



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


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					"""AutoTokenizer"""
							UpperCamelCase__				:      List[str]         =					["""tokenizer"""]

							UpperCamelCase__				:      Any         =					{
							    """semantic_prompt""": 1,
							    """coarse_prompt""": 2,
							    """fine_prompt""": 2,
							}


							def __init__(							self  ,  a_  ,  a_=None		):
											super().__init__(a_		)

											a__				      =		speaker_embeddings


							@classmethod
							def _a      (							cls  ,  a_  ,  a_="speaker_embeddings_path.json"  ,  **a_		):

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

											a__				      =		AutoTokenizer.from_pretrained(a_  ,  **a_		)

											return cls(tokenizer=a_  ,  speaker_embeddings=a_		)


							def _a      (							self  ,  a_  ,  a_="speaker_embeddings_path.json"  ,  a_="speaker_embeddings"  ,  a_ = False  ,  **a_  ,  ):
											if self.speaker_embeddings is not None:
															os.makedirs(os.path.join(a_  ,  a_  ,  """v2"""		)  ,  exist_ok=a_		)

															a__				      =		{}

															a__				      =		save_directory

															for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																							a__				      =		self._load_voice_preset(a_		)

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

																							a__				      =		tmp_dict

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

											super().save_pretrained(a_  ,  a_  ,  **a_		)


							def _a      (							self  ,  a_ = None  ,  **a_		):
											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"""  ,  a_		)  ,  cache_dir=kwargs.pop("""cache_dir"""  ,  a_		)  ,  force_download=kwargs.pop("""force_download"""  ,  a_		)  ,  proxies=kwargs.pop("""proxies"""  ,  a_		)  ,  resume_download=kwargs.pop("""resume_download"""  ,  a_		)  ,  local_files_only=kwargs.pop("""local_files_only"""  ,  a_		)  ,  use_auth_token=kwargs.pop("""use_auth_token"""  ,  a_		)  ,  revision=kwargs.pop("""revision"""  ,  a_		)  ,  )
															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(a_		)

											return voice_preset_dict


							def _a      (							self  ,  a_ = None		):
											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  ,  a_=None  ,  a_=None  ,  a_="pt"  ,  a_=256  ,  a_=False  ,  a_=True  ,  a_=False  ,  **a_  ,  ):
											if voice_preset is not None and not isinstance(a_  ,  a_		):
															if (
															    isinstance(a_  ,  a_		)
															    and self.speaker_embeddings is not None
															    and voice_preset in self.speaker_embeddings
															):
																			a__				      =		self._load_voice_preset(a_		)

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

																			a__				      =		np.load(a_		)

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

											a__				      =		self.tokenizer(
											    a_  ,  return_tensors=a_  ,  padding="""max_length"""  ,  max_length=a_  ,  return_attention_mask=a_  ,  return_token_type_ids=a_  ,  add_special_tokens=a_  ,  **a_  ,  )

											if voice_preset is not None:
															a__				      =		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  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@register_to_config
							def __init__(							self  ,  a_ = 128  ,  a_ = 256  ,  a_ = 2_000.0  ,  a_ = 768  ,  a_ = 12  ,  a_ = 12  ,  a_ = 64  ,  a_ = 2_048  ,  a_ = 0.1  ,  ):
											super().__init__()

											a__				      =		nn.Sequential(
											    nn.Linear(a_  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  nn.Linear(d_model * 4  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  )

											a__				      =		nn.Embedding(a_  ,  a_		)
											a__				      =		False

											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)

											a__				      =		nn.Dropout(p=a_		)

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

											a__				      =		TaLayerNorm(a_		)

											a__				      =		nn.Dropout(p=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		torch.mul(query_input.unsqueeze(-1		)  ,  key_input.unsqueeze(-2		)		)
											return mask.unsqueeze(-3		)







							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__      ,			a__      ,			a__				      =		decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

											# decoder_noise_time is in [0, 1), so rescale to expected timing range.
											a__				      =		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		)

											a__				      =		self.conditioning_emb(a_		).unsqueeze(1		)

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

											a__				      =		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.
											a__				      =		torch.broadcast_to(
											    torch.arange(a_  ,  device=decoder_input_tokens.device		)  ,  (batch, seq_length)  ,  )

											a__				      =		self.position_encoding(a_		)

											a__				      =		self.continuous_inputs_projection(a_		)
											inputs += position_encodings
											a__				      =		self.dropout(a_		)

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

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

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

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

											a__				      =		self.decoder_norm(a_		)
											a__				      =		self.post_dropout(a_		)

											a__				      =		self.spec_out(a_		)
											return spec_out




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.ModuleList()

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

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

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







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer[0](
											    a_  ,  conditioning_emb=a_  ,  attention_mask=a_  ,  )

											if encoder_hidden_states is not None:
															a__				      =		torch.where(encoder_attention_mask > 0  ,  0  ,  -1E1_0		).to(
															    encoder_hidden_states.dtype		)

															a__				      =		self.layer[1](
															    a_  ,  key_value_states=a_  ,  attention_mask=a_  ,  )

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

											return (hidden_states,)




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaLayerNorm(a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											# pre_self_attention_layer_norm
											a__				      =		self.layer_norm(a_		)

											if conditioning_emb is not None:
															a__				      =		self.FiLMLayer(a_  ,  a_		)

											# Self-attention block
											a__				      =		self.attention(a_		)

											a__				      =		hidden_states + self.dropout(a_		)

											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer_norm(a_		)
											a__				      =		self.attention(
											    a_  ,  encoder_hidden_states=a_  ,  attention_mask=attention_mask.squeeze(1		)  ,  )
											a__				      =		hidden_states + self.dropout(a_		)
											return layer_output




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaDenseGatedActDense(d_model=a_  ,  d_ff=a_  ,  dropout_rate=a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None		):
											a__				      =		self.layer_norm(a_		)
											if conditioning_emb is not None:
															a__				      =		self.film(a_  ,  a_		)

											a__				      =		self.DenseReluDense(a_		)
											a__				      =		hidden_states + self.dropout(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Dropout(a_		)
											a__				      =		NewGELUActivation()







							def _a      (							self  ,  a_		):
											a__				      =		self.act(self.wi_a(a_		)		)
											a__				      =		self.wi_a(a_		)
											a__				      =		hidden_gelu * hidden_linear
											a__				      =		self.dropout(a_		)

											a__				      =		self.wo(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.Parameter(torch.ones(a_		)		)
											a__				      =		eps







							def _a      (							self  ,  a_		):
											# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
											# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
											# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
											# half-precision inputs is done in fp32

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

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

											return self.weight * hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def _a      (							self  ,  a_		):
											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044_715 * torch.pow(a_  ,  3.0		))		))




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  out_features * 2  ,  bias=a_		)







							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		self.scale_bias(a_		)
											a__      ,			a__				      =		torch.chunk(a_  ,  2  ,  -1		)
											a__				      =		x * (1 + scale) + shift
											return x

