


'''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


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

if is_tf_available():




  class 	a__       (							tf.keras.Model						):





         '''simple docstring'''






         def __init__(	self      ,					lowerCamelCase_     ) ->							Dict:
                super().__init__()
                lowerCAmelCase__      =			tokenizer
                lowerCAmelCase__      =			AutoConfig.from_pretrained(_lowerCAmelCase     )
                lowerCAmelCase__      =			TFAutoModel.from_config(_lowerCAmelCase     )







         def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
                lowerCAmelCase__      =			self.tokenizer(_lowerCAmelCase     )
                lowerCAmelCase__      =			self.bert(**_lowerCAmelCase     )
                return out["pooler_output"]




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





       '''simple docstring'''






       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
              super().setUp()

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

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







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
              for tokenizer, tf_tokenizer in zip(self.tokenizers      ,					self.tf_tokenizers     ):
                     for test_inputs in (self.test_sentences, self.paired_sentences):
                            lowerCAmelCase__      =			tokenizer(_lowerCAmelCase      ,					return_tensors='''tf'''      ,					padding='''longest'''     )
                            lowerCAmelCase__      =			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     ) ->							List[str]:
              for tf_tokenizer in self.tf_tokenizers:
                     lowerCAmelCase__      =			tf_tokenizer(self.paired_sentences     )
                     lowerCAmelCase__      =			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:
                     lowerCAmelCase__      =			tf.function(_lowerCAmelCase     )
                     for test_inputs in (self.test_sentences, self.paired_sentences):
                            lowerCAmelCase__      =			tf.constant(_lowerCAmelCase     )
                            lowerCAmelCase__      =			compiled_tokenizer(_lowerCAmelCase     )
                            lowerCAmelCase__      =			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     ) ->							List[Any]:
              for tf_tokenizer in self.tf_tokenizers:
                     lowerCAmelCase__      =			ModelToSave(tokenizer=_lowerCAmelCase     )
                     lowerCAmelCase__      =			tf.convert_to_tensor(self.test_sentences     )
                     lowerCAmelCase__      =			model(_lowerCAmelCase     )  # Build model with some sample inputs
                     with TemporaryDirectory() as tempdir:
                            lowerCAmelCase__      =			Path(_lowerCAmelCase     ) / '''saved.model'''
                            model.save(_lowerCAmelCase     )
                            lowerCAmelCase__      =			tf.keras.models.load_model(_lowerCAmelCase     )
                     lowerCAmelCase__      =			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'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()