

from typing import TYPE_CHECKING

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


a_						     =    {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'AdaptiveEmbedding',
			    'TransfoXLForSequenceClassification',
			    'TransfoXLLMHeadModel',
			    'TransfoXLModel',
			    'TransfoXLPreTrainedModel',
			    'load_tf_weights_in_transfo_xl',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFAdaptiveEmbedding',
			    'TFTransfoXLForSequenceClassification',
			    'TFTransfoXLLMHeadModel',
			    'TFTransfoXLMainLayer',
			    'TFTransfoXLModel',
			    'TFTransfoXLPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
			from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_transfo_xl import (
						    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    AdaptiveEmbedding,
						    TransfoXLForSequenceClassification,
						    TransfoXLLMHeadModel,
						    TransfoXLModel,
						    TransfoXLPreTrainedModel,
						    load_tf_weights_in_transfo_xl,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_transfo_xl import (
						    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAdaptiveEmbedding,
						    TFTransfoXLForSequenceClassification,
						    TFTransfoXLLMHeadModel,
						    TFTransfoXLMainLayer,
						    TFTransfoXLModel,
						    TFTransfoXLPreTrainedModel,
						)

else:
			import sys

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


class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return index | (index + 1)
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return (index & (index + 1)) - 1
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       value
				while index < self.size:
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_prev(a	) + 1
					if current_left_border == index:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       value
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       max(a      ,      a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_next(a	)






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int      ,      a							: int	)							->       int:

				"""simple docstring"""

				right -= 1  # Because of right is exclusive
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0
				while left <= right:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.get_prev(a	)
					if left <= current_left:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       max(a      ,      self.tree[right]	)
						SCREAMING_SNAKE_CASE   :      str				     =       current_left
					else:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       max(a      ,      self.arr[right]	)
						right -= 1
				return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()