


'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

UpperCAmelCase_			:  str										=   random.Random()


def       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Any=1.0,					UpperCamelCase__							:       int=None,					UpperCamelCase__							:       Any=None      ):
 if rng is None:
  __A	:		Tuple	  =      global_rng

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

 return values

class     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=7   ,   __lowercase=400   ,   __lowercase=2_000   ,   __lowercase=2_048   ,   __lowercase=128   ,   __lowercase=1   ,   __lowercase=512   ,   __lowercase=30   ,   __lowercase=44_100   ,   ):

  """simple docstring"""







  __A	:		List[str]	  =      parent
  __A	:		Optional[Any]	  =      batch_size
  __A	:		Union[str, Any]	  =      min_seq_length
  __A	:		List[Any]	  =      max_seq_length
  __A	:		Optional[int]	  =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  __A	:		str	  =      spectrogram_length
  __A	:		int	  =      feature_size
  __A	:		str	  =      num_audio_channels
  __A	:		List[Any]	  =      hop_length
  __A	:		List[str]	  =      chunk_length
  __A	:		Optional[int]	  =      sampling_rate


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return {
      "spectrogram_length": self.spectrogram_length,
      "feature_size": self.feature_size,
      "num_audio_channels": self.num_audio_channels,
      "hop_length": self.hop_length,
      "chunk_length": self.chunk_length,
      "sampling_rate": self.sampling_rate,
  }







 def 			snake_case__ (   self   ,   __lowercase=False   ,   __lowercase=False    ):

  """simple docstring"""







  def _flatten(__lowercase    ):
   return list(itertools.chain(*__lowercase    )    )

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


@require_torch
@require_torchaudio
class     _lowerCamelCase       (     snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		List[str]     				=    TvltFeatureExtractor


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      TvltFeatureExtractionTester(self    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  self.assertTrue(hasattr(__lowercase   ,   'spectrogram_length'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'feature_size'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'num_audio_channels'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'hop_length'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'chunk_length'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'sampling_rate'    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.feature_extraction_class(**self.feat_extract_dict    )

  with tempfile.TemporaryDirectory() as tmpdirname:
   __A	:		Optional[Any]	  =      feat_extract_first.save_pretrained(__lowercase    )[0]
   check_json_file_has_correct_format(__lowercase    )
   __A	:		Optional[Any]	  =      self.feature_extraction_class.from_pretrained(__lowercase    )

  __A	:		int	  =      feat_extract_first.to_dict()
  __A	:		Tuple	  =      feat_extract_second.to_dict()
  __A	:		int	  =      dict_first.pop('mel_filters'    )
  __A	:		Union[str, Any]	  =      dict_second.pop('mel_filters'    )
  self.assertTrue(np.allclose(__lowercase   ,   __lowercase    )    )
  self.assertEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.feature_extraction_class(**self.feat_extract_dict    )

  with tempfile.TemporaryDirectory() as tmpdirname:
   __A	:		Dict	  =      os.path.join(__lowercase   ,   'feat_extract.json'    )
   feat_extract_first.to_json_file(__lowercase    )
   __A	:		Optional[Any]	  =      self.feature_extraction_class.from_json_file(__lowercase    )

  __A	:		List[Any]	  =      feat_extract_first.to_dict()
  __A	:		Union[str, Any]	  =      feat_extract_second.to_dict()
  __A	:		List[str]	  =      dict_first.pop('mel_filters'    )
  __A	:		int	  =      dict_second.pop('mel_filters'    )
  self.assertTrue(np.allclose(__lowercase   ,   __lowercase    )    )
  self.assertEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      self.feature_extraction_class(**self.feat_extract_dict    )

  # create three inputs of length 800, 1000, and 1200
  __A	:		Optional[int]	  =      [floats_list((1, x)    )[0] for x in range(800   ,   1_400   ,   200    )]
  __A	:		str	  =      [np.asarray(__lowercase    ) for speech_input in speech_inputs]

  # Test not batched input
  __A	:		List[str]	  =      feature_extractor(np_speech_inputs[0]   ,   return_tensors='np'   ,   sampling_rate=44_100    ).audio_values

  self.assertTrue(encoded_audios.ndim == 4    )
  self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
  self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
  self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

  # Test batched
  __A	:		Dict	  =      feature_extractor(__lowercase   ,   return_tensors='np'   ,   sampling_rate=44_100    ).audio_values

  self.assertTrue(encoded_audios.ndim == 4    )
  self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
  self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
  self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

  # Test audio masking
  __A	:		Any	  =      feature_extractor(
      __lowercase   ,   return_tensors='np'   ,   sampling_rate=44_100   ,   mask_audio=__lowercase    ).audio_values

  self.assertTrue(encoded_audios.ndim == 4    )
  self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
  self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
  self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

  # Test 2-D numpy arrays are batched.
  __A	:		Optional[Any]	  =      [floats_list((1, x)    )[0] for x in (800, 800, 800)]
  __A	:		List[str]	  =      np.asarray(__lowercase    )
  __A	:		List[Any]	  =      feature_extractor(__lowercase   ,   return_tensors='np'   ,   sampling_rate=44_100    ).audio_values
  self.assertTrue(encoded_audios.ndim == 4    )
  self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
  self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
  self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,   'clean'   ,   split='validation'    )
  # automatic decoding with librispeech
  __A	:		Optional[Any]	  =      ds.sort('id'    ).select(range(__lowercase    )    )[:num_samples]['audio']

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







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      self._load_datasamples(1    )
  __A	:		Tuple	  =      TvltFeatureExtractor()
  __A	:		Optional[Any]	  =      feature_extractor(__lowercase   ,   return_tensors='pt'    ).audio_values

  self.assertEquals(audio_values.shape   ,   (1, 1, 192, 128)    )

  __A	:		Optional[Any]	  =      torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]    )
  self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,   __lowercase   ,   atol=1E-4    )    )










'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase_			:  Any										=   logging.get_logger(__name__)

UpperCAmelCase_			:  Tuple										=   {'vocab_file': 'spm_char.model'}

UpperCAmelCase_			:  int										=   {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

UpperCAmelCase_			:  List[Any]										=   {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    VOCAB_FILES_NAMES
 __lowercase					:		int     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		int     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Optional[int]     				=    ['''input_ids''', '''attention_mask''']


 def __init__(   self   ,   __lowercase   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="<unk>"   ,   __lowercase="<pad>"   ,   __lowercase = None   ,   **__lowercase   ,   ):

  """simple docstring"""







  __A	:		Dict	  =      {} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      bos_token=__lowercase   ,   eos_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   sp_model_kwargs=self.sp_model_kwargs   ,   **__lowercase   ,   )

  __A	:		Optional[Any]	  =      vocab_file

  __A	:		Any	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(__lowercase    )


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.sp_model.get_piece_size()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {self.convert_ids_to_tokens(__lowercase    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def __getstate__(   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      self.__dict__.copy()
  __A	:		str	  =      None
  return state


 def __setstate__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      d

  # for backward compatibility
  if not hasattr(self   ,   'sp_model_kwargs'    ):
   __A	:		Any	  =      {}

  __A	:		List[str]	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(self.vocab_file    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.encode(__lowercase   ,   out_type=__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.piece_to_id(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.sp_model.IdToPiece(__lowercase    )
  return token


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		str	  =      []
  __A	:		str	  =      ''
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    out_string += self.sp_model.decode(__lowercase    ) + token
    __A	:		Any	  =      []
   else:
    current_sub_tokens.append(__lowercase    )
  out_string += self.sp_model.decode(__lowercase    )
  return out_string.strip()


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  if token_ids_a is None:
   return token_ids_a + [self.eos_token_id]
  # We don't expect to process pairs, but leave the pair logic for API consistency
  return token_ids_a + token_ids_a + [self.eos_token_id]


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = False    ):

  """simple docstring"""







  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=__lowercase   ,   token_ids_a=__lowercase   ,   already_has_special_tokens=__lowercase    )

  __A	:		Dict	  =      [1]
  if token_ids_a is None:
   return ([0] * len(__lowercase    )) + suffix_ones
  return ([0] * len(__lowercase    )) + ([0] * len(__lowercase    )) + suffix_ones







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  if not os.path.isdir(__lowercase    ):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
   return
  __A	:		Optional[int]	  =      os.path.join(
      __lowercase   ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

  if os.path.abspath(self.vocab_file    ) != os.path.abspath(__lowercase    ) and os.path.isfile(self.vocab_file    ):
   copyfile(self.vocab_file   ,   __lowercase    )
  elif not os.path.isfile(self.vocab_file    ):
   with open(__lowercase   ,   'wb'    ) as fi:
    __A	:		Tuple	  =      self.sp_model.serialized_model_proto()
    fi.write(__lowercase    )

  return (out_vocab_file,)






