





from typing import TYPE_CHECKING

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


_a						=   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   ["RoFormerTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "RoFormerForCausalLM",
			    "RoFormerForMaskedLM",
			    "RoFormerForMultipleChoice",
			    "RoFormerForQuestionAnswering",
			    "RoFormerForSequenceClassification",
			    "RoFormerForTokenClassification",
			    "RoFormerLayer",
			    "RoFormerModel",
			    "RoFormerPreTrainedModel",
			    "load_tf_weights_in_roformer",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFRoFormerForCausalLM",
			    "TFRoFormerForMaskedLM",
			    "TFRoFormerForMultipleChoice",
			    "TFRoFormerForQuestionAnswering",
			    "TFRoFormerForSequenceClassification",
			    "TFRoFormerForTokenClassification",
			    "TFRoFormerLayer",
			    "TFRoFormerModel",
			    "TFRoFormerPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FlaxRoFormerForMaskedLM",
			    "FlaxRoFormerForMultipleChoice",
			    "FlaxRoFormerForQuestionAnswering",
			    "FlaxRoFormerForSequenceClassification",
			    "FlaxRoFormerForTokenClassification",
			    "FlaxRoFormerModel",
			    "FlaxRoFormerPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


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()



