




'''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     :  Dict       ,		__lowerCAmelCase     :  Optional[Any]      ):
								super().__init__()
								__snake_case						 =   tokenizer
								__snake_case						 =   AutoConfig.from_pretrained(__lowerCAmelCase      )
								__snake_case						 =   TFAutoModel.from_config(__lowerCAmelCase      )




		def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Any      ):
								__snake_case						 =   self.tokenizer(__lowerCAmelCase      )
								__snake_case						 =   self.bert(**__lowerCAmelCase      )
								return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  List[Any]      ):
							super().setUp()

							__snake_case						 =   [
							    BertTokenizer.from_pretrained(__lowerCAmelCase      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
							]  # repeat for when fast_bert_tokenizer=false
							__snake_case						 =   [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      )

							__snake_case						 =   [
							    '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',
							]
							__snake_case						 =   list(zip(self.test_sentences       ,		self.test_sentences[::-1]      )      )




	def   lowercase__   (       self     :  Any      ):
							for tokenizer, tf_tokenizer in zip(self.tokenizers       ,		self.tf_tokenizers      ):
													for test_inputs in (self.test_sentences, self.paired_sentences):
																			__snake_case						 =   tokenizer(__lowerCAmelCase       ,		return_tensors='tf'       ,		padding='longest'      )
																			__snake_case						 =   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   lowercase__   (       self     :  str      ):
							for tf_tokenizer in self.tf_tokenizers:
													__snake_case						 =   tf_tokenizer(self.paired_sentences      )
													__snake_case						 =   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   lowercase__   (       self     :  List[str]      ):
							for tf_tokenizer in self.tf_tokenizers:
													__snake_case						 =   tf.function(__lowerCAmelCase      )
													for test_inputs in (self.test_sentences, self.paired_sentences):
																			__snake_case						 =   tf.constant(__lowerCAmelCase      )
																			__snake_case						 =   compiled_tokenizer(__lowerCAmelCase      )
																			__snake_case						 =   tf_tokenizer(__lowerCAmelCase      )

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




	@slow
	def   lowercase__   (       self     :  Tuple      ):
							for tf_tokenizer in self.tf_tokenizers:
													__snake_case						 =   ModelToSave(tokenizer=__lowerCAmelCase      )
													__snake_case						 =   tf.convert_to_tensor(self.test_sentences      )
													__snake_case						 =   model(__lowerCAmelCase      )  # Build model with some sample inputs
													with TemporaryDirectory() as tempdir:
																			__snake_case						 =   Path(__lowerCAmelCase      ) / 'saved.model'
																			model.save(__lowerCAmelCase      )
																			__snake_case						 =   tf.keras.models.load_model(__lowerCAmelCase      )
													__snake_case						 =   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'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

