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 |
|---|---|---|---|---|
'''simple docstring'''
def lowerCamelCase__ ( A_ = 200 ):
UpperCAmelCase_ = [1, 2, 5, 10, 20, 50, 100, 200]
UpperCAmelCase_ = [0] * (pence + 1)
UpperCAmelCase_ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A_ , ... | 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case ... | 660 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common... | 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sen... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__snake_case : Union[str, Any] = {
'''configuration_layoutlmv3''': ... | 660 | '''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Construct mode... | 660 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Any = logging.get_logger(__name__)
__snake_case : List[Any] = {
'''facebook/encodec_24khz''': '''https://hug... | 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key"... | 660 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Any = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class lowercase_ (... | 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
if len(A_ ) <= 1:
return [tuple(A_ )]
UpperCAmelCase_ = []
def generate(A_ , A_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , A_ )
for ... | 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 660 | 1 |
'''simple docstring'''
__snake_case : Optional[Any] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
... | 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
if num < 0:
return False
UpperCAmelCase_ = num
UpperCAmelCase_ = 0
while num > 0:
UpperCAmelCase_ = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main... | 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import... | 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availabl... | 660 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : List[str] = {
'''google/umt5-small''': ... | 660 | '''simple docstring'''
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, prepare_image_inputs
if is_torch_availab... | 660 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCamelCase__ ( A_ ):
return x + 2
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ... | 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCA... | 660 | 1 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__snake_case : List[str] = logging.get_logger(__na... | 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ... | 660 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class lowercase_ :
def __init__( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = {}
def lowerCamel... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_C... | 660 | 1 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
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_configurat... | 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ ... | 660 | 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-2.0
#
#... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_A... | 660 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available,... | 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = len(A_ )
# We need to create solution object to save path.
UpperCAmelCase_ = [[0 for _ in range(A_ )] for _ in range(A_ )]
UpperCAmelCase_ ... | 660 | '''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__snake_case : int = logging.get_logger(__name__... | 660 | 1 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case : str = logging.get_logger(__name__)
__snake_case ... | 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ ... | 660 | 1 |
'''simple docstring'''
from math import isqrt
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , A_ , A_ ):
UpperCAmelC... | 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] ... | 660 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impo... | 660 | '''simple docstring'''
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... | 660 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case ... | 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dic... | 660 | 1 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__snake_case : Optional[Any] = False
class lowercase_ (... | 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block... | 660 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_sco... | 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case ... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_A... | 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sen... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case : List[str] = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
... | 660 | '''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Construct mode... | 660 | 1 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, r... | 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key"... | 660 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowercase_ ( _A , _A ):
@register_to_config
def __init__( self ,... | 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 660 | 1 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__snake_case : Dict = logging.getLogger(__name__)
if is_torch_tpu_a... | 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : List[Any] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''toke... | 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__snake_case : Optional[int] = logging.get... | 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availabl... | 660 | 1 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, t... | 660 | '''simple docstring'''
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, prepare_image_inputs
if is_torch_availab... | 660 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .toke... | 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCA... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
__snake_case : str = list[list[int]]
# assigning initial values to the grid
__snake_case : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
... | 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = [0] * len(A_ )
UpperCAmelCase_ = []
UpperCAmelCase_ = [1] * len(A_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(A_ ... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_C... | 660 | 1 |
'''simple docstring'''
from manim import *
class lowercase_ ( _A ):
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ = Rectan... | 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ ... | 660 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_A... | 660 | 1 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__snake_case : List[Any] = collections.namedtuple('... | 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCamelCase__ ( A_ , A_ , A_ ):
if not arr:
return None, None, 0
if low == high:
return low, ... | 660 | '''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__snake_case : int = logging.get_logger(__name__... | 660 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block... | 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ ... | 660 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils i... | 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] ... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCamelCase__ ( A_ , A_=0 ):
return sorted(A_ , key=lambda A_ : x[column] )
def lowerCamelCase__ ( ... | 660 | '''simple docstring'''
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... | 660 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dic... | 660 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.... | 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
... | 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case ... | 660 | 1 |
'''simple docstring'''
import requests
__snake_case : Dict = '''YOUR API KEY'''
def lowerCamelCase__ ( A_ , A_ = giphy_api_key ):
UpperCAmelCase_ = "+".join(query.split() )
UpperCAmelCase_ = F"""https://api.giphy.com/v1/gifs/search?q={... | 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sen... | 660 | 1 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase__ ( A_ ):
def wrapper(*A_ , **A_ ):
UpperCAmelCase_ = timeit.default_timer()
... | 660 | '''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Construct mode... | 660 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational i... | 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key"... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class lowercase_ :
def __init__( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = []
self.adlist.append(
{"val... | 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 660 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 660 | 1 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Tuple = logging.get_logger(__name__)
__snak... | 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Dict = {'''configuration_xlnet''': ['''XLNET_PRET... | 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case : Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiT... | 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availabl... | 660 | 1 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import t... | 660 | '''simple docstring'''
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, prepare_image_inputs
if is_torch_availab... | 660 | 1 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCA... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra... | 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ... | 660 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__snake_case : str = '''\
'''
__snake_case : str = '''
Perplexity (PPL) is one of the most ... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_C... | 660 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ ... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = len(A_ )
for i in range(1 , A_ ):
UpperCAmelCase_ = collection[i]
UpperCAmelCase_ = 0
UpperCAmelCase_ = i - 1
while low <= high:
... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_A... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__snake_case : Optional[Any] = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
def __init__( self , UpperCamelCase__ , UpperC... | 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize... | 660 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
__snake_case : List[str] = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, ... | 660 | '''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__snake_case : int = logging.get_logger(__name__... | 660 | 1 |
'''simple docstring'''
import numpy as np
__snake_case : List[Any] = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''',... | 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ ... | 660 | 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 ..auto import CONFIG_MAPPING
__snake_case : str = logging.get_logger(_... | 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] ... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( A_ ):
if len(A_ ) == 0:
return array
UpperCAmelCase_ , UpperCAmelCase_ = min(A_ ), max(A_ )
# Compute the variables
UpperCAmelCase_ = _max - _min + ... | 660 | '''simple docstring'''
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... | 660 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transfor... | 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dic... | 660 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simpl... | 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block... | 660 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( _A , unittest.TestCase )... | 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case ... | 660 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowercase_ ( unittest.TestCase , _A ):
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ ... | 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sen... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCamelCase__ ( A_ , A_ , A_ = 10**-10 ):
UpperCAmelCase_ = a
while True:
UpperCAmelCase_ = ... | 660 | '''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Construct mode... | 660 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] ... | 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key"... | 660 | 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
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case ... | 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 660 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : List[Any] = {
'''k... | 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__snake_case : Tuple = 0
__snake_case : Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0,... | 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | 1 |
'''simple docstring'''
import argparse
import copy
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = {}
with open(A_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
UpperCAmelCase_ = []
_list.append([line.split()[1], line.split... | 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 660 | 1 |
'''simple docstring'''
import qiskit
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase_ = qiskit.QuantumCircuit(A_ , A_ ... | 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availabl... | 660 | 1 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ ( A_ ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def lowerCamelCase__ ... | 660 | '''simple docstring'''
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, prepare_image_inputs
if is_torch_availab... | 660 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=0.2 ... | 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCA... | 660 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def lowerCamelCase__ ( A_ ):
if not isinstance(A_ , A_ ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(A_ )
if is_prime(A_ ) and is_prime(n... | 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(A_ , A_ ):
raise TypeError("Input value must be a 'int' type" )
return bin(A_ ).count("1" )
if __name__ == "__main__... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_C... | 660 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Any = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """timm_backbone"""
def __init__( self , UpperCamelCase__=None , U... | 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ ... | 660 | 1 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__snake_case : List[Any] = re.compile(R'''^(?P<major>\d+)''' R'''\.(?P<minor>\d+)''' R'''\.(?P<patch>\d+)$''')
@total_ordering
@... | 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_A... | 660 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, ... | 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize... | 660 | 1 |
'''simple docstring'''
from string import ascii_uppercase
__snake_case : List[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__snake_case : List[str] = dict(enumerate(ascii_uppercase))
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = ... | 660 | '''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__snake_case : int = logging.get_logger(__name__... | 660 | 1 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__snake_case : str = {
'''facebook/maskformer-swin-base-... | 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ ... | 660 | 1 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__snake_case : str = logging.get_logger(__name__)
class l... | 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] ... | 660 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Construct mode... | 660 | '''simple docstring'''
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... | 660 | 1 |
'''simple docstring'''
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 TensorType
class lowerca... | 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dic... | 660 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokeniz... | 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block... | 660 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__snake_case : List[Any] = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Syste... | 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case ... | 660 | 1 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mba... | 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sen... | 660 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__snake_case : str = object()
# For specifying empty leaf dict `{}`
__snake_case : List[str] = ob... | 660 | '''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Construct mode... | 660 | 1 |
'''simple docstring'''
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_i... | 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key"... | 660 | 1 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, Sin... | 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 660 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__snake_case : Tuple = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Le... | 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_C... | 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ ):
# load base model
UpperCAmelCase_ = StableDiffusi... | 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class ... | 660 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( _A ):
a_ = (EulerDiscreteScheduler,)
a_ = 10
def lowerCamelCase_ ( s... | 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availabl... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ ... | 660 | '''simple docstring'''
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, prepare_image_inputs
if is_torch_availab... | 660 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dic... | 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCA... | 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
assert (
isinstance(A_ , A_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
UpperCAmelCase_ , UpperCAmelCase_ ... | 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ... | 660 | 1 |
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