
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase       :				Union[str, Any]       =			{'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				int       =			['''ReformerTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				Dict       =			['''ReformerTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				Optional[Any]       =			[
			    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ReformerAttention''',
			    '''ReformerForMaskedLM''',
			    '''ReformerForQuestionAnswering''',
			    '''ReformerForSequenceClassification''',
			    '''ReformerLayer''',
			    '''ReformerModel''',
			    '''ReformerModelWithLMHead''',
			    '''ReformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_reformer import ReformerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_reformer_fast import ReformerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_reformer import (
						    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ReformerAttention,
						    ReformerForMaskedLM,
						    ReformerForQuestionAnswering,
						    ReformerForSequenceClassification,
						    ReformerLayer,
						    ReformerModel,
						    ReformerModelWithLMHead,
						    ReformerPreTrainedModel,
						)

else:
			import sys

			_lowerCamelCase       :				int       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

def        a_		(       __lowercase   :	str ) ->  int:
			_snake_case		=			hex_num.strip()
			if not hex_num:
						raise ValueError('No value was passed to the function' )

			_snake_case		=			hex_num[0] == '-'
			if is_negative:
						_snake_case		=			hex_num[1:]

			try:
						_snake_case		=			int(__lowercase       ,					16 )
			except ValueError:
						raise ValueError('Invalid value was passed to the function' )

			_snake_case		=			''
			while int_num > 0:
						_snake_case		=			str(int_num % 2 ) + bin_str
						int_num >>= 1

			return int(('-' + bin_str) if is_negative else bin_str )


if __name__ == "__main__":
			import doctest

			doctest.testmod()