
from typing import TYPE_CHECKING

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


_lowerCamelCase :    Optional[int]  							= {
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    Optional[Any]  							= [
		    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TapasForMaskedLM',
		    'TapasForQuestionAnswering',
		    'TapasForSequenceClassification',
		    'TapasModel',
		    'TapasPreTrainedModel',
		    'load_tf_weights_in_tapas',
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    Dict  							= [
		    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFTapasForMaskedLM',
		    'TFTapasForQuestionAnswering',
		    'TFTapasForSequenceClassification',
		    'TFTapasModel',
		    'TFTapasPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[int]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    int:

							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
														raise ValueError("String lengths must match!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						0

							for chara, chara in zip(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														if chara != chara:
																					count += 1

							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()






