


"""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


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


@require_sentencepiece
@require_tokenizers
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	List[Any]    =		ReformerTokenizer
     A_       :	Union[str, Any]    =		ReformerTokenizerFast
     A_       :	List[str]    =		True
     A_       :	Optional[Any]    =		False
     A_       :	Optional[Any]    =		True


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

          _a    	=				ReformerTokenizer(__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase							)
          tokenizer.save_pretrained(self.tmpdirname							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				'''<s>'''
          _a    	=				1

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


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				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(__UpperCAmelCase							)    ,				1000							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          self.assertEqual(self.get_tokenizer().vocab_size    ,				1000							)


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          if not self.test_rust_tokenizer:
               return

          _a    	=				self.get_tokenizer()
          _a    	=				self.get_rust_tokenizer()

          _a    	=				'''I was born in 92000, and this is falsé.'''

          _a    	=				tokenizer.tokenize(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				self.get_rust_tokenizer()
          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=15							)       ->							int:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
                    _a    	=				self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase    ,				**__UpperCAmelCase							)

                    # Simple input
                    _a    	=				'''This is a simple input'''
                    _a    	=				['''This is a simple input 1''', '''This is a simple input 2''']
                    _a    	=				('''This is a simple input''', '''This is a pair''')
                    _a    	=				[
                        ('''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(__UpperCAmelCase    ,				tokenizer_r.encode    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding='''max_length'''							)

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

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

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

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

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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				ReformerTokenizer(__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase							)

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

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)    ,				[285, 46, 10, 170, 382]    ,				)

          _a    	=				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
          self.assertListEqual(
              __UpperCAmelCase    ,				[
                  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''',
                  '''.''',
              ]    ,				)
          _a    	=				tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)
          self.assertListEqual(
              __UpperCAmelCase    ,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]    ,				)

          _a    	=				tokenizer.convert_ids_to_tokens(__UpperCAmelCase							)
          self.assertListEqual(
              __UpperCAmelCase    ,				[
                  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 							_UpperCAmelCase      (      self							)       ->							Any:
          return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''							)


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				'''Hello World!'''
          _a    	=				[126, 32, 262, 152, 38, 72, 287]

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


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				(
              '''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'''
          )
          _a    	=				[
              108,
              265,
              24,
              111,
              4,
              258,
              156,
              35,
              28,
              275,
              3,
              259,
              297,
              260,
              84,
              4,
              35,
              110,
              44,
              8,
              259,
              91,
              268,
              21,
              11,
              209,
              274,
              109,
              266,
              277,
              117,
              86,
              93,
              315,
              258,
              278,
              258,
              277,
              258,
              0,
              258,
              288,
              258,
              319,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              287,
              258,
              315,
              258,
              289,
              258,
              278,
              99,
              269,
              266,
              262,
              8,
              259,
              241,
              4,
              217,
              230,
              268,
              266,
              55,
              168,
              106,
              75,
              193,
              266,
              223,
              27,
              49,
              26,
              282,
              25,
              264,
              299,
              19,
              26,
              0,
              258,
              277,
              117,
              86,
              93,
              176,
              183,
              270,
              11,
              262,
              42,
              61,
              265,
          ]

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


     @require_torch
     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          _a    	=				list(self.big_tokenizer.get_vocab().keys()							)[:10]
          _a    	=				''' '''.join(__UpperCAmelCase							)
          _a    	=				self.big_tokenizer.encode_plus(__UpperCAmelCase    ,				return_tensors='''pt'''							)
          _a    	=				self.big_tokenizer.batch_encode_plus([sequence, sequence]    ,				return_tensors='''pt'''							)

          _a    	=				ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          _a    	=				encoded_sequence['''input_ids'''].shape
          _a    	=				ReformerModel(__UpperCAmelCase							)

          # 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(**__UpperCAmelCase							)
               model(**__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          # fmt: off
          _a    	=				{'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''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
          _a    	=				[
              '''This is a very simple sentence.''',
              '''The quick brown fox jumps over the lazy dog.''',
          ]

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



"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							BatchFeature:
          if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                        F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                        F' {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.'''							)

          if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

          if is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							).T for audio in raw_audio]
          elif not is_batched and not isinstance(__UpperCAmelCase    ,				np.ndarray							):
               _a    	=				np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							)
          elif isinstance(__UpperCAmelCase    ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
               _a    	=				raw_audio.astype(np.floataa							)

          # always return batch
          if not is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase							).T]

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

          _a    	=				input_values
          if return_tensors is not None:
               _a    	=				padded_inputs.convert_to_tensors(__UpperCAmelCase							)

          return padded_inputs