


"""simple docstring"""
from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
				from ..ta.tokenization_ta import TaTokenizer
else:
				from ...utils.dummy_sentencepiece_objects import TaTokenizer

__SCREAMING_SNAKE_CASE							:    Dict		=    TaTokenizer

if is_tokenizers_available():
				from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
				from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__SCREAMING_SNAKE_CASE							:    str		=    TaTokenizerFast

__SCREAMING_SNAKE_CASE							:    List[str]		=    {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Dict		=    [
				    'MT5EncoderModel',
				    'MT5ForConditionalGeneration',
				    'MT5ForQuestionAnswering',
				    'MT5Model',
				    'MT5PreTrainedModel',
				    'MT5Stack',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
				from .configuration_mta import MTaConfig, MTaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mta import (
								    MTaEncoderModel,
								    MTaForConditionalGeneration,
								    MTaForQuestionAnswering,
								    MTaModel,
								    MTaPreTrainedModel,
								    MTaStack,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    List[str]		=    _LazyModule(
				    __name__,
				    globals()['__file__'],
				    _import_structure,
				    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
				    module_spec=__spec__,
				)







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



