






'''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


_A	:			Union[str, Any]         =['''bert-base-uncased''', '''bert-base-cased''']
_A	:			int         ='''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():

			class _lowercase	(				tf.keras.Model		):




										def __init__(							self:				Optional[int]			,							UpperCamelCase__:				List[Any]     ):
																	super().__init__()
																	lowerCamelCase__					:	Optional[int]         =       tokenizer
																	lowerCamelCase__					:	Dict         =       AutoConfig.from_pretrained(UpperCamelCase__     )
																	lowerCamelCase__					:	Tuple         =       TFAutoModel.from_config(UpperCamelCase__     )




										def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				Optional[Any]     ):
																	lowerCamelCase__					:	Any         =       self.tokenizer(UpperCamelCase__     )
																	lowerCamelCase__					:	Union[str, Any]         =       self.bert(**UpperCamelCase__     )
																	return out["pooler_output"]

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




							def 		lowerCamelCase_				(							self:				Any     ):
														super().setUp()

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

														lowerCamelCase__					:	Tuple         =       [
														    """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""",
														]
														lowerCamelCase__					:	Optional[int]         =       list(zip(self.test_sentences			,							self.test_sentences[::-1]     )     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														for tokenizer, tf_tokenizer in zip(self.tokenizers			,							self.tf_tokenizers     ):
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												lowerCamelCase__					:	List[Any]         =       tokenizer(UpperCamelCase__			,							return_tensors="""tf"""			,							padding="""longest"""     )
																												lowerCamelCase__					:	List[str]         =       tf_tokenizer(UpperCamelCase__     )

																												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:				List[Any]     ):
														for tf_tokenizer in self.tf_tokenizers:
																					lowerCamelCase__					:	int         =       tf_tokenizer(self.paired_sentences     )
																					lowerCamelCase__					:	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 		lowerCamelCase_				(							self:				Any     ):
														for tf_tokenizer in self.tf_tokenizers:
																					lowerCamelCase__					:	List[Any]         =       tf.function(UpperCamelCase__     )
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												lowerCamelCase__					:	Optional[int]         =       tf.constant(UpperCamelCase__     )
																												lowerCamelCase__					:	List[str]         =       compiled_tokenizer(UpperCamelCase__     )
																												lowerCamelCase__					:	Optional[int]         =       tf_tokenizer(UpperCamelCase__     )

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




							@slow
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														for tf_tokenizer in self.tf_tokenizers:
																					lowerCamelCase__					:	Dict         =       ModelToSave(tokenizer=UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       tf.convert_to_tensor(self.test_sentences     )
																					lowerCamelCase__					:	Any         =       model(UpperCamelCase__     )  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												lowerCamelCase__					:	List[Any]         =       Path(UpperCamelCase__     ) / """saved.model"""
																												model.save(UpperCamelCase__     )
																												lowerCamelCase__					:	List[Any]         =       tf.keras.models.load_model(UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       loaded_model(UpperCamelCase__     )
																					# 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     )





def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




