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 |
|---|---|---|---|---|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMI... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 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
fr... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_ten... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ):
raise TypeError('Input value must be a \'int\' type' ... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
snake_case : ... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [0] * len(UpperCAmelCase__ )
for i in range(1 ,len(UpperCAmelCase__ ) ):
# use last results for better performance - dynamic programming
_SCREAMING... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowercase :
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = None
def __magic_name__ ( self )-> ... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : str = logging.get_logger(__name__)
... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def SCREAMI... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
snake_case : Optional[Any] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
i... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case : str = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_available(... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__=1024 ,UpperCAmelCase__=1024 ,UpperCAmelCase__=False ,**U... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
import numpy as np
from PIL import Image
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = np.array(UpperCAmelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('Th... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = len(A_ )
_SCREAMING_SNAKE_CASE = [0] * len_array
if len_array > 0:
_SCREAMING_SNAKE_CASE = array[0]... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
from collections import deque
class __lowercase :
"""simple docstring"""
def __init__( self , A_ , A_ , A_ )-> None:
_SCREAMING_SNAKE_CASE = process_name # process name
_SCREAMING_SNAKE_CASE = arrival_time # arrival ti... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
# If row is equal to the size of the b... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
for param in module.parameters():
_SCREAMING_SNAKE_CASE = False
def SCREAMING_SNAKE_CASE ( ):
... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCA... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 1 |
import gc
import threading
import time
import psutil
import torch
class __lowercase :
"""simple docstring"""
def __init__( self )-> Union[str, Any]:
_SCREAMING_SNAKE_CASE = psutil.Process()
_SCREAMING_SNAKE_CASE = False
... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) != 32:
raise ValueError('Input must be of length 32' )
_SCREAMING_SNAKE_CASE = B''
for ... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
snake_case ... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
from datetime import datetime as dt
import os
from github import Github
snake_case : Union[str, Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def SCREAMING_SNAKE_CASE ( ):
... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
import os
from datetime import datetime as dt
from github import Github
snake_case : str = [
'good first issue',
'feature request',
'wip',
]
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Github(os.environ... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
snake_case : str = logging.get_logger(__name__)
# TODO: upload to AWS
snake_case : List[str] = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if index == r:
for j in range(UpperCAmelCase__ ):
print(data[j] ,end=' ' )
print('... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
snake_case : int = logging.get_logger(__name__)
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *A_ , ... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : str = {
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
'p... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ["image_processor", "tokenizer"]
SCREAM... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__=0 ):
"""simple docstring"""
... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
return quad(UpperCAmelCase__ ,0 ,UpperCAmelCase__ ,args=(Upper... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
import os
from distutils.util import strtobool
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
for e in env_keys:
_SCREAMING_SNAKE_CASE = int(os.environ.get(UpperCAmelCase__ ,-1 ) )
if val >= 0:
... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common im... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
from __future__ import annotations
from collections.abc import Callable
snake_case : Optional[Any] = list[list[float | int]]
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(UpperCAmel... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 9
_SCREAMING_SNAKE_CASE = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , A_ , A_=None ... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / de... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case : Tuple = ['small', 'medium', 'large']
snake_case : Optional[int] = 'lm_head.decoder.weight'
snake_case : Optional[Any] = 'lm_head.weight'
def SCREAMING_SN... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 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_pro... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def SCREAMI... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_met... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
f... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
from math import ceil
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = list(range(0 ,UpperCAmelCase__ ) )
_SCREAMING_SNAKE_CASE = [item for sublist in list(device_map.values() ) for item ... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
f... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
from PIL import Image
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
def brightness(UpperCAmelCase__ ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level ... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : str = logging.get_logger(__name__)
snake_case : Tuple = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_avai... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 2
while i * i <= n:
_SCREAMING_SNAKE_CASE = 0
while n % i == 0:
n //= i
multiplicity += 1
... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : str = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
return "".join(sorted(UpperCAmelCase__ ) )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> Any:
# we need a list not a string, so do something to change the type
_SCREAMING_SNAKE_CASE = arr.split(',' )
def __magic_name__ ( self ... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def SCREAMI... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : Any = logging.get_logger(__name__)
snake_case : Opti... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
class __lowercase :
"""simple docstring"""
def __init__( self )-> Optional[int]:
_SCREAMING_SNAKE_CASE = {}
def __magic_name__ ( self )-> None:
print(self.vertex )
for i in self.vertex:
... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseMod... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, Ne... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
snake_case : int = logging.get_logger(__nam... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case : List[str] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_torch_avail... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassif... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
snake_case : Any = datasets.logging.get_logger(__name__)
snake_case : Tuple = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n ... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
for i in range(1 ,1001 ):
total += i**i
return str(UpperCAmelCase__ )[-10:]
if __name__ == "__main__":
print(solution())
| 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
snake_case : Union[str, Any] ... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = 0.0 ,UpperCAmelCase__ = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if num <= 0:
_SCREAMING_SNAKE_CASE = f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(UpperCAmelCase__... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import to... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
def __magic_name__ ( self , A_ )-> float:
... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
snake_case : List[Any] = logging.getLogger(__name__)
class __lowercase :
"""simple docstring"""
... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
_SCREAMING_SNAKE_CASE = sum(UpperCAmelCase__ ) / len(UpperCAmelCase__ ) # Calculate the av... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
'repo_name': ['test_repo1', 'test_repo2', '... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
import colorsys
from PIL import Image # type: ignore
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = x
_SCREAMING_SNAKE_CASE = y
for step in range(UpperCAmelCase__ ): ... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, Patchi... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
snake_case : str = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_ca... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
snake_case : Optional[Any] = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
from __future__ import annotations
import time
snake_case : List[str] = list[tuple[int, int]]
snake_case : int = [
[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, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
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