



"""simple docstring"""







from __future__ import annotations

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       list[int]   ):
						"""simple docstring"""






						if not list_of_ints:
												return []

						if min(lowerCamelCase_   ) < 0:
												raise ValueError("All numbers must be positive"   )

						snake_case      : Optional[Any]							=   max(len(bin(lowerCamelCase_   )[2:]   ) for x in list_of_ints   )
						return _msd_radix_sort(lowerCamelCase_    ,		lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       list[int]    ,		lowerCamelCase_:       int   ):
						"""simple docstring"""






						if bit_position == 0 or len(lowerCamelCase_   ) in [0, 1]:
												return list_of_ints

						snake_case      : Any							=   []
						snake_case      : Tuple							=   []
						# Split numbers based on bit at bit_position from the right
						for number in list_of_ints:
												if (number >> (bit_position - 1)) & 1:
																		# number has a one at bit bit_position
																		ones.append(lowerCamelCase_   )
												else:
																		# number has a zero at bit bit_position
																		zeros.append(lowerCamelCase_   )

    # recursively split both lists further
						snake_case      : Any							=   _msd_radix_sort(lowerCamelCase_    ,		bit_position - 1   )
						snake_case      : str							=   _msd_radix_sort(lowerCamelCase_    ,		bit_position - 1   )

						# recombine lists
						snake_case      : Tuple							=   zeros
						res.extend(lowerCamelCase_   )

						return res

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       list[int]   ):
						"""simple docstring"""






						snake_case      : Tuple							=   len(lowerCamelCase_   )
						if not list_of_ints or length == 1:
												return

						if min(lowerCamelCase_   ) < 0:
												raise ValueError("All numbers must be positive"   )

						snake_case      : Optional[int]							=   max(len(bin(lowerCamelCase_   )[2:]   ) for x in list_of_ints   )
						_msd_radix_sort_inplace(lowerCamelCase_    ,		lowerCamelCase_    ,		0    ,		lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       list[int]    ,		lowerCamelCase_:       int    ,		lowerCamelCase_:       int    ,		lowerCamelCase_:       int   ):
						"""simple docstring"""






						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

						snake_case      : str							=   begin_index
						snake_case      : Union[str, Any]							=   end_index - 1
						while i <= j:
												snake_case      : Any							=   False
												if not (list_of_ints[i] >> bit_position) & 1:
																		# found zero at the beginning
																		i += 1
																		snake_case      : Optional[Any]							=   True
												if (list_of_ints[j] >> bit_position) & 1:
																		# found one at the end
																		j -= 1
																		snake_case      : List[Any]							=   True

												if changed:
																		continue

												snake_case						, snake_case      : List[str]							=   list_of_ints[j], list_of_ints[i]
												j -= 1
												if j != i:
																		i += 1

						_msd_radix_sort_inplace(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_   )
						_msd_radix_sort_inplace(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()








"""simple docstring"""







from typing import TYPE_CHECKING

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


A    =	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CamembertForCausalLM',
			    'CamembertForMaskedLM',
			    'CamembertForMultipleChoice',
			    'CamembertForQuestionAnswering',
			    'CamembertForSequenceClassification',
			    'CamembertForTokenClassification',
			    'CamembertModel',
			    'CamembertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFCamembertForCausalLM',
			    'TFCamembertForMaskedLM',
			    'TFCamembertForMultipleChoice',
			    'TFCamembertForQuestionAnswering',
			    'TFCamembertForSequenceClassification',
			    'TFCamembertForTokenClassification',
			    'TFCamembertModel',
			    'TFCamembertPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert import CamembertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert_fast import CamembertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_camembert import (
						    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CamembertForCausalLM,
						    CamembertForMaskedLM,
						    CamembertForMultipleChoice,
						    CamembertForQuestionAnswering,
						    CamembertForSequenceClassification,
						    CamembertForTokenClassification,
						    CamembertModel,
						    CamembertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_camembert import (
						    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCamembertForCausalLM,
						    TFCamembertForMaskedLM,
						    TFCamembertForMultipleChoice,
						    TFCamembertForQuestionAnswering,
						    TFCamembertForSequenceClassification,
						    TFCamembertForTokenClassification,
						    TFCamembertModel,
						    TFCamembertPreTrainedModel,
						)

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



