

"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor

a    :Union[str, Any]			=       random.Random()




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase=1.0     ,    __lowerCAmelCase=None     ,    __lowerCAmelCase=None							)				-> Union[str, Any]:
      if rng is None:
            SCREAMING_SNAKE_CASE__      :					Tuple											=		global_rng

      SCREAMING_SNAKE_CASE__      :					int											=		[]
      for batch_idx in range(shape[0]							):
            values.append([]							)
            for _ in range(shape[1]							):
                  values[-1].append(rng.random() * scale							)

      return values

@require_torch
@require_torchaudio
class    __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(  self		,			_a		,			_a=7		,			_a=400		,			_a=2_000		,			_a=24		,			_a=24		,			_a=0.0		,			_a=16_000		,			_a=True		,			_a=True		,			)     ->					Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		parent
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		batch_size
           SCREAMING_SNAKE_CASE__      :					Tuple											=		min_seq_length
           SCREAMING_SNAKE_CASE__      :					Tuple											=		max_seq_length
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           SCREAMING_SNAKE_CASE__      :					Any											=		feature_size
           SCREAMING_SNAKE_CASE__      :					List[str]											=		num_mel_bins
           SCREAMING_SNAKE_CASE__      :					int											=		padding_value
           SCREAMING_SNAKE_CASE__      :					Tuple											=		sampling_rate
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		return_attention_mask
           SCREAMING_SNAKE_CASE__      :					int											=		do_normalize

     def     _a	(  self     )     ->					Tuple:


           """simple docstring"""


           return {
               "feature_size": self.feature_size,
               "num_mel_bins": self.num_mel_bins,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
               "do_normalize": self.do_normalize,
           }





     def     _a	(  self		,			_a=False		,			_a=False     )     ->					Tuple:


           """simple docstring"""


           def _flatten(_a     ):
                 return list(itertools.chain(*_lowerCamelCase     )     )

           if equal_length:
                 SCREAMING_SNAKE_CASE__      :					int											=		[floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
           else:
                 # make sure that inputs increase in size
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		[
                     floats_list((x, self.feature_size)     )
                     for x in range(self.min_seq_length		,			self.max_seq_length		,			self.seq_length_diff     )
                 ]
           if numpify:
                 SCREAMING_SNAKE_CASE__      :					Dict											=		[np.asarray(_lowerCamelCase     ) for x in speech_inputs]
           return speech_inputs





@require_torch
@require_torchaudio
class    __a							(A_      ,		unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :int				=       SpeechaTextFeatureExtractor if is_speech_available() else None

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Tuple											=		SpeechaTextFeatureExtractionTester(self     )

     def     _a	(  self		,			_a     )     ->					List[Any]:


           """simple docstring"""


           self.assertTrue(np.all(np.mean(_lowerCamelCase		,			axis=0     ) < 1E-3     )     )
           self.assertTrue(np.all(np.abs(np.var(_lowerCamelCase		,			axis=0     ) - 1     ) < 1E-3     )     )

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           # create three inputs of length 800, 1000, and 1200
           SCREAMING_SNAKE_CASE__      :					Tuple											=		[floats_list((1, x)     )[0] for x in range(800		,			1_400		,			200     )]
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		[np.asarray(_lowerCamelCase     ) for speech_input in speech_inputs]

           # Test feature size
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		feature_extractor(_lowerCamelCase		,			padding=_lowerCamelCase		,			return_tensors="""np"""     ).input_features
           self.assertTrue(input_features.ndim == 3     )
           self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size     )

           # Test not batched input
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		feature_extractor(speech_inputs[0]		,			return_tensors="""np"""     ).input_features
           SCREAMING_SNAKE_CASE__      :					Tuple											=		feature_extractor(np_speech_inputs[0]		,			return_tensors="""np"""     ).input_features
           self.assertTrue(np.allclose(_lowerCamelCase		,			_lowerCamelCase		,			atol=1E-3     )     )

           # Test batched
           SCREAMING_SNAKE_CASE__      :					Dict											=		feature_extractor(_lowerCamelCase		,			return_tensors="""np"""     ).input_features
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		feature_extractor(_lowerCamelCase		,			return_tensors="""np"""     ).input_features
           for enc_seq_a, enc_seq_a in zip(_lowerCamelCase		,			_lowerCamelCase     ):
                 self.assertTrue(np.allclose(_lowerCamelCase		,			_lowerCamelCase		,			atol=1E-3     )     )

           # Test 2-D numpy arrays are batched.
           SCREAMING_SNAKE_CASE__      :					Dict											=		[floats_list((1, x)     )[0] for x in (800, 800, 800)]
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		np.asarray(_lowerCamelCase     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		feature_extractor(_lowerCamelCase		,			return_tensors="""np"""     ).input_features
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		feature_extractor(_lowerCamelCase		,			return_tensors="""np"""     ).input_features
           for enc_seq_a, enc_seq_a in zip(_lowerCamelCase		,			_lowerCamelCase     ):
                 self.assertTrue(np.allclose(_lowerCamelCase		,			_lowerCamelCase		,			atol=1E-3     )     )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		[floats_list((1, x)     )[0] for x in range(800		,			1_400		,			200     )]

           SCREAMING_SNAKE_CASE__      :					int											=		["""longest""", """max_length""", """do_not_pad"""]
           SCREAMING_SNAKE_CASE__      :					str											=		[None, 16, None]
           for max_length, padding in zip(_lowerCamelCase		,			_lowerCamelCase     ):
                 SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		feature_extractor(
                     _lowerCamelCase		,			padding=_lowerCamelCase		,			max_length=_lowerCamelCase		,			return_attention_mask=_lowerCamelCase     )
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		inputs.input_features
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		inputs.attention_mask
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		[np.sum(_lowerCamelCase     ) for x in attention_mask]

                 self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
                 self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
                 self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )

     def     _a	(  self     )     ->					str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		[floats_list((1, x)     )[0] for x in range(800		,			1_400		,			200     )]

           SCREAMING_SNAKE_CASE__      :					Tuple											=		["""longest""", """max_length""", """do_not_pad"""]
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		[None, 16, None]
           for max_length, padding in zip(_lowerCamelCase		,			_lowerCamelCase     ):
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		feature_extractor(
                     _lowerCamelCase		,			max_length=_lowerCamelCase		,			padding=_lowerCamelCase		,			return_tensors="""np"""		,			return_attention_mask=_lowerCamelCase     )
                 SCREAMING_SNAKE_CASE__      :					Dict											=		inputs.input_features
                 SCREAMING_SNAKE_CASE__      :					Any											=		inputs.attention_mask
                 SCREAMING_SNAKE_CASE__      :					List[str]											=		[np.sum(_lowerCamelCase     ) for x in attention_mask]

                 self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
                 self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6     )
                 self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
                 self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6     )
                 self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )

     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           SCREAMING_SNAKE_CASE__      :					int											=		[floats_list((1, x)     )[0] for x in range(800		,			1_400		,			200     )]
           SCREAMING_SNAKE_CASE__      :					int											=		feature_extractor(
               _lowerCamelCase		,			padding="""max_length"""		,			max_length=4		,			truncation=_lowerCamelCase		,			return_tensors="""np"""		,			return_attention_mask=_lowerCamelCase		,			)
           SCREAMING_SNAKE_CASE__      :					Dict											=		inputs.input_features
           SCREAMING_SNAKE_CASE__      :					Dict											=		inputs.attention_mask
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		np.sum(attention_mask == 1		,			axis=1     )

           self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
           self._check_zero_mean_unit_variance(input_features[1]     )
           self._check_zero_mean_unit_variance(input_features[2]     )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		[floats_list((1, x)     )[0] for x in range(800		,			1_400		,			200     )]
           SCREAMING_SNAKE_CASE__      :					int											=		feature_extractor(
               _lowerCamelCase		,			padding="""longest"""		,			max_length=4		,			truncation=_lowerCamelCase		,			return_tensors="""np"""		,			return_attention_mask=_lowerCamelCase		,			)
           SCREAMING_SNAKE_CASE__      :					Any											=		inputs.input_features
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		inputs.attention_mask
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		np.sum(attention_mask == 1		,			axis=1     )

           self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
           self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
           self._check_zero_mean_unit_variance(input_features[2]     )

           # make sure that if max_length < longest -> then pad to max_length
           self.assertEqual(input_features.shape		,			(3, 4, 24)     )

           SCREAMING_SNAKE_CASE__      :					str											=		[floats_list((1, x)     )[0] for x in range(800		,			1_400		,			200     )]
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		feature_extractor(
               _lowerCamelCase		,			padding="""longest"""		,			max_length=16		,			truncation=_lowerCamelCase		,			return_tensors="""np"""		,			return_attention_mask=_lowerCamelCase		,			)
           SCREAMING_SNAKE_CASE__      :					List[str]											=		inputs.input_features
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		inputs.attention_mask
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		np.sum(attention_mask == 1		,			axis=1     )

           self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
           self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
           self._check_zero_mean_unit_variance(input_features[2]     )

           # make sure that if max_length < longest -> then pad to max_length
           self.assertEqual(input_features.shape		,			(3, 6, 24)     )

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           import torch

           SCREAMING_SNAKE_CASE__      :					Dict											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		np.random.rand(100		,			32     ).astype(np.floataa     )
           SCREAMING_SNAKE_CASE__      :					Dict											=		np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		feature_extractor.pad([{"""input_features""": inputs}]		,			return_tensors="""np"""     )
                 self.assertTrue(np_processed.input_features.dtype == np.floataa     )
                 SCREAMING_SNAKE_CASE__      :					Optional[int]											=		feature_extractor.pad([{"""input_features""": inputs}]		,			return_tensors="""pt"""     )
                 self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )

     def     _a	(  self		,			_a     )     ->					Dict:


           """simple docstring"""


           from datasets import load_dataset

           SCREAMING_SNAKE_CASE__      :					List[str]											=		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,			"""clean"""		,			split="""validation"""     )
           # automatic decoding with librispeech
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		ds.sort("""id"""     ).select(range(_lowerCamelCase     )     )[:num_samples]["""audio"""]

           return [x["array"] for x in speech_samples]





     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Dict											=		np.array([
               -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
               -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
               -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
           ]     )
           # fmt: on

           SCREAMING_SNAKE_CASE__      :					List[str]											=		self._load_datasamples(1     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		feature_extractor(_lowerCamelCase		,			return_tensors="""pt"""     ).input_features
           self.assertEquals(input_features.shape		,			(1, 584, 24)     )
           self.assertTrue(np.allclose(input_features[0, 0, :30]		,			_lowerCamelCase		,			atol=1E-4     )     )






"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase__          = {
    'google/rembert': 256,
}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int="[CLS]"    ,      _lowerCamelCase			:			Optional[int]="[SEP]"    ,      _lowerCamelCase			:			Optional[int]="[UNK]"    ,      _lowerCamelCase			:			Optional[Any]="[SEP]"    ,      _lowerCamelCase			:			str="[PAD]"    ,      _lowerCamelCase			:			List[Any]="[CLS]"    ,      _lowerCamelCase			:			Any="[MASK]"    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			int			):
       return len(self.sp_model			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			List[str]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       d
       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple=False			):
       _snake_case					     =       self.sp_model.EncodeAsPieces(_lowerCamelCase			)
       return pieces







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       self.sp_model.decode_pieces(_lowerCamelCase			)
       return out_string







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''			)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep			) * [0]
       return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase			)			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)

       return (out_vocab_file,)



