

lowerCAmelCase__								=		"Input must be a string of 8 numbers plus letter"
lowerCAmelCase__								=		"TRWAGMYFPDXBNJZSQVHLCKE"






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				Optional[Any]:




	'''simple docstring'''




	if not isinstance(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE	):
		_UpperCamelCase   :		Optional[int]			=						F'''Expected string as input, found {type(_SCREAMING_SNAKE_CASE	).__name__}'''
		raise TypeError(_SCREAMING_SNAKE_CASE	)

	_UpperCamelCase   :		Tuple			=						spanish_id.replace('-'							,       ''	).upper()
	if len(_SCREAMING_SNAKE_CASE	) != 9:
		raise ValueError(_SCREAMING_SNAKE_CASE	)

	try:
		_UpperCamelCase   :		Optional[int]			=						int(spanish_id_clean[0:8]	)
		_UpperCamelCase   :		List[Any]			=						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 % 2_3]


if __name__ == "__main__":
							import doctest

							doctest.testmod()







from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




