



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    :		str		      =    ["""bert-base-uncased""", """bert-base-cased"""]
_lowerCamelCase    :		List[Any]		      =    """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():


			class 						lowercase    (							tf.keras.Model):
									'''simple docstring'''


									def __init__(  self    :				Dict       ,	snake_case    :				Union[str, Any]	):



														'''simple docstring'''


														super().__init__()
														SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
														SCREAMING_SNAKE_CASE							:     Tuple						=				AutoConfig.from_pretrained(snake_case	)
														SCREAMING_SNAKE_CASE							:     Any						=				TFAutoModel.from_config(snake_case	)







									def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



														'''simple docstring'''


														SCREAMING_SNAKE_CASE							:     Tuple						=				self.tokenizer(snake_case	)
														SCREAMING_SNAKE_CASE							:     Dict						=				self.bert(**snake_case	)
														return out["pooler_output"]


@require_tf
@require_tensorflow_text
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											super().setUp()

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[
											    BertTokenizer.from_pretrained(snake_case	) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											SCREAMING_SNAKE_CASE							:     int						=				[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	)

											SCREAMING_SNAKE_CASE							:     Any						=				[
											    '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							:     Dict						=				list(zip(self.test_sentences       ,	self.test_sentences[::-1]	)	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''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							:     Any						=				tokenizer(snake_case       ,	return_tensors='tf'       ,	padding='longest'	)
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				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       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											for tf_tokenizer in self.tf_tokenizers:
																SCREAMING_SNAKE_CASE							:     Dict						=				tf_tokenizer(self.paired_sentences	)
																SCREAMING_SNAKE_CASE							:     List[str]						=				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       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											for tf_tokenizer in self.tf_tokenizers:
																SCREAMING_SNAKE_CASE							:     Tuple						=				tf.function(snake_case	)
																for test_inputs in (self.test_sentences, self.paired_sentences):
																					SCREAMING_SNAKE_CASE							:     Dict						=				tf.constant(snake_case	)
																					SCREAMING_SNAKE_CASE							:     str						=				compiled_tokenizer(snake_case	)
																					SCREAMING_SNAKE_CASE							:     Tuple						=				tf_tokenizer(snake_case	)

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







						@slow
						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


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




from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]