


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


lowerCAmelCase__			:      Dict								=['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase__			:      Optional[int]								='hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







				class 	__lowercase    (tf.keras.Model  ):





											"""simple docstring"""







											def __init__(      self			,						lowerCAmelCase__       ):





														"""simple docstring"""




														super().__init__()
														SCREAMING_SNAKE_CASE_		:     Any					=					tokenizer
														SCREAMING_SNAKE_CASE_		:     Tuple					=					AutoConfig.from_pretrained(lowerCAmelCase__       )
														SCREAMING_SNAKE_CASE_		:     Dict					=					TFAutoModel.from_config(lowerCAmelCase__       )






											def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





														"""simple docstring"""




														SCREAMING_SNAKE_CASE_		:     Any					=					self.tokenizer(lowerCAmelCase__       )
														SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.bert(**lowerCAmelCase__       )
														return out["pooler_output"]







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().setUp()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
										    BertTokenizer.from_pretrained(lowerCAmelCase__       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
										]  # repeat for when fast_bert_tokenizer=false
										SCREAMING_SNAKE_CASE_		:     str					=					[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       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					[
										    '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',
										]
										SCREAMING_SNAKE_CASE_		:     int					=					list(zip(self.test_sentences			,						self.test_sentences[::-1]       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tokenizer, tf_tokenizer in zip(self.tokenizers			,						self.tf_tokenizers       ):
													for test_inputs in (self.test_sentences, self.paired_sentences):
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer(lowerCAmelCase__			,						return_tensors='tf'			,						padding='longest'       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
													SCREAMING_SNAKE_CASE_		:     str					=					tf_tokenizer(self.paired_sentences       )
													SCREAMING_SNAKE_CASE_		:     Tuple					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
													SCREAMING_SNAKE_CASE_		:     Any					=					tf.function(lowerCAmelCase__       )
													for test_inputs in (self.test_sentences, self.paired_sentences):
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tf.constant(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					compiled_tokenizer(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tf_tokenizer(lowerCAmelCase__       )

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






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					ModelToSave(tokenizer=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Any					=					tf.convert_to_tensor(self.test_sentences       )
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(lowerCAmelCase__       )  # Build model with some sample inputs
													with TemporaryDirectory() as tempdir:
																SCREAMING_SNAKE_CASE_		:     Dict					=					Path(lowerCAmelCase__       ) / 'saved.model'
																model.save(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Any					=					tf.keras.models.load_model(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					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       )









from __future__ import annotations






def 						a__			(				A__,      A__ = None,      A__ = None				):
			if start is None:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					0

			if end is None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(A__				) - 1

			if start >= end:
						return

			SCREAMING_SNAKE_CASE_		:     Tuple					=					(start + end) // 2

			slowsort(A__,      A__,      A__				)
			slowsort(A__,      mid + 1,      A__				)

			if sequence[end] < sequence[mid]:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sequence[mid], sequence[end]

			slowsort(A__,      A__,      end - 1				)


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

				testmod()





