






"""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


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

if is_tf_available():


			class _lowerCAmelCase						(    tf.keras.Model       ):







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







									def 			A      (    self   ,				lowerCAmelCase_							)		->							Dict:
													_SCREAMING_SNAKE_CASE   :		str       		=    self.tokenizer(lowerCAmelCase_							)
													_SCREAMING_SNAKE_CASE   :		int       		=    self.bert(**lowerCAmelCase_							)
													return out["pooler_output"]


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







						def 			A      (    self							)		->							List[str]:
										super().setUp()

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [
										    BertTokenizer.from_pretrained(lowerCAmelCase_							) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
										]  # repeat for when fast_bert_tokenizer=false
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [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   :		Optional[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   :		Union[str, Any]       		=    list(zip(self.test_sentences   ,				self.test_sentences[::-1]							)							)







						def 			A      (    self							)		->							Optional[Any]:
										for tokenizer, tf_tokenizer in zip(self.tokenizers   ,				self.tf_tokenizers							):
														for test_inputs in (self.test_sentences, self.paired_sentences):
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    tokenizer(lowerCAmelCase_   ,				return_tensors='tf'   ,				padding='longest'							)
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    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 			A      (    self							)		->							List[Any]:
										for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE   :		str       		=    tf_tokenizer(self.paired_sentences							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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]:
										for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE   :		int       		=    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   :		str       		=    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 			A      (    self							)		->							Any:
										for tf_tokenizer in self.tf_tokenizers:
														_SCREAMING_SNAKE_CASE   :		int       		=    ModelToSave(tokenizer=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		str       		=    tf.convert_to_tensor(self.test_sentences							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(lowerCAmelCase_							)  # Build model with some sample inputs
														with TemporaryDirectory() as tempdir:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    Path(lowerCAmelCase_							) / 'saved.model'
																		model.save(lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.keras.models.load_model(lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Any       		=    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"""
lowerCAmelCase__    						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowerCAmelCase__    						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__    						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

