



from typing import TYPE_CHECKING

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


_lowerCAmelCase	:   Optional[Any]    =   {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Any    =   ["RobertaTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Union[str, Any]    =   [
	    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "RobertaForCausalLM",
	    "RobertaForMaskedLM",
	    "RobertaForMultipleChoice",
	    "RobertaForQuestionAnswering",
	    "RobertaForSequenceClassification",
	    "RobertaForTokenClassification",
	    "RobertaModel",
	    "RobertaPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[Any]    =   [
	    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFRobertaForCausalLM",
	    "TFRobertaForMaskedLM",
	    "TFRobertaForMultipleChoice",
	    "TFRobertaForQuestionAnswering",
	    "TFRobertaForSequenceClassification",
	    "TFRobertaForTokenClassification",
	    "TFRobertaMainLayer",
	    "TFRobertaModel",
	    "TFRobertaPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   [
	    "FlaxRobertaForCausalLM",
	    "FlaxRobertaForMaskedLM",
	    "FlaxRobertaForMultipleChoice",
	    "FlaxRobertaForQuestionAnswering",
	    "FlaxRobertaForSequenceClassification",
	    "FlaxRobertaForTokenClassification",
	    "FlaxRobertaModel",
	    "FlaxRobertaPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Union[str, Any]    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import random
from typing import Any







def     UpperCAmelCase_		(					snake_case__					)	->     list[Any]:



				"""simple docstring"""





				for _ in range(len(snake_case__					)					):
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  data[b], data[a]
				return data


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   [0, 1, 2, 3, 4, 5, 6, 7]
	_lowerCAmelCase	:   Optional[Any]    =   ["python", "says", "hello", "!"]
	print("Fisher-Yates Shuffle:")
	print("List", integers, strings)
	print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
