

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

if is_tf_available():



						class   __lowercase    (			tf.keras.Model     ):




									"""simple docstring"""







									def __init__(			self  :					int       ,						lowerCAmelCase__  :					int):
															super().__init__()
															SCREAMING_SNAKE_CASE_:			str  		=	tokenizer
															SCREAMING_SNAKE_CASE_:			str  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:			Any  		=	TFAutoModel.from_config(lowerCAmelCase__)




									def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					List[str]):
															SCREAMING_SNAKE_CASE_:			Dict  		=	self.tokenizer(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:			Any  		=	self.bert(**lowerCAmelCase__)
															return out["pooler_output"]



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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									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_:			Tuple  		=	[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_:			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_:			Optional[int]  		=	list(zip(self.test_sentences       ,						self.test_sentences[::-1]))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									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(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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									for tf_tokenizer in self.tf_tokenizers:
															SCREAMING_SNAKE_CASE_:			int  		=	tf_tokenizer(self.paired_sentences)
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									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_:			int  		=	tf.constant(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Any  		=	compiled_tokenizer(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf_tokenizer(lowerCAmelCase__)

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




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									for tf_tokenizer in self.tf_tokenizers:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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_:			Union[str, Any]  		=	Path(lowerCAmelCase__) / "saved.model"
																					model.save(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	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)









def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):

						if density <= 0:
												raise ValueError("Impossible fluid density"   )
						if bulk_modulus <= 0:
												raise ValueError("Impossible bulk modulus"   )

						return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()






