






"""simple docstring"""

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


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

if is_tf_available():

			class 						A_							(      tf.keras.Model  ):







							def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple	)				->					List[Any]:
													super().__init__()
													__magic_name__            =     tokenizer
													__magic_name__            =     AutoConfig.from_pretrained(__lowerCamelCase	)
													__magic_name__            =     TFAutoModel.from_config(__lowerCamelCase	)







							def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Dict	)				->					List[Any]:
													__magic_name__            =     self.tokenizer(__lowerCamelCase	)
													__magic_name__            =     self.bert(**__lowerCamelCase	)
													return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[Any]:
										super().setUp()

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

										__magic_name__            =     [
										    "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",
										]
										__magic_name__            =     list(zip(self.test_sentences						,    self.test_sentences[::-1]	)	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[int]:

										for tokenizer, tf_tokenizer in zip(self.tokenizers						,    self.tf_tokenizers	):
																for test_inputs in (self.test_sentences, self.paired_sentences):
																						__magic_name__            =     tokenizer(__lowerCamelCase						,    return_tensors="tf"						,    padding="longest"	)
																						__magic_name__            =     tf_tokenizer(__lowerCamelCase	)

																						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     _snake_case					(							self							:						int	)				->					Tuple:

										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     tf_tokenizer(self.paired_sentences	)
																__magic_name__            =     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     _snake_case					(							self							:						List[str]	)				->					Dict:

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

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







				@slow
				def     _snake_case					(							self							:						Dict	)				->					str:

										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     ModelToSave(tokenizer=__lowerCamelCase	)
																__magic_name__            =     tf.convert_to_tensor(self.test_sentences	)
																__magic_name__            =     model(__lowerCamelCase	)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__magic_name__            =     Path(__lowerCamelCase	) / "saved.model"
																						model.save(__lowerCamelCase	)
																						__magic_name__            =     tf.keras.models.load_model(__lowerCamelCase	)
																__magic_name__            =     loaded_model(__lowerCamelCase	)
																# 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	)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






						return "".join(chr(ord(__lowerCamelCase   ) - 3_2   ) if "a" <= char <= "z" else char for char in word   )


if __name__ == "__main__":
			from doctest import testmod

			testmod()



