


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


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

if is_tf_available():




			class    UpperCAmelCase					(       tf.keras.Model    ):




						def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		Tuple						):







									"""simple docstring"""







									super().__init__()
									UpperCamelCase         =					tokenizer
									UpperCamelCase         =					AutoConfig.from_pretrained(__magic_name__						)
									UpperCamelCase         =					TFAutoModel.from_config(__magic_name__						)





						def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Dict						):







									"""simple docstring"""







									UpperCamelCase         =					self.tokenizer(__magic_name__						)
									UpperCamelCase         =					self.bert(**__magic_name__						)
									return out["pooler_output"]




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




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						super().setUp()

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

						UpperCamelCase         =					[
						    """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""",
						]
						UpperCamelCase         =					list(zip(self.test_sentences       ,       self.test_sentences[::-1]						)						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						for tokenizer, tf_tokenizer in zip(self.tokenizers       ,       self.tf_tokenizers						):
									for test_inputs in (self.test_sentences, self.paired_sentences):
												UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""tf"""       ,       padding="""longest"""						)
												UpperCamelCase         =					tf_tokenizer(__magic_name__						)

												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							:		int						):







						"""simple docstring"""







						for tf_tokenizer in self.tf_tokenizers:
									UpperCamelCase         =					tf_tokenizer(self.paired_sentences						)
									UpperCamelCase         =					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							:		List[str]						):







						"""simple docstring"""







						for tf_tokenizer in self.tf_tokenizers:
									UpperCamelCase         =					tf.function(__magic_name__						)
									for test_inputs in (self.test_sentences, self.paired_sentences):
												UpperCamelCase         =					tf.constant(__magic_name__						)
												UpperCamelCase         =					compiled_tokenizer(__magic_name__						)
												UpperCamelCase         =					tf_tokenizer(__magic_name__						)

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





			@slow
			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						for tf_tokenizer in self.tf_tokenizers:
									UpperCamelCase         =					ModelToSave(tokenizer=__magic_name__						)
									UpperCamelCase         =					tf.convert_to_tensor(self.test_sentences						)
									UpperCamelCase         =					model(__magic_name__						)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												UpperCamelCase         =					Path(__magic_name__						) / """saved.model"""
												model.save(__magic_name__						)
												UpperCamelCase         =					tf.keras.models.load_model(__magic_name__						)
									UpperCamelCase         =					loaded_model(__magic_name__						)
									# 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						)








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 100	):





			"""simple docstring"""




			UpperCamelCase         =					sum(i * i for i in range(1       ,  n + 1	)	)
			UpperCamelCase         =					int(math.pow(sum(range(1       ,  n + 1	)	)       ,  2	)	)
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
			print(F'''{solution() = }''')




