



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


SCREAMING_SNAKE_CASE__   			=   ['''bert-base-uncased''', '''bert-base-cased''']
SCREAMING_SNAKE_CASE__   			=   '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():


							class   __lowerCAmelCase  (		tf.keras.Model   ):
												"""simple docstring"""






												def __init__(  self       :							str					,       _snake_case       :							Optional[int]       ):






															"""simple docstring"""


															super().__init__()
															A__	  =     tokenizer
															A__	  =     AutoConfig.from_pretrained(_snake_case       )
															A__	  =     TFAutoModel.from_config(_snake_case       )



												def      _a				(  self       :							Optional[int]					,       _snake_case       :							Optional[int]       ):






															"""simple docstring"""


															A__	  =     self.tokenizer(_snake_case       )
															A__	  =     self.bert(**_snake_case       )
															return out["pooler_output"]


@require_tf
@require_tensorflow_text
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								super().setUp()

								A__	  =     [
								    BertTokenizer.from_pretrained(_snake_case       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								A__	  =     [TFBertTokenizer.from_pretrained(_snake_case       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(_snake_case					,       use_fast_bert_tokenizer=_snake_case       )
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

								A__	  =     [
								    'This is a straightforward English test sentence.',
								    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
								    'Now we\'re going to add some Chinese: 一 二 三 一二三',
								    'And some much more rare Chinese: 齉 堃 齉堃',
								    'Je vais aussi écrire en français pour tester les accents',
								    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
								]
								A__	  =     list(zip(self.test_sentences					,       self.test_sentences[::-1]       )       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								for tokenizer, tf_tokenizer in zip(self.tokenizers					,       self.tf_tokenizers       ):
											for test_inputs in (self.test_sentences, self.paired_sentences):
														A__	  =     tokenizer(_snake_case					,       return_tensors='tf'					,       padding='longest'       )
														A__	  =     tf_tokenizer(_snake_case       )

														for key in python_outputs.keys():
																	self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
																	self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,       tf.intaa       ) == tf_outputs[key]       )       )



					@slow
					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								for tf_tokenizer in self.tf_tokenizers:
											A__	  =     tf_tokenizer(self.paired_sentences       )
											A__	  =     tf_tokenizer(
											    text=[sentence[0] for sentence in self.paired_sentences]					,       text_pair=[sentence[1] for sentence in self.paired_sentences]					,       )
											for key in merged_outputs.keys():
														self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,       tf.intaa       ) == separated_outputs[key]       )       )



					@slow
					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								for tf_tokenizer in self.tf_tokenizers:
											A__	  =     tf.function(_snake_case       )
											for test_inputs in (self.test_sentences, self.paired_sentences):
														A__	  =     tf.constant(_snake_case       )
														A__	  =     compiled_tokenizer(_snake_case       )
														A__	  =     tf_tokenizer(_snake_case       )

														for key in eager_outputs.keys():
																	self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )



					@slow
					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								for tf_tokenizer in self.tf_tokenizers:
											A__	  =     ModelToSave(tokenizer=_snake_case       )
											A__	  =     tf.convert_to_tensor(self.test_sentences       )
											A__	  =     model(_snake_case       )  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
														A__	  =     Path(_snake_case       ) / 'saved.model'
														model.save(_snake_case       )
														A__	  =     tf.keras.models.load_model(_snake_case       )
											A__	  =     loaded_model(_snake_case       )
											# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
											self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )					,       1E-5       )












UpperCAmelCase__      			=						'''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase__      			=						'''TRWAGMYFPDXBNJZSQVHLCKE'''







def     a_   (__A      )				->						bool:



					"""simple docstring"""

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

					__a					:						int      =					spanish_id.replace("-"				,       ""      ).upper()
					if len(__A      ) != 9:
										raise ValueError(__A      )

					try:
										__a					:						Tuple      =					int(spanish_id_clean[0:8]      )
										__a					:						Optional[int]      =					spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__A      ) from ex

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

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





