


from typing import TYPE_CHECKING

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


__UpperCamelCase    		=       {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    '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:
						__UpperCamelCase    		=       [
						    '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:
						__UpperCamelCase    		=       [
						    '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

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






def        UpperCamelCase_(				_A   :List[str]				,     _A   :Tuple				,     _A   :Any				,     _A   :Tuple				,     _A   :Optional[int]				,     _A   :str  )->		List[str]:
			if index == r:
						for j in range(_A  ):
									print(data[j]				,     end=" "  )
						print(" "  )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			UpperCamelCase__					=       arr[i]
			combination_util(_A				,     _A				,     _A				,     index + 1				,     _A				,     i + 1  )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(_A				,     _A				,     _A				,     _A				,     _A				,     i + 1  )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()
def        UpperCamelCase_(				_A   :Dict				,     _A   :Optional[Any]				,     _A   :Union[str, Any]  )->		Optional[int]:
			# A temporary array to store all combination one by one
			UpperCamelCase__					=       [0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(_A				,     _A				,     _A				,     0				,     _A				,     0  )


if __name__ == "__main__":
						# Driver code to check the function above
						__UpperCamelCase    		=       [1_0, 2_0, 3_0, 4_0, 5_0]
						print_combination(arr, len(arr), 3)
						# This code is contributed by Ambuj sahu


