



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







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							bool:

		if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
				UpperCAmelCase_						=				f'''Expected string as input, found {type(__SCREAMING_SNAKE_CASE ).__name__}'''
				raise TypeError(__SCREAMING_SNAKE_CASE )

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

		try:
				UpperCAmelCase_						=				int(spanish_id_clean[0:8] )
				UpperCAmelCase_						=				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()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

