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_      =      logging.get_logger(__name__)






class      __lowercase					(    _snake_case    ):
						lowercase					 =     """AutoTokenizer"""
						lowercase					 =     ["""tokenizer"""]

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

						def __init__(	self				:      int							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple=None   )							->		List[Any]:


									'''simple docstring'''

									super().__init__(snake_case_   )

									lowercase							=		speaker_embeddings

						@classmethod
						def        __a		(	cls				:      Dict							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]="speaker_embeddings_path.json"							,							**__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									if speaker_embeddings_dict_path is not None:
												lowercase							=		get_file_from_repo(
												    snake_case_							,							snake_case_							,							subfolder=kwargs.pop('''subfolder'''							,							snake_case_   )							,							cache_dir=kwargs.pop('''cache_dir'''							,							snake_case_   )							,							force_download=kwargs.pop('''force_download'''							,							snake_case_   )							,							proxies=kwargs.pop('''proxies'''							,							snake_case_   )							,							resume_download=kwargs.pop('''resume_download'''							,							snake_case_   )							,							local_files_only=kwargs.pop('''local_files_only'''							,							snake_case_   )							,							use_auth_token=kwargs.pop('''use_auth_token'''							,							snake_case_   )							,							revision=kwargs.pop('''revision'''							,							snake_case_   )							,							)
												if speaker_embeddings_path is None:
															logger.warning(
															    f'`{os.path.join(snake_case_							,							snake_case_   )}` 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`.'   )
															lowercase							=		None
												else:
															with open(snake_case_   ) as speaker_embeddings_json:
																		lowercase							=		json.load(snake_case_   )
									else:
												lowercase							=		None

									lowercase							=		AutoTokenizer.from_pretrained(snake_case_							,							**snake_case_   )

									return cls(tokenizer=snake_case_							,							speaker_embeddings=snake_case_   )

						def        __a		(	self				:      int							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[int]="speaker_embeddings_path.json"							,							__lowerCamelCase				:      Any="speaker_embeddings"							,							__lowerCamelCase				:      int = False							,							**__lowerCamelCase				:      Optional[Any]							,							)							->		List[str]:


									'''simple docstring'''

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

												lowercase							=		{}

												lowercase							=		save_directory

												for prompt_key in self.speaker_embeddings:
															if prompt_key != "repo_or_path":
																		lowercase							=		self._load_voice_preset(snake_case_   )

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

																		lowercase							=		tmp_dict

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

									super().save_pretrained(snake_case_							,							snake_case_							,							**snake_case_   )

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Any = None							,							**__lowerCamelCase				:      Union[str, Any]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		self.speaker_embeddings[voice_preset]

									lowercase							=		{}
									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}].'   )

												lowercase							=		get_file_from_repo(
												    self.speaker_embeddings.get('''repo_or_path'''							,							'''/'''   )							,							voice_preset_paths[key]							,							subfolder=kwargs.pop('''subfolder'''							,							snake_case_   )							,							cache_dir=kwargs.pop('''cache_dir'''							,							snake_case_   )							,							force_download=kwargs.pop('''force_download'''							,							snake_case_   )							,							proxies=kwargs.pop('''proxies'''							,							snake_case_   )							,							resume_download=kwargs.pop('''resume_download'''							,							snake_case_   )							,							local_files_only=kwargs.pop('''local_files_only'''							,							snake_case_   )							,							use_auth_token=kwargs.pop('''use_auth_token'''							,							snake_case_   )							,							revision=kwargs.pop('''revision'''							,							snake_case_   )							,							)
												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.'   )

												lowercase							=		np.load(snake_case_   )

									return voice_preset_dict

						def        __a		(	self				:      int							,							__lowerCamelCase				:      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				:      List[Any]							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      Tuple="pt"							,							__lowerCamelCase				:      Dict=2_56							,							__lowerCamelCase				:      Optional[int]=False							,							__lowerCamelCase				:      List[Any]=True							,							__lowerCamelCase				:      Optional[int]=False							,							**__lowerCamelCase				:      Dict							,							)							->		Tuple:


									'''simple docstring'''

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

												else:
															if isinstance(snake_case_							,							snake_case_   ) and not voice_preset.endswith('''.npz'''   ):
																		lowercase							=		voice_preset + ".npz"

															lowercase							=		np.load(snake_case_   )

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

									lowercase							=		self.tokenizer(
									    snake_case_							,							return_tensors=snake_case_							,							padding='''max_length'''							,							max_length=snake_case_							,							return_attention_mask=snake_case_							,							return_token_type_ids=snake_case_							,							add_special_tokens=snake_case_							,							**snake_case_							,							)

									if voice_preset is not None:
												lowercase							=		voice_preset

									return encoded_text






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin






class      __lowercase					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      int = 1_28							,							__lowerCamelCase				:      int = 2_56							,							__lowerCamelCase				:      float = 2000.0							,							__lowerCamelCase				:      int = 7_68							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 64							,							__lowerCamelCase				:      int = 20_48							,							__lowerCamelCase				:      float = 0.1							,							)							->		Optional[int]:


									'''simple docstring'''

									super().__init__()

									lowercase							=		nn.Sequential(
									    nn.Linear(__lowerCamelCase							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							)

									lowercase							=		nn.Embedding(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		False

									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )

									lowercase							=		nn.ModuleList()
									for lyr_num in range(__lowerCamelCase   ):
												# FiLM conditional T5 decoder
												lowercase							=		DecoderLayer(d_model=__lowerCamelCase							,							d_kv=__lowerCamelCase							,							num_heads=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
												self.decoders.append(__lowerCamelCase   )

									lowercase							=		TaLayerNorm(__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		torch.mul(query_input.unsqueeze(-1   )							,							key_input.unsqueeze(-2   )   )
									return mask.unsqueeze(-3   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

									# decoder_noise_time is in [0, 1), so rescale to expected timing range.
									lowercase							=		get_timestep_embedding(
									    decoder_noise_time * self.config.max_decoder_noise_time							,							embedding_dim=self.config.d_model							,							max_period=self.config.max_decoder_noise_time							,							).to(dtype=self.dtype   )

									lowercase							=		self.conditioning_emb(__lowerCamelCase   ).unsqueeze(1   )

									assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

									lowercase							=		decoder_input_tokens.shape[1]

									# If we want to use relative positions for audio context, we can just offset
									# this sequence by the length of encodings_and_masks.
									lowercase							=		torch.broadcast_to(
									    torch.arange(__lowerCamelCase							,							device=decoder_input_tokens.device   )							,							(batch, seq_length)							,							)

									lowercase							=		self.position_encoding(__lowerCamelCase   )

									lowercase							=		self.continuous_inputs_projection(__lowerCamelCase   )
									inputs += position_encodings
									lowercase							=		self.dropout(__lowerCamelCase   )

									# decoder: No padding present.
									lowercase							=		torch.ones(
									    decoder_input_tokens.shape[:2]							,							device=decoder_input_tokens.device							,							dtype=inputs.dtype   )

									# Translate encoding masks to encoder-decoder masks.
									lowercase							=		[(x, self.encoder_decoder_mask(__lowerCamelCase							,							__lowerCamelCase   )) for x, y in encodings_and_masks]

									# cross attend style: concat encodings
									lowercase							=		torch.cat([x[0] for x in encodings_and_encdec_masks]							,							dim=1   )
									lowercase							=		torch.cat([x[1] for x in encodings_and_encdec_masks]							,							dim=-1   )

									for lyr in self.decoders:
												lowercase							=		lyr(
												    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							encoder_attention_mask=__lowerCamelCase							,							)[0]

									lowercase							=		self.decoder_norm(__lowerCamelCase   )
									lowercase							=		self.post_dropout(__lowerCamelCase   )

									lowercase							=		self.spec_out(__lowerCamelCase   )
									return spec_out






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]=1E-6   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.ModuleList()

									# cond self attention: layer 0
									self.layer.append(
									    TaLayerSelfAttentionCond(d_model=__lowerCamelCase							,							d_kv=__lowerCamelCase							,							num_heads=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )   )

									# cross attention: layer 1
									self.layer.append(
									    TaLayerCrossAttention(
									        d_model=__lowerCamelCase							,							d_kv=__lowerCamelCase							,							num_heads=__lowerCamelCase							,							dropout_rate=__lowerCamelCase							,							layer_norm_epsilon=__lowerCamelCase							,							)   )

									# Film Cond MLP + dropout: last layer
									self.layer.append(
									    TaLayerFFCond(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase							,							layer_norm_epsilon=__lowerCamelCase   )   )



						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[Any]=None							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=None							,							)							->		Any:


									'''simple docstring'''

									lowercase							=		self.layer[0](
									    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

									if encoder_hidden_states is not None:
												lowercase							=		torch.where(encoder_attention_mask > 0							,							0							,							-1E10   ).to(
												    encoder_hidden_states.dtype   )

												lowercase							=		self.layer[1](
												    __lowerCamelCase							,							key_value_states=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

									# Apply Film Conditional Feed Forward layer
									lowercase							=		self.layer[-1](__lowerCamelCase							,							__lowerCamelCase   )

									return (hidden_states,)






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaLayerNorm(__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )

									if conditioning_emb is not None:
												lowercase							=		self.FiLMLayer(__lowerCamelCase							,							__lowerCamelCase   )

									# Self-attention block
									lowercase							=		self.attention(__lowerCamelCase   )

									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )

									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									super().__init__()
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									lowercase							=		self.attention(
									    __lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							attention_mask=attention_mask.squeeze(1   )							,							)
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return layer_output






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaDenseGatedActDense(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str=None   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									if conditioning_emb is not None:
												lowercase							=		self.film(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		self.DenseReluDense(__lowerCamelCase   )
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )
									lowercase							=		NewGELUActivation()



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.act(self.wi_a(__lowerCamelCase   )   )
									lowercase							=		self.wi_a(__lowerCamelCase   )
									lowercase							=		hidden_gelu * hidden_linear
									lowercase							=		self.dropout(__lowerCamelCase   )

									lowercase							=		self.wo(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=1E-6   )							->		Optional[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Parameter(torch.ones(__lowerCamelCase   )   )
									lowercase							=		eps



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		hidden_states.to(torch.floataa   ).pow(2   ).mean(-1							,							keepdim=__lowerCamelCase   )
									lowercase							=		hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

									# convert into half-precision if necessary
									if self.weight.dtype in [torch.floataa, torch.bfloataa]:
												lowercase							=		hidden_states.to(self.weight.dtype   )

									return self.weight * hidden_states






class      __lowercase					(    nn.Module    ):

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      torch.Tensor   )							->		torch.Tensor:


									'''simple docstring'''

									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_4715 * torch.pow(__lowerCamelCase							,							3.0   ))   ))






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							out_features * 2							,							bias=__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self.scale_bias(__lowerCamelCase   )
									lowercase		,lowercase							=		torch.chunk(__lowerCamelCase							,							2							,							-1   )
									lowercase							=		x * (1 + scale) + shift
									return x





