 |
'''simple docstring'''
import functools
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase ,... | 48 | 1 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
lowerCamelCase = None
def _A ( ):
"""simple docstring"""
__lowercase... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
l... | 48 | 1 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 48 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCas... | 48 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 48 | 1 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCamelCase = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use val... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 48 | 1 |
'''simple docstring'''
import requests
lowerCamelCase = """""" # <-- Put your OpenWeatherMap appid here!
lowerCamelCase = """https://api.openweathermap.org/data/2.5/"""
def _A ( _lowerCAmelCase = "Chicago" , _lowerCAmelCase = APPID ):
"""simple docstring"""
... | 48 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase ... | 48 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : List[str]... | 48 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=A ):
'''simple docstring'''
lowerCAmelCase__ = ["""torch""", """scipy"""]
def __init__( self : Union[str, Any] , *_lowerCAmelCa... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_C... | 48 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = """▁"""
l... | 48 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =SwinConfig(image_size=192 )
... | 48 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 48 |
'''simple docstring'''
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 com... | 48 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 48 |
'''simple docstring'''
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 = logging.get_lo... | 48 | 1 |
'''simple docstring'''
# 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... | 48 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.Test... | 48 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_ava... | 48 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance... | 48 | 1 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data im... | 48 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from trans... | 48 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCas... | 48 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import ... | 48 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVec... | 48 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
lowerCamelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCamelCase = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase = [2, 4, 1, 5]
lowerCame... | 48 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data im... | 48 | 1 |
'''simple docstring'''
lowerCamelCase = 6_5521
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =1
__lowercase =0
for plain_chr in plain_text:
__lowercase =(a + ord(_lowerCAmelCase )) % MOD_ADLER
__l... | 48 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 | 1 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=1_024 , _lowerCAmelCase=1_024 ... | 48 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-43... | 48 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 | 1 |
'''simple docstring'''
lowerCamelCase = {str(digit): digit**5 for digit in range(10)}
def _A ( _lowerCAmelCase ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCAmelCase ) )
def _A ( ):
"""simple docstring""... | 48 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 |
'''simple docstring'''
import functools
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase ,... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _A ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' )
print('| Input 1 | Input... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
l... | 48 | 1 |
'''simple docstring'''
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... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 48 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase ... | 48 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRET... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 48 | 1 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCamelCase = logging.get_logger(__name... | 48 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowercase =1
for n in range(m + 1 ):
for k i... | 48 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase ... | 48 | 1 |
'''simple docstring'''
import math
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if (
not isinstance(_lowerCAmelCase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ... | 48 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _A ( _lowerCAmelCase ):
"""simple docstring"""
return np.dot(_lowerCAmelCase , _lowerCAmelCase )
class _UpperCamelCase :
... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_C... | 48 | 1 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
__lowercase =np.exp(outputs - maxes )
... | 48 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =SwinConfig(image_size=192 )
... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 ):
"""simple docstring"""
__lowercase =right or len(_lowerCAmelCase ) - 1
if left > right:
return -1
elif list_dat... | 48 |
'''simple docstring'''
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 com... | 48 | 1 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring... | 48 |
'''simple docstring'''
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 = logging.get_lo... | 48 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue... | 48 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.Test... | 48 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accele... | 48 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance... | 48 | 1 |
'''simple docstring'''
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 = logging.get_lo... | 48 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from trans... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
while a != 0:
__lowercase , __lowercase =b % a, a
return b
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docs... | 48 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCas... | 48 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as ... | 48 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVec... | 48 | 1 |
'''simple docstring'''
import argparse
import os
import re
lowerCamelCase = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCamelCase = re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z... | 48 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data im... | 48 | 1 |
'''simple docstring'''
import argparse
import os
import re
lowerCamelCase = """src/transformers"""
# Pattern that looks at the indentation in a line.
lowerCamelCase = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase = re.compile(R"""^\s*\"([^... | 48 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(_lowerCAmelCase ).json()
... | 48 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffus... | 48 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_availabl... | 48 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Args:
predictions ... | 48 | 1 |
'''simple docstring'''
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 ... | 48 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 | 1 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
lowerCamelCase = {
... | 48 |
'''simple docstring'''
import functools
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase ,... | 48 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
l... | 48 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''... | 48 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 48 | 1 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 48 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _UpperCamelCase ( A )... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 48 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[0 for i in range(r + 1 )]
# nc0 = 1
__lowercase =1
for i in range(1 , n + 1 ):
# to compute current row from pr... | 48 |
'''simple docstring'''
from math import factorial
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__lowercase =n // 2
return int(facto... | 48 | 1 |
'''simple docstring'''
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Tuple , _lowerCAmelCase : list):
'''simple docstring'''
__lowercase =set_counts
__lowercase =max(_lowerCAmelCase)
... | 48 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase ... | 48 | 1 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline... | 48 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 48 | 1 |
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