


"""simple docstring"""




from __future__ import annotations





def 			a_   (  lowercase__   :list[int]    ):
			if not list_of_ints:
						return []

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

			__lowerCamelCase        =			max(len(bin(lowercase__    )[2:]    ) for x in list_of_ints    )
			return _msd_radix_sort(lowercase__, lowercase__    )





def 			a_   (  lowercase__   :list[int], lowercase__   :int    ):
			if bit_position == 0 or len(lowercase__    ) in [0, 1]:
						return list_of_ints

			__lowerCamelCase        =			[]
			__lowerCamelCase        =			[]
			# 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(lowercase__    )
						else:
									# number has a zero at bit bit_position
									zeros.append(lowercase__    )

    # recursively split both lists further
			__lowerCamelCase        =			_msd_radix_sort(lowercase__, bit_position - 1    )
			__lowerCamelCase        =			_msd_radix_sort(lowercase__, bit_position - 1    )

			# recombine lists
			__lowerCamelCase        =			zeros
			res.extend(lowercase__    )

			return res





def 			a_   (  lowercase__   :list[int]    ):
			__lowerCamelCase        =			len(lowercase__    )
			if not list_of_ints or length == 1:
						return

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

			__lowerCamelCase        =			max(len(bin(lowercase__    )[2:]    ) for x in list_of_ints    )
			_msd_radix_sort_inplace(lowercase__, lowercase__, 0, lowercase__    )





def 			a_   (  lowercase__   :list[int], lowercase__   :int, lowercase__   :int, lowercase__   :int    ):
			if bit_position == 0 or end_index - begin_index <= 1:
						return

			bit_position -= 1

			__lowerCamelCase        =			begin_index
			__lowerCamelCase        =			end_index - 1
			while i <= j:
						__lowerCamelCase        =			False
						if not (list_of_ints[i] >> bit_position) & 1:
									# found zero at the beginning
									i += 1
									__lowerCamelCase        =			True
						if (list_of_ints[j] >> bit_position) & 1:
									# found one at the end
									j -= 1
									__lowerCamelCase        =			True

						if changed:
									continue

						__lowerCamelCase    ,__lowerCamelCase        =			list_of_ints[j], list_of_ints[i]
						j -= 1
						if j != i:
									i += 1

			_msd_radix_sort_inplace(lowercase__, lowercase__, lowercase__, lowercase__    )
			_msd_radix_sort_inplace(lowercase__, lowercase__, lowercase__, lowercase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()








"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__	:   Optional[Any]          =							{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   List[Any]          =							[
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   Tuple          =							[
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




