code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from PIL import Image
def __UpperCAmelCase ( lowerCamelCase_ : Image ) -> Image:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] = image.size
SCREAMING_SNAKE_CASE_ : str = 0
SCREAMING_SNAKE_CAS... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : int = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''f... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils... | 105 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int , lowerCamelCase_ : int = 0 , lowerCamelCase_ : int = 0 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = right or len(lowerCamelCas... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
from __future__ import annotations
import math
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negat... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
import inspect
import unittest
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case ( self ):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def snake_case ( self ):
import diffusers
... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 |
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 (
Fla... | 105 | 1 |
from ....utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,snake_case__ ,snake_case__=None ,snake_case__=2048 ):
SCREAMING_SNAKE_CASE_ : int =... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 7 , lowerCamelCase_ : int = 1_00_00_00 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ : Any = 1
f... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mode... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase_... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : list ) -> list:
"""simple docstring"""
if len(lowerCamelCase_ ) <= 1:
return [tuple(lowerCamelCase_ )]
SCREAMING_SNAKE_CASE_ : Optional[int] = []
def generate(lowerCamelCase_ : int , low... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 9
SCREAMING_SNAKE_CASE_ : List[str] = [
[0, 1, 4],
[0, 7, 8... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,snake_case__ ,snake_case__ = None ... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCamelCase__ : List[Any] = '''src/transformers'''
UpperCamelCase__ : ... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
if isinstance(snake_case__ ,snake_case__ ):
... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
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 (
Fla... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] , lowerCamelCase_ : Any ) -> Any:
"""simple docstring"""
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(lowerCamelCase_ ):
... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_uti... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCamelCase__ : Optional[int] = logging.get_logger(__name_... | 105 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = {'''vocab_file''': '''vocab.json'''}
UpperCamelCase__ :... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Optional[Any] = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : str = field(default="languag... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impo... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 |
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 (
Fla... | 105 | 1 |
import math
def __UpperCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tuple = int(math.flo... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : Union[str, Any] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokeniza... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ : Union[str, Any] = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if ... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[int] ) -> int:
"""simple docstring"""
if not nums:
return 0
SCREAMING_SNAKE_CASE_ : List[str] = nums[0]
SCREAMING_SNAKE_CASE_ : Any = 0
for ... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
# 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/licenses/LICENSE-2.0
#
# Un... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
UpperCamelCase__ : Dict = logging.get_logger(__name__)
Upper... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Inform... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = n
SCREAMING_SNAKE_CASE_ : Optional[int] = [None] * self.n
SCREAMING_SNAKE_CASE_ : List[str] = 0 # index of the first element
... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
fro... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormer... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __UpperCAmelCase ( lowerCamelCase_ : Any , lowerCamelCase_ : Tuple ) -> Tuple:
"""simple do... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
class lowerCAmelCase_ :
def __init__( self ,snake_case__ = "" ,snake_case__ = False ):
# Mapping from the first character of the prefix of the node
SCREAMING_SNAKE_CASE_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its w... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int = 0 ) -> list:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = length or len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase__ : List[str] = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive... | 105 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
from __future__ import annotations
from typing import Any
def __UpperCAmelCase ( lowerCamelCase_ : list ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
SCREAMING_SNAKE_CASE_ : List[Any] = {'+', '-', '*', '/'}
SCREAMI... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
from __future__ import annotations
import math
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE_ : Union[st... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_par... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
import argparse
import json
from tqdm import tqdm
def __UpperCAmelCase ( ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake_c... | 105 |
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 (
Fla... | 105 | 1 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCamelCase__ : int = None
try:
import msvcrt
except ImportError:
UpperCamelCase__ : List[Any] = None
try:
import fcntl
except ImportError:
UpperCamelC... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
UpperCamelCase__ : Any = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : Union[str, Any] = "M-CLIP"
def __init__( self ,snake_case__=1024 ,snake_case__=768 ,**snake_case__ )... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''S... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
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 lowerCAmelCase_ ( lowerCamelCase_ , unittest.Test... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
from __future__ import annotations
UpperCamelCase__ : Tuple = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
import math
from collections.abc import Callable
def __UpperCAmelCase ( lowerCamelCase_ : Callable[[float], float] , lowerCamelCase_ : float , lowerCamelCase_ : float ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : float ... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
import math
import tensorflow as tf
from packaging import version
def __UpperCAmelCase ( lowerCamelCase_ : Any ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = tf.convert_to_tensor(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Any... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def __UpperCAmelCase ( lowerCamelCase_ : int = 1_00_00_00 , lowerCamelCase_ : int = 10 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : defaultdict = defaultdict(lowe... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
import torch
from torch import nn
class lowerCAmelCase_ ( nn.Module ):
def __init__( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__=1 ,snake_case__=False ):
super().__init__()
SCREAMING_SNAKE_CASE_ : A... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/ni... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impor... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __UpperCAmelCase ( lowerCamelCase_ : int = 8 ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''google/umt5-small''': '''https://huggingfa... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUM... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : List[str] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all valu... | 105 |
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 (
Fla... | 105 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
from math import factorial, radians
def __UpperCAmelCase ( lowerCamelCase_ : float , lowerCamelCase_ : int = 18 , lowerCamelCase_ : int = 10 ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = angle_in_degrees - (... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfo... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase__ : Union[str, Any] ... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Dict = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENC... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, requ... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(lowerCamelCase_ : int , lowerCamelCase_ : int ) -> int:
... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int = 10_00 ) -> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
from math import factorial
def __UpperCAmelCase ( lowerCamelCase_ : int = 1_00 ) -> int:
"""simple docstring"""
return sum(int(lowerCamelCase_ ) for x in str(factorial(lowerCamelCase_ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : tuple[int, int] , lowerCamelCase_ : int ) -> list[tuple[int, int]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = position
... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : list[int] ) -> list[list[int]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = []
if len(lowerCamelCase_ ) == 1:
return [nums.copy()]
for _ in range(len(lowerCamelCase_ ) ):
SCREAMING_SN... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
UpperCamelCase__ : Any = datasets.load_iris()
UpperCamelCase__ : Any = np.array(data['''data'''])
UpperCamelCase__ : List[Any] = np.array(da... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
from math import factorial
def __UpperCAmelCase ( lowerCamelCase_ : int = 20 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
SCREAMI... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
from __future__ import annotations
UpperCamelCase__ : str = list[list[int]]
# assigning initial values to the grid
UpperCamelCase__ : 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],
[9, 0,... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .token... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ : Tuple = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase__ : Dict = _LazyModule(__nam... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''... | 105 |
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 (
Fla... | 105 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : str = ... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCAmelCase ( lowerCamelCase_ : Callable[[int | float], int | float] , lowerCamelCase_ : int | float , lowerCamelCase_ : int | float , lowerCamelCase_ : int = 1_... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedK... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
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