
"""simple docstring"""






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


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """AutoTokenizer"""
	UpperCamelCase				=  ["""tokenizer"""]

	UpperCamelCase				=  {
	    """semantic_prompt""": 1,
	    """coarse_prompt""": 2,
	    """fine_prompt""": 2,
	}





	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int=None					)							->			Dict:







			'''simple docstring'''




			super().__init__(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= speaker_embeddings





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







			'''simple docstring'''




			if speaker_embeddings_dict_path is not None:
					SCREAMING_SNAKE_CASE       :     int				= 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\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."					)
							SCREAMING_SNAKE_CASE       :     int				= None
					else:
							with open(lowerCamelCase_					) as speaker_embeddings_json:
									SCREAMING_SNAKE_CASE       :     Dict				= json.load(lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= None

			SCREAMING_SNAKE_CASE       :     List[str]				= AutoTokenizer.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)

			return cls(tokenizer=lowerCamelCase_		, speaker_embeddings=lowerCamelCase_					)





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







			'''simple docstring'''




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

					SCREAMING_SNAKE_CASE       :     Optional[int]				= {}

					SCREAMING_SNAKE_CASE       :     Any				= save_directory

					for prompt_key in self.speaker_embeddings:
							if prompt_key != "repo_or_path":
									SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self._load_voice_preset(lowerCamelCase_					)

									SCREAMING_SNAKE_CASE       :     List[str]				= {}
									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_		, )
											SCREAMING_SNAKE_CASE       :     str				= os.path.join(lowerCamelCase_		, f"{prompt_key}_{key}.npy"					)

									SCREAMING_SNAKE_CASE       :     Tuple				= tmp_dict

					with open(os.path.join(lowerCamelCase_		, lowerCamelCase_					)		, '''w'''					) as fp:
							json.dump(lowerCamelCase_		, lowerCamelCase_					)

			super().save_pretrained(lowerCamelCase_		, lowerCamelCase_		, **lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :str = None		, **lowerCamelCase_    :Optional[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self.speaker_embeddings[voice_preset]

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {}
			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}]."					)

					SCREAMING_SNAKE_CASE       :     Optional[int]				= 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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."					)

					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.load(lowerCamelCase_					)

			return voice_preset_dict





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







			'''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    :Optional[int]		, lowerCamelCase_    :Union[str, Any]=None		, lowerCamelCase_    :List[Any]=None		, lowerCamelCase_    :List[str]="pt"		, lowerCamelCase_    :Union[str, Any]=2_56		, lowerCamelCase_    :Optional[Any]=False		, lowerCamelCase_    :Optional[int]=True		, lowerCamelCase_    :Tuple=False		, **lowerCamelCase_    :Tuple		, )							->			Union[str, Any]:







			'''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
					):
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self._load_voice_preset(lowerCamelCase_					)

					else:
							if isinstance(lowerCamelCase_		, lowerCamelCase_					) and not voice_preset.endswith('''.npz'''					):
									SCREAMING_SNAKE_CASE       :     List[Any]				= voice_preset + '''.npz'''

							SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.load(lowerCamelCase_					)

			if voice_preset is not None:
					self._validate_voice_preset_dict(lowerCamelCase_		, **lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= BatchFeature(data=lowerCamelCase_		, tensor_type=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= 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:
					SCREAMING_SNAKE_CASE       :     Dict				= voice_preset

			return encoded_text







"""simple docstring"""






import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Any						     =			{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """encodec"""





	def __init__(						self    :List[str]		, lowerCamelCase_    :Tuple=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]		, lowerCamelCase_    :str=2_40_00		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :str=1_28		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :int=1		, lowerCamelCase_    :Dict=[8, 5, 4, 2]		, lowerCamelCase_    :List[Any]="weight_norm"		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]="reflect"		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Any=10_24		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=True		, **lowerCamelCase_    :Optional[int]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= target_bandwidths
			SCREAMING_SNAKE_CASE       :     List[str]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= audio_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= normalize
			SCREAMING_SNAKE_CASE       :     str				= chunk_length_s
			SCREAMING_SNAKE_CASE       :     List[str]				= overlap
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_filters
			SCREAMING_SNAKE_CASE       :     Tuple				= num_residual_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= upsampling_ratios
			SCREAMING_SNAKE_CASE       :     Optional[int]				= norm_type
			SCREAMING_SNAKE_CASE       :     Any				= kernel_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= last_kernel_size
			SCREAMING_SNAKE_CASE       :     Tuple				= residual_kernel_size
			SCREAMING_SNAKE_CASE       :     Any				= dilation_growth_rate
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_causal_conv
			SCREAMING_SNAKE_CASE       :     str				= pad_mode
			SCREAMING_SNAKE_CASE       :     List[Any]				= compress
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_lstm_layers
			SCREAMING_SNAKE_CASE       :     Dict				= trim_right_ratio
			SCREAMING_SNAKE_CASE       :     List[Any]				= codebook_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= codebook_dim if codebook_dim is not None else hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= use_conv_shortcut

			if self.norm_type not in ["weight_norm", "time_group_norm"]:
					raise ValueError(
					    f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"					)

			super().__init__(**lowerCamelCase_					)





	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate					)





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			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					)					)





	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.prod(self.upsampling_ratios					)
			return math.ceil(self.sampling_rate / hop_length					)







	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





