



import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A_		:     Union[str, Any]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''AutoTokenizer'''
   _UpperCAmelCase     	=			['''tokenizer''']

   _UpperCAmelCase     	=			{
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Optional[Any]=None							)       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							)

    a						=		speaker_embeddings

   @classmethod
   def 		A							(      cls			:       Optional[Any]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[str, Any]="speaker_embeddings_path.json"							,   **__lowerCAmelCase			:       List[str]							)       ->				str:


    """simple docstring"""






    if speaker_embeddings_dict_path is not None:
     a						=		get_file_from_repo(
         __lowerCAmelCase							,   __lowerCAmelCase							,   subfolder=kwargs.pop("subfolder"							,   __lowerCAmelCase							)							,   cache_dir=kwargs.pop("cache_dir"							,   __lowerCAmelCase							)							,   force_download=kwargs.pop("force_download"							,   __lowerCAmelCase							)							,   proxies=kwargs.pop("proxies"							,   __lowerCAmelCase							)							,   resume_download=kwargs.pop("resume_download"							,   __lowerCAmelCase							)							,   local_files_only=kwargs.pop("local_files_only"							,   __lowerCAmelCase							)							,   use_auth_token=kwargs.pop("use_auth_token"							,   __lowerCAmelCase							)							,   revision=kwargs.pop("revision"							,   __lowerCAmelCase							)							,   )
     if speaker_embeddings_path is None:
      logger.warning(
          f"""`{os.path.join(__lowerCAmelCase							,   __lowerCAmelCase							)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""							)
      a						=		None
     else:
      with open(__lowerCAmelCase							) as speaker_embeddings_json:
       a						=		json.load(__lowerCAmelCase							)
    else:
     a						=		None

    a						=		AutoTokenizer.from_pretrained(__lowerCAmelCase							,   **__lowerCAmelCase							)

    return cls(tokenizer=__lowerCAmelCase							,   speaker_embeddings=__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]="speaker_embeddings_path.json"							,   __lowerCAmelCase			:       str="speaker_embeddings"							,   __lowerCAmelCase			:       bool = False							,   **__lowerCAmelCase			:       Dict							,   )       ->				Optional[Any]:


    """simple docstring"""






    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(__lowerCAmelCase							,   __lowerCAmelCase							,   "v2"							)							,   exist_ok=__lowerCAmelCase							)

     a						=		{}

     a						=		save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       a						=		self._load_voice_preset(__lowerCAmelCase							)

       a						=		{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["repo_or_path"]							,   __lowerCAmelCase							,   f"""{prompt_key}_{key}"""							)							,   voice_preset[key]							,   allow_pickle=__lowerCAmelCase							,   )
        a						=		os.path.join(__lowerCAmelCase							,   f"""{prompt_key}_{key}.npy"""							)

       a						=		tmp_dict

     with open(os.path.join(__lowerCAmelCase							,   __lowerCAmelCase							)							,   "w"							) as fp:
      json.dump(__lowerCAmelCase							,   __lowerCAmelCase							)

    super().save_pretrained(__lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str = None							,   **__lowerCAmelCase			:       Union[str, Any]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.speaker_embeddings[voice_preset]

    a						=		{}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          f"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""							)

     a						=		get_file_from_repo(
         self.speaker_embeddings.get("repo_or_path"							,   "/"							)							,   voice_preset_paths[key]							,   subfolder=kwargs.pop("subfolder"							,   __lowerCAmelCase							)							,   cache_dir=kwargs.pop("cache_dir"							,   __lowerCAmelCase							)							,   force_download=kwargs.pop("force_download"							,   __lowerCAmelCase							)							,   proxies=kwargs.pop("proxies"							,   __lowerCAmelCase							)							,   resume_download=kwargs.pop("resume_download"							,   __lowerCAmelCase							)							,   local_files_only=kwargs.pop("local_files_only"							,   __lowerCAmelCase							)							,   use_auth_token=kwargs.pop("use_auth_token"							,   __lowerCAmelCase							)							,   revision=kwargs.pop("revision"							,   __lowerCAmelCase							)							,   )
     if path is None:
      raise ValueError(
          f"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"							,   "/"							)							,   voice_preset_paths[key]							)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""							)

     a						=		np.load(__lowerCAmelCase							)

    return voice_preset_dict

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       Optional[dict] = None							)       ->				Dict:


    """simple docstring"""






    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(f"""Voice preset unrecognized, missing {key} as a key."""							)

     if not isinstance(voice_preset[key]							,   np.ndarray							):
      raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key]							)}D ndarray."""							)

     if len(voice_preset[key].shape							) != self.preset_shape[key]:
      raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key]							)}D ndarray."""							)




   def __call__(      self			:       List[Any]							,   __lowerCAmelCase			:       str=None							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Union[str, Any]="pt"							,   __lowerCAmelCase			:       List[Any]=256							,   __lowerCAmelCase			:       List[str]=False							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       List[str]=False							,   **__lowerCAmelCase			:       List[Any]							,   )       ->				Tuple:


    """simple docstring"""






    if voice_preset is not None and not isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):
     if (
         isinstance(__lowerCAmelCase							,   __lowerCAmelCase							)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      a						=		self._load_voice_preset(__lowerCAmelCase							)

     else:
      if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) and not voice_preset.endswith(".npz"							):
       a						=		voice_preset + ".npz"

      a						=		np.load(__lowerCAmelCase							)

    if voice_preset is not None:
     self._validate_voice_preset_dict(__lowerCAmelCase							,   **__lowerCAmelCase							)
     a						=		BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)

    a						=		self.tokenizer(
        __lowerCAmelCase							,   return_tensors=__lowerCAmelCase							,   padding="max_length"							,   max_length=__lowerCAmelCase							,   return_attention_mask=__lowerCAmelCase							,   return_token_type_ids=__lowerCAmelCase							,   add_special_tokens=__lowerCAmelCase							,   **__lowerCAmelCase							,   )

    if voice_preset is not None:
     a						=		voice_preset

    return encoded_text






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


A_		:     List[str]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''audio_values''', '''audio_mask''']

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Dict=2048							,   __lowerCAmelCase			:       List[Any]=1							,   __lowerCAmelCase			:       Dict=[16, 16]							,   __lowerCAmelCase			:       str=128							,   __lowerCAmelCase			:       Optional[int]=4_4100							,   __lowerCAmelCase			:       int=86							,   __lowerCAmelCase			:       Optional[Any]=2048							,   __lowerCAmelCase			:       str=0.0							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(
        feature_size=__lowerCAmelCase							,   sampling_rate=__lowerCAmelCase							,   padding_value=__lowerCAmelCase							,   **__lowerCAmelCase							,   )

    a						=		spectrogram_length
    a						=		num_channels
    a						=		patch_size
    a						=		feature_size // self.patch_size[1]
    a						=		n_fft
    a						=		sampling_rate // hop_length_to_sampling_rate
    a						=		sampling_rate
    a						=		padding_value
    a						=		mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2							,   num_mel_filters=__lowerCAmelCase							,   min_frequency=0.0							,   max_frequency=2_2_0_5_0.0							,   sampling_rate=__lowerCAmelCase							,   norm="slaney"							,   mel_scale="slaney"							,   ).T

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       np.array							)       ->				np.ndarray:


    """simple docstring"""






    a						=		spectrogram(
        __lowerCAmelCase							,   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=8_0.0							,   )
    a						=		log_spec[:, :-1]
    a						=		log_spec - 2_0.0
    a						=		np.clip(log_spec / 4_0.0							,   -2.0							,   0.0							) + 1.0
    return log_spec




   def __call__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,   __lowerCAmelCase			:       Optional[Union[str, TensorType]] = None							,   __lowerCAmelCase			:       Optional[bool] = True							,   __lowerCAmelCase			:       Optional[int] = None							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = False							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				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."							)

    a						=		isinstance(__lowerCAmelCase							,   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}"""							)
    a						=		is_batched_numpy or (
        isinstance(__lowerCAmelCase							,   (list, tuple)							) and (isinstance(raw_speech[0]							,   (np.ndarray, tuple, list)							))
    )
    if is_batched:
     a						=		[np.asarray([speech]							,   dtype=np.floataa							).T for speech in raw_speech]
    elif not is_batched and not isinstance(__lowerCAmelCase							,   np.ndarray							):
     a						=		np.asarray(__lowerCAmelCase							,   dtype=np.floataa							)
    elif isinstance(__lowerCAmelCase							,   np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
     a						=		raw_speech.astype(np.floataa							)
    # always return batch
    if not is_batched:
     a						=		[np.asarray([raw_speech]							).T]

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

    # Create audio attention mask
    a						=		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:
     a						=		[
         (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
     ]
     a						=		np.array(__lowerCAmelCase							).astype(np.floataa							)

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

    # return as BatchFeature
    if return_attention_mask:
     a						=		{"audio_values": padded_audio_features, "audio_mask": audio_mask}
    else:
     a						=		{"audio_values": padded_audio_features}

    a						=		BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)
    return encoded_inputs

