


'''simple docstring'''






import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase				=get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =ReformerTokenizer
       UpperCAmelCase		 =ReformerTokenizerFast
       UpperCAmelCase		 =True
       UpperCAmelCase		 =False
       UpperCAmelCase		 =True






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          super().setUp()

          _UpperCAmelCase  :  str				       =ReformerTokenizer(snake_case    ,				keep_accents=snake_case)
          tokenizer.save_pretrained(self.tmpdirname)






       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       ='<s>'
          _UpperCAmelCase  :  Tuple				       =1

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






       def      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]    ,				'<unk>')
          self.assertEqual(vocab_keys[1]    ,				'<s>')
          self.assertEqual(vocab_keys[-1]    ,				'j')
          self.assertEqual(len(snake_case)    ,				1_0_0_0)






       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          self.assertEqual(self.get_tokenizer().vocab_size    ,				1_0_0_0)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase  :  List[Any]				       =self.get_tokenizer()
          _UpperCAmelCase  :  List[Any]				       =self.get_rust_tokenizer()

          _UpperCAmelCase  :  Any				       ='I was born in 92000, and this is falsé.'

          _UpperCAmelCase  :  int				       =tokenizer.tokenize(snake_case)
          _UpperCAmelCase  :  str				       =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          _UpperCAmelCase  :  Optional[Any]				       =tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          _UpperCAmelCase  :  str				       =rust_tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer()
          _UpperCAmelCase  :  Tuple				       =tokenizer.encode(snake_case)
          _UpperCAmelCase  :  str				       =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case=1_5)		->    Tuple:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase  :  List[Any]				       =self.rust_tokenizer_class.from_pretrained(snake_case    ,				**snake_case)

                # Simple input
                _UpperCAmelCase  :  Tuple				       ='This is a simple input'
                _UpperCAmelCase  :  Any				       =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase  :  Optional[Any]				       =('This is a simple input', 'This is a pair')
                _UpperCAmelCase  :  str				       =[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          pass






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =ReformerTokenizer(snake_case    ,				keep_accents=snake_case)

          _UpperCAmelCase  :  Any				       =tokenizer.tokenize('This is a test')
          self.assertListEqual(snake_case    ,				['▁This', '▁is', '▁a', '▁t', 'est'])

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(snake_case)    ,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]    ,				)

          _UpperCAmelCase  :  Tuple				       =tokenizer.tokenize('I was born in 92000, and this is falsé.')
          self.assertListEqual(
              snake_case    ,				[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ]    ,				)
          _UpperCAmelCase  :  Any				       =tokenizer.convert_tokens_to_ids(snake_case)
          self.assertListEqual(
              snake_case    ,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]    ,				)

          _UpperCAmelCase  :  Tuple				       =tokenizer.convert_ids_to_tokens(snake_case)
          self.assertListEqual(
              snake_case    ,				[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ]    ,				)






       @cached_property
       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       ='Hello World!'
          _UpperCAmelCase  :  Optional[int]				       =[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

          self.assertListEqual(snake_case    ,				self.big_tokenizer.encode(snake_case))






       @slow
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =(
              'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
              ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
          )
          _UpperCAmelCase  :  Dict				       =[
              1_0_8,
              2_6_5,
              2_4,
              1_1_1,
              4,
              2_5_8,
              1_5_6,
              3_5,
              2_8,
              2_7_5,
              3,
              2_5_9,
              2_9_7,
              2_6_0,
              8_4,
              4,
              3_5,
              1_1_0,
              4_4,
              8,
              2_5_9,
              9_1,
              2_6_8,
              2_1,
              1_1,
              2_0_9,
              2_7_4,
              1_0_9,
              2_6_6,
              2_7_7,
              1_1_7,
              8_6,
              9_3,
              3_1_5,
              2_5_8,
              2_7_8,
              2_5_8,
              2_7_7,
              2_5_8,
              0,
              2_5_8,
              2_8_8,
              2_5_8,
              3_1_9,
              2_5_8,
              0,
              2_5_8,
              0,
              2_5_8,
              0,
              2_5_8,
              0,
              2_5_8,
              2_8_7,
              2_5_8,
              3_1_5,
              2_5_8,
              2_8_9,
              2_5_8,
              2_7_8,
              9_9,
              2_6_9,
              2_6_6,
              2_6_2,
              8,
              2_5_9,
              2_4_1,
              4,
              2_1_7,
              2_3_0,
              2_6_8,
              2_6_6,
              5_5,
              1_6_8,
              1_0_6,
              7_5,
              1_9_3,
              2_6_6,
              2_2_3,
              2_7,
              4_9,
              2_6,
              2_8_2,
              2_5,
              2_6_4,
              2_9_9,
              1_9,
              2_6,
              0,
              2_5_8,
              2_7_7,
              1_1_7,
              8_6,
              9_3,
              1_7_6,
              1_8_3,
              2_7_0,
              1_1,
              2_6_2,
              4_2,
              6_1,
              2_6_5,
          ]

          self.assertListEqual(snake_case    ,				self.big_tokenizer.encode(snake_case))






       @require_torch
       @slow
       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          _UpperCAmelCase  :  Dict				       =list(self.big_tokenizer.get_vocab().keys())[:1_0]
          _UpperCAmelCase  :  Any				       =' '.join(snake_case)
          _UpperCAmelCase  :  Optional[Any]				       =self.big_tokenizer.encode_plus(snake_case    ,				return_tensors='pt')
          _UpperCAmelCase  :  Optional[int]				       =self.big_tokenizer.batch_encode_plus([sequence, sequence]    ,				return_tensors='pt')

          _UpperCAmelCase  :  Optional[int]				       =ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          _UpperCAmelCase  :  List[str]				       =encoded_sequence['input_ids'].shape
          _UpperCAmelCase  :  Any				       =ReformerModel(snake_case)

          # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
             model(**snake_case)
             model(**snake_case)






       @slow
       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          # fmt: off
          _UpperCAmelCase  :  str				       ={'input_ids': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '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]]}  # noqa: E501
          # fmt: on

          # This tokenizer does not know some characters like ")".
          # That is the reason why we use very simple texts here.
          # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
          _UpperCAmelCase  :  Tuple				       =[
              'This is a very simple sentence.',
              'The quick brown fox jumps over the lazy dog.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=snake_case    ,				model_name='google/reformer-crime-and-punishment'    ,				revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'    ,				padding=snake_case    ,				sequences=snake_case    ,				)





'''simple docstring'''






from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowercase				=logging.get_logger(__name__)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["audio_values", "audio_mask"]






       def __init__(     self    ,				snake_case=2_0_4_8    ,				snake_case=1    ,				snake_case=[1_6, 1_6]    ,				snake_case=1_2_8    ,				snake_case=4_4_1_0_0    ,				snake_case=8_6    ,				snake_case=2_0_4_8    ,				snake_case=0.0    ,				**snake_case    ,				)		->    Optional[int]:




          '''simple docstring'''
          super().__init__(
              feature_size=snake_case    ,				sampling_rate=snake_case    ,				padding_value=snake_case    ,				**snake_case    ,				)

          _UpperCAmelCase  :  Dict				       =spectrogram_length
          _UpperCAmelCase  :  Any				       =num_channels
          _UpperCAmelCase  :  Optional[int]				       =patch_size
          _UpperCAmelCase  :  List[str]				       =feature_size // self.patch_size[1]
          _UpperCAmelCase  :  Optional[int]				       =n_fft
          _UpperCAmelCase  :  int				       =sampling_rate // hop_length_to_sampling_rate
          _UpperCAmelCase  :  int				       =sampling_rate
          _UpperCAmelCase  :  List[Any]				       =padding_value
          _UpperCAmelCase  :  Optional[int]				       =mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2    ,				num_mel_filters=snake_case    ,				min_frequency=0.0    ,				max_frequency=2_20_50.0    ,				sampling_rate=snake_case    ,				norm='slaney'    ,				mel_scale='slaney'    ,				).T






       def      lowerCAmelCase						(     self    ,				snake_case)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =spectrogram(
              snake_case    ,				window_function(self.n_fft    ,				'hann')    ,				frame_length=self.n_fft    ,				hop_length=self.hop_length    ,				power=2.0    ,				mel_filters=self.mel_filters.T    ,				log_mel='dB'    ,				db_range=80.0    ,				)
          _UpperCAmelCase  :  Any				       =log_spec[:, :-1]
          _UpperCAmelCase  :  List[str]				       =log_spec - 20.0
          _UpperCAmelCase  :  Union[str, Any]				       =np.clip(log_spec / 40.0    ,				-2.0    ,				0.0) + 1.0
          return log_spec






       def __call__(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = True    ,				snake_case = None    ,				snake_case = False    ,				snake_case = False    ,				**snake_case    ,				)		->    BatchFeature:




          '''simple docstring'''

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    'This feature extractor is set to support sampling rate'
                    f" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                    f" with {self.sampling_rate} and not {sampling_rate}.")
          else:
             logger.warning(
                 'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                 'Failing to do so can result in silent errors that might be hard to debug.')

          _UpperCAmelCase  :  Tuple				       =isinstance(snake_case    ,				np.ndarray) and len(raw_speech.shape) > 1
          if is_batched_numpy and len(raw_speech.shape) > 2:
             raise ValueError(f"Only mono-channel audio is supported for input to {self}")
          _UpperCAmelCase  :  str				       =is_batched_numpy or (
              isinstance(snake_case    ,				(list, tuple)) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)))
          )
          if is_batched:
             _UpperCAmelCase  :  List[str]				       =[np.asarray([speech]    ,				dtype=np.floataa).T for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case    ,				np.ndarray):
             _UpperCAmelCase  :  List[Any]				       =np.asarray(snake_case    ,				dtype=np.floataa)
          elif isinstance(snake_case    ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
             _UpperCAmelCase  :  List[Any]				       =raw_speech.astype(np.floataa)
          # always return batch
          if not is_batched:
             _UpperCAmelCase  :  Dict				       =[np.asarray([raw_speech]).T]

          # Convert audio signals to log mel spectrograms, truncate by time axis
          _UpperCAmelCase  :  List[Any]				       =[
              self._np_extract_fbank_features(waveform.squeeze()).T[: self.spectrogram_length] for waveform in raw_speech
          ]
          if isinstance(audio_features[0]    ,				snake_case):
             _UpperCAmelCase  :  Dict				       =[np.asarray(snake_case    ,				dtype=np.floataa) for feature in audio_features]

          # Create audio attention mask
          _UpperCAmelCase  :  Tuple				       =max(
              [ceil(feature.shape[0] / self.patch_size[0]) * self.freq_len for feature in audio_features])  # The maximum number of audio patches in a batch
          if return_attention_mask:
             _UpperCAmelCase  :  Any				       =[
                 (ceil(feature.shape[0] / self.patch_size[0]) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             _UpperCAmelCase  :  int				       =np.array(snake_case).astype(np.floataa)

          # convert into correct format for padding
          _UpperCAmelCase  :  List[Any]				       =max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
          _UpperCAmelCase  :  List[str]				       =np.ones([len(snake_case), 1, max_time_len, self.feature_size]).astype(np.floataa)
          _UpperCAmelCase  :  Any				       =padded_audio_features * self.padding_value
          for i in range(len(snake_case)):
             _UpperCAmelCase  :  List[str]				       =audio_features[i]
             _UpperCAmelCase  :  Dict				       =feature

          # return as BatchFeature
          if return_attention_mask:
             _UpperCAmelCase  :  List[str]				       ={'audio_values': padded_audio_features, 'audio_mask': audio_mask}
          else:
             _UpperCAmelCase  :  Optional[int]				       ={'audio_values': padded_audio_features}

          _UpperCAmelCase  :  Any				       =BatchFeature(data=snake_case    ,				tensor_type=snake_case)
          return encoded_inputs

