



'''simple docstring'''
# Lint as: python3
import itertools
import os
import re


snake_case_			:     Union[str, Any]  	=  re.compile(r'([A-Z]+)([A-Z][a-z])')
snake_case_			:     Union[str, Any]  	=  re.compile(r'([a-z\d])([A-Z])')

snake_case_			:     str  	=  re.compile(r'(?<!_)_(?!_)')
snake_case_			:     Any  	=  re.compile(r'(_{2,})')

snake_case_			:     Any  	=  r'^\w+(\.\w+)*$'

snake_case_			:     Union[str, Any]  	=  r'<>:/\|?*'






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       _uppercase_uppercase_re.sub(R'\1_\2'  ,  UpperCAmelCase_							)
						_UpperCamelCase :		Optional[int]			    =       _lowercase_uppercase_re.sub(R'\1_\2'  ,  UpperCAmelCase_							)
						return name.lower()






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		int			    =       _single_underscore_re.split(UpperCAmelCase_							)
						_UpperCamelCase :		Tuple			    =       [_multiple_underscores_re.split(UpperCAmelCase_							) for n in name]
						return "".join(n.capitalize() for n in itertools.chain.from_iterable(UpperCAmelCase_							) if n != ''							)






def A__   (					UpperCAmelCase_							):
						if os.path.basename(UpperCAmelCase_							) != name:
												raise ValueError(f'Should be a dataset name, not a path: {name}'							)
						return camelcase_to_snakecase(UpperCAmelCase_							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						if os.path.basename(UpperCAmelCase_							) != name:
												raise ValueError(f'Should be a dataset name, not a path: {name}'							)
						if not re.match(_split_re  ,  UpperCAmelCase_							):
												raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'							)
						return f'{filename_prefix_for_name(UpperCAmelCase_							)}-{split}'






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None							):
						_UpperCamelCase :		Optional[int]			    =       filename_prefix_for_split(UpperCAmelCase_  ,  UpperCAmelCase_							)
						if filetype_suffix:
												prefix += f'.{filetype_suffix}'
						_UpperCamelCase :		List[Any]			    =       os.path.join(UpperCAmelCase_  ,  UpperCAmelCase_							)
						return f'{filepath}*'






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None							):
						_UpperCamelCase :		Optional[int]			    =       filename_prefix_for_split(UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase :		Tuple			    =       os.path.join(UpperCAmelCase_  ,  UpperCAmelCase_							)

						if shard_lengths:
												_UpperCamelCase :		Union[str, Any]			    =       len(UpperCAmelCase_							)
												_UpperCamelCase :		Any			    =       [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(UpperCAmelCase_							)]
												if filetype_suffix:
																		_UpperCamelCase :		int			    =       [filename + f'.{filetype_suffix}' for filename in filenames]
												return filenames
						else:
												_UpperCamelCase :		Any			    =       prefix
												if filetype_suffix:
																		filename += f'.{filetype_suffix}'
												return [filename]







'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


