

import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






		"""simple docstring"""


		a     =      PhobertTokenizer
		a     =      False






		def    lowercase_	(							self		:	str      )	->							List[str]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							SCREAMING_SNAKE_CASE__					=					['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
							SCREAMING_SNAKE_CASE__					=					dict(zip(__lowerCamelCase			,     range(len(__lowerCamelCase      )      )      )      )
							SCREAMING_SNAKE_CASE__					=					['''#version: 0.2''', '''l à</w>''']
							SCREAMING_SNAKE_CASE__					=					{'''unk_token''': '''<unk>'''}

							SCREAMING_SNAKE_CASE__					=					os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''vocab_file''']      )
							SCREAMING_SNAKE_CASE__					=					os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['''merges_file''']      )

							with open(self.vocab_file			,     '''w'''			,     encoding='''utf-8'''      ) as fp:
												for token in vocab_tokens:
																	fp.write(f'''{token} {vocab_tokens[token]}\n'''      )
							with open(self.merges_file			,     '''w'''			,     encoding='''utf-8'''      ) as fp:
												fp.write('''\n'''.join(__lowerCamelCase      )      )






		def    lowercase_	(							self		:	Tuple			,     **__lowerCamelCase		:	Any      )	->							int:
							kwargs.update(self.special_tokens_map      )
							return PhobertTokenizer.from_pretrained(self.tmpdirname			,     **__lowerCamelCase      )






		def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	str      )	->							str:
							SCREAMING_SNAKE_CASE__					=					'''Tôi là VinAI Research'''
							SCREAMING_SNAKE_CASE__					=					'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
							return input_text, output_text







		def    lowercase_	(							self		:	Optional[Any]      )	->							int:
							SCREAMING_SNAKE_CASE__					=					PhobertTokenizer(self.vocab_file			,     self.merges_file			,     **self.special_tokens_map      )
							SCREAMING_SNAKE_CASE__					=					'''Tôi là VinAI Research'''
							SCREAMING_SNAKE_CASE__					=					'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
							SCREAMING_SNAKE_CASE__					=					tokenizer.tokenize(__lowerCamelCase      )
							print(__lowerCamelCase      )
							self.assertListEqual(__lowerCamelCase			,     __lowerCamelCase      )

							SCREAMING_SNAKE_CASE__					=					tokens + [tokenizer.unk_token]

							SCREAMING_SNAKE_CASE__					=					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase      )			,     __lowerCamelCase      )





import doctest
from collections import deque

import numpy as np






class  UpperCAmelCase__	:






  """simple docstring"""


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







  def    lowercase_	(							self		:	List[str]      )	->							list[float]:
       SCREAMING_SNAKE_CASE__					=					len(self.first_signal      )
       SCREAMING_SNAKE_CASE__					=					len(self.second_signal      )

       SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     __lowerCamelCase      )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__					=					[[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      ):
            SCREAMING_SNAKE_CASE__					=					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
       SCREAMING_SNAKE_CASE__					=					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()


