





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__)










def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	if not grid or not grid[0]:
		raise TypeError('''The grid does not contain the appropriate information'''							)

	for cell_n in range(1 ,len(grid[0]							)							):
		grid[0][cell_n] += grid[0][cell_n - 1]
	lowerCamelCase__              =	grid[0]

	for row_n in range(1 ,len(__snake_case							)							):
		lowerCamelCase__              =	grid[row_n]
		lowerCamelCase__              =	fill_row(__snake_case ,__snake_case							)
		lowerCamelCase__              =	grid[row_n]

	return grid[-1][-1]





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   list:







	'''simple docstring'''







	current_row[0] += row_above[0]
	for cell_n in range(1 ,len(__snake_case							)							):
		current_row[cell_n] += min(current_row[cell_n - 1] ,row_above[cell_n]							)

	return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



