


'''simple docstring'''






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
  from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
  __UpperCAmelCase					       =							get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__UpperCAmelCase					       =							128_022
__UpperCAmelCase					       =							128_028




@require_sentencepiece
class 	a__       (							lowerCAmelCase__       ,       unittest.TestCase						):





       '''simple docstring'''






       lowercase__    :     Dict					=   MaMaaaTokenizer
       lowercase__    :     List[str]					=   False
       lowercase__    :     int					=   False
       lowercase__    :     List[Any]					=   True







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

              lowerCAmelCase__      =			['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
              lowerCAmelCase__      =			dict(zip(_SCREAMING_SNAKE_CASE      ,					range(len(_SCREAMING_SNAKE_CASE     )     )     )     )
              lowerCAmelCase__      =			Path(self.tmpdirname     )
              save_json(_SCREAMING_SNAKE_CASE      ,					save_dir / VOCAB_FILES_NAMES['''vocab_file''']     )
              if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                     copyfile(_SCREAMING_SNAKE_CASE      ,					save_dir / VOCAB_FILES_NAMES['''spm_file''']     )

              lowerCAmelCase__      =			MaMaaaTokenizer.from_pretrained(self.tmpdirname     )
              tokenizer.save_pretrained(self.tmpdirname     )







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					**lowerCamelCase_     ) ->							Any:
              return MaMaaaTokenizer.from_pretrained(self.tmpdirname      ,					**_SCREAMING_SNAKE_CASE     )







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
              return (
                  "This is a test",
                  "This is a test",
              )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
              lowerCAmelCase__      =			'''</s>'''
              lowerCAmelCase__      =			0

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE     )      ,					_SCREAMING_SNAKE_CASE     )
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE     )      ,					_SCREAMING_SNAKE_CASE     )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
              lowerCAmelCase__      =			self.get_tokenizer()
              lowerCAmelCase__      =			list(tokenizer.get_vocab().keys()     )

              self.assertEqual(vocab_keys[0]      ,					'''</s>'''     )
              self.assertEqual(vocab_keys[1]      ,					'''<unk>'''     )
              self.assertEqual(vocab_keys[-1]      ,					'''<s>'''     )
              self.assertEqual(len(_SCREAMING_SNAKE_CASE     )      ,					tokenizer.vocab_size + len(tokenizer.get_added_vocab()     )     )







       @unittest.skip('''Skip this test while all models are still to be uploaded.'''     )
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
              pass







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
              lowerCAmelCase__      =			self.get_tokenizer()

              lowerCAmelCase__      =			tokenizer.tokenize('''This is a test'''     )
              self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE     )      ,					[2, 3, 4, 5, 6]      ,					)

              lowerCAmelCase__      =			tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]     )
              self.assertListEqual(_SCREAMING_SNAKE_CASE      ,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

              lowerCAmelCase__      =			tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE     )
              self.assertEqual(_SCREAMING_SNAKE_CASE      ,					'''This is a test'''     )







       @slow
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
              # fmt: off
              lowerCAmelCase__      =			{'''input_ids''': [[12_80_22, 11_01_08, 3_97, 11, 3_82_72, 22_47, 12_48_11, 2_85, 1_81_05, 15_86, 2_07, 7, 3_95_34, 44_28, 3_97, 10_19, 1_81_05, 15_86, 2_07, 7, 4_13_37, 1_67_86, 2_41, 7, 2_02_14, 17, 12_56_90, 1_03_98, 7, 4_43_78, 5_80_69, 6_83_42, 77_98, 73_43, 11, 2_99, 3_33_10, 4, 1_58, 3_73_50, 9_40_77, 45_69, 2_99, 3_33_10, 90, 4, 5_28_40, 2_90, 4, 3_12_70, 1_12, 2_99, 6_82, 4, 5_28_40, 3_99_53, 1_40_79, 1_93, 5_25_19, 9_08_94, 1_78_94, 12_06_97, 11, 4_04_45, 5_51, 17, 10_19, 5_25_19, 9_08_94, 1_77_56, 9_63, 11, 4_04_45, 4_80, 17, 97_92, 11_20, 51_73, 13_93, 62_40, 1_67_86, 2_41, 12_09_96, 28, 12_45, 13_93, 11_82_40, 1_11_23, 10_19, 9_36_12, 26_91, 1_06_18, 9_80_58, 12_04_09, 19_28, 2_79, 4, 4_06_83, 3_67, 1_78, 2_07, 10_19, 1_03, 10_31_21, 5_06, 6_52_96, 5, 2], [12_80_22, 2_12_17, 3_67, 1_17, 12_54_50, 1_28, 7_19, 7, 73_08, 40, 9_36_12, 1_26_69, 11_16, 1_67_04, 71, 1_77_85, 36_99, 1_55_92, 35, 1_44, 95_84, 2_41, 1_19_43, 7_13, 9_50, 7_99, 22_47, 8_84_27, 1_50, 1_49, 11_88_13, 12_07_06, 10_19, 10_69_06, 8_15_18, 28, 12_24, 2_27_99, 3_97, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_80_22, 16_58, 12_33_11, 51_55, 55_78, 47_22, 2_79, 1_49_47, 23_66, 11_20, 11_97, 14, 13_48, 92_32, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
              # fmt: on

              self.tokenizer_integration_test_util(
                  expected_encoding=_SCREAMING_SNAKE_CASE      ,					model_name='''facebook/m2m100_418M'''      ,					revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''      ,					)




@require_torch
@require_sentencepiece
@require_tokenizers
class 	a__       (							unittest.TestCase						):





       '''simple docstring'''






       lowercase__    :     Union[str, Any]					=   "facebook/m2m100_418M"
       lowercase__    :     Optional[Any]					=   [
           "In my opinion, there are two levels of response from the French government.",
           "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
       ]
       lowercase__    :     Optional[int]					=   [
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
       ]

       # fmt: off
       lowercase__    :     Union[str, Any]					=   [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]







       @classmethod
       def 						__SCREAMING_SNAKE_CASE							(	cls     ) ->							Union[str, Any]:
              lowerCAmelCase__      =			MaMaaaTokenizer.from_pretrained(
                  cls.checkpoint_name      ,					src_lang='''en'''      ,					tgt_lang='''fr'''     )
              lowerCAmelCase__      =			1
              return cls







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
              self.assertEqual(self.tokenizer.get_lang_id('''ar'''     )      ,					12_80_06     )
              self.assertEqual(self.tokenizer.get_lang_id('''en'''     )      ,					12_80_22     )
              self.assertEqual(self.tokenizer.get_lang_id('''ro'''     )      ,					12_80_76     )
              self.assertEqual(self.tokenizer.get_lang_id('''mr'''     )      ,					12_80_63     )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
              lowerCAmelCase__      =			self.tokenizer.get_vocab()
              self.assertEqual(len(_SCREAMING_SNAKE_CASE     )      ,					self.tokenizer.vocab_size     )
              self.assertEqual(vocab['''<unk>''']      ,					3     )
              self.assertIn(self.tokenizer.get_lang_token('''en'''     )      ,					_SCREAMING_SNAKE_CASE     )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
              lowerCAmelCase__      =			'''en'''
              lowerCAmelCase__      =			self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
              self.assertListEqual(self.expected_src_tokens      ,					_SCREAMING_SNAKE_CASE     )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
              self.assertIn(_SCREAMING_SNAKE_CASE      ,					self.tokenizer.all_special_ids     )
              # fmt: off
              lowerCAmelCase__      =			[FR_CODE, 53_64, 82, 86_42, 4, 2_94, 47, 8, 1_40_28, 1_36, 32_86, 97_06, 6, 9_07_97, 6, 14_40_12, 1_62, 8_81_28, 3_00_61, 5, 2]
              # fmt: on
              lowerCAmelCase__      =			self.tokenizer.decode(_SCREAMING_SNAKE_CASE      ,					skip_special_tokens=_SCREAMING_SNAKE_CASE     )
              lowerCAmelCase__      =			self.tokenizer.decode(generated_ids[1:]      ,					skip_special_tokens=_SCREAMING_SNAKE_CASE     )
              self.assertEqual(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE     )
              self.assertNotIn(self.tokenizer.eos_token      ,					_SCREAMING_SNAKE_CASE     )







       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
              lowerCAmelCase__      =			tempfile.mkdtemp()
              lowerCAmelCase__      =			self.tokenizer.lang_token_to_id
              self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE     )
              lowerCAmelCase__      =			MaMaaaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
              self.assertDictEqual(new_tok.lang_token_to_id      ,					_SCREAMING_SNAKE_CASE     )







       @require_torch
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
              lowerCAmelCase__      =			'''en'''
              lowerCAmelCase__      =			'''fr'''

              lowerCAmelCase__      =			self.tokenizer(self.src_text      ,					text_target=self.tgt_text      ,					padding=_SCREAMING_SNAKE_CASE      ,					return_tensors='''pt'''     )

              lowerCAmelCase__      =			shift_tokens_right(
                  batch['''labels''']      ,					self.tokenizer.pad_token_id      ,					self.tokenizer.eos_token_id     )

              for k in batch:
                     lowerCAmelCase__      =			batch[k].tolist()
              # batch = {k: v.tolist() for k,v in batch.items()}
              # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
              # batch.decoder_inputs_ids[0][0] ==
              assert batch.input_ids[1][0] == EN_CODE
              assert batch.input_ids[1][-1] == 2
              assert batch.labels[1][0] == FR_CODE
              assert batch.labels[1][-1] == 2
              assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







       @require_torch
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
              lowerCAmelCase__      =			'''mr'''
              self.assertListEqual(self.tokenizer.prefix_tokens      ,					[self.tokenizer.get_lang_id('''mr'''     )]     )
              self.assertListEqual(self.tokenizer.suffix_tokens      ,					[self.tokenizer.eos_token_id]     )

              lowerCAmelCase__      =			'''zh'''
              self.assertListEqual(self.tokenizer.prefix_tokens      ,					[self.tokenizer.get_lang_id('''zh'''     )]     )
              self.assertListEqual(self.tokenizer.suffix_tokens      ,					[self.tokenizer.eos_token_id]     )







       @require_torch
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
              lowerCAmelCase__      =			'''mr'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens      ,					[self.tokenizer.get_lang_id('''mr'''     )]     )
              self.assertListEqual(self.tokenizer.suffix_tokens      ,					[self.tokenizer.eos_token_id]     )
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens      ,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )

              lowerCAmelCase__      =			'''zh'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens      ,					[self.tokenizer.get_lang_id('''zh'''     )]     )
              self.assertListEqual(self.tokenizer.suffix_tokens      ,					[self.tokenizer.eos_token_id]     )
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens      ,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )







       @require_torch
       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
              lowerCAmelCase__      =			self.tokenizer._build_translation_inputs('''A test'''      ,					return_tensors='''pt'''      ,					src_lang='''en'''      ,					tgt_lang='''ar'''     )

              self.assertEqual(
                  nested_simplify(_SCREAMING_SNAKE_CASE     )      ,					{
                      # en_XX, A, test, EOS
                      '''input_ids''': [[12_80_22, 58, 41_83, 2]],
                      '''attention_mask''': [[1, 1, 1, 1]],
                      # ar_AR
                      '''forced_bos_token_id''': 12_80_06,
                  }      ,					)



'''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()