




from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class       _A				(		__lowercase     ):
		__a					    =       42




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE=True						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			torch.nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[0]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			nn.ModuleList([]			)

								# down
								_UpperCAmelCase      =			block_out_channels[0]
								for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			block_out_channels[i]
														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_down_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		add_downsample=not is_final_block						,		resnet_eps=1e-6						,		downsample_padding=0						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)
														self.down_blocks.append(_SCREAMING_SNAKE_CASE			)

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default"""						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# out
								_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[-1]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()

								_UpperCAmelCase      =			2 * out_channels if double_z else out_channels
								_UpperCAmelCase      =			nn.Convad(block_out_channels[-1]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			x
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														# down
														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE			)

								else:
														# down
														for down_block in self.down_blocks:
																				_UpperCAmelCase      =			down_block(_SCREAMING_SNAKE_CASE			)

														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE			)

								# post-process
								_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE="group"						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[-1]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			nn.ModuleList([]			)

								_UpperCAmelCase      =			in_channels if norm_type == """spatial""" else None

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# up
								_UpperCAmelCase      =			list(reversed(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			reversed_block_out_channels[0]
								for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			reversed_block_out_channels[i]

														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_up_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block + 1						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		prev_output_channel=_SCREAMING_SNAKE_CASE						,		add_upsample=not is_final_block						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		resnet_time_scale_shift=_SCREAMING_SNAKE_CASE						,		)
														self.up_blocks.append(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			output_channel

								# out
								if norm_type == "spatial":
														_UpperCAmelCase      =			SpatialNorm(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[0]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()
								_UpperCAmelCase      =			nn.Convad(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			z
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			next(iter(self.up_blocks.parameters()			)			).dtype
								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								else:
														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

														# up
														for up_block in self.up_blocks:
																				_UpperCAmelCase      =			up_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

        # post-process
								if latent_embeds is None:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="random"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True			):
								super().__init__()
								_UpperCAmelCase      =			n_e
								_UpperCAmelCase      =			vq_embed_dim
								_UpperCAmelCase      =			beta
								_UpperCAmelCase      =			legacy

								_UpperCAmelCase      =			nn.Embedding(self.n_e						,		self.vq_embed_dim			)
								self.embedding.weight.data.uniform_(-1.0 / self.n_e						,		1.0 / self.n_e			)

								_UpperCAmelCase      =			remap
								if self.remap is not None:
														self.register_buffer("""used"""						,		torch.tensor(np.load(self.remap			)			)			)
														_UpperCAmelCase      =			self.used.shape[0]
														_UpperCAmelCase      =			unknown_index  # "random" or "extra" or integer
														if self.unknown_index == "extra":
																				_UpperCAmelCase      =			self.re_embed
																				_UpperCAmelCase      =			self.re_embed + 1
														print(
														    F"Remapping {self.n_e} indices to {self.re_embed} indices. "
														    F"Using {self.unknown_index} for unknown indices."			)
								else:
														_UpperCAmelCase      =			n_e

								_UpperCAmelCase      =			sane_index_shape






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			(inds[:, :, None] == used[None, None, ...]).long()
								_UpperCAmelCase      =			match.argmax(-1			)
								_UpperCAmelCase      =			match.sum(2			) < 1
								if self.unknown_index == "random":
														_UpperCAmelCase      =			torch.randint(0						,		self.re_embed						,		size=new[unknown].shape			).to(device=new.device			)
								else:
														_UpperCAmelCase      =			self.unknown_index
								return new.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								if self.re_embed > self.used.shape[0]:  # extra token
														_UpperCAmelCase      =			0  # simply set to zero
								_UpperCAmelCase      =			torch.gather(used[None, :][inds.shape[0] * [0], :]						,		1						,		_SCREAMING_SNAKE_CASE			)
								return back.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								# reshape z -> (batch, height, width, channel) and flatten
								_UpperCAmelCase      =			z.permute(0						,		2						,		3						,		1			).contiguous()
								_UpperCAmelCase      =			z.view(-1						,		self.vq_embed_dim			)

								# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
								_UpperCAmelCase      =			torch.argmin(torch.cdist(_SCREAMING_SNAKE_CASE						,		self.embedding.weight			)						,		dim=1			)

								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			).view(z.shape			)
								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			None

								# compute loss for embedding
								if not self.legacy:
														_UpperCAmelCase      =			self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
								else:
														_UpperCAmelCase      =			torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

								# preserve gradients
								_UpperCAmelCase      =			z + (z_q - z).detach()

								# reshape back to match original input shape
								_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								if self.remap is not None:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z.shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.remap_to_used(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			min_encoding_indices.reshape(-1						,		1			)  # flatten

								if self.sane_index_shape:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z_q.shape[0]						,		z_q.shape[2]						,		z_q.shape[3]			)

								return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# shape specifying (batch, height, width, channel)
								if self.remap is not None:
														_UpperCAmelCase      =			indices.reshape(shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.unmap_to_all(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			indices.reshape(-1			)  # flatten again

								# get quantized latent vectors
								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			)

								if shape is not None:
														_UpperCAmelCase      =			z_q.view(_SCREAMING_SNAKE_CASE			)
														# reshape back to match original input shape
														_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								return z_q




class       _A				(		__lowercase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								_UpperCAmelCase      =			parameters
								_UpperCAmelCase    ,					_UpperCAmelCase      =			torch.chunk(_SCREAMING_SNAKE_CASE						,		2						,		dim=1			)
								_UpperCAmelCase      =			torch.clamp(self.logvar						,		-30.0						,		20.0			)
								_UpperCAmelCase      =			deterministic
								_UpperCAmelCase      =			torch.exp(0.5 * self.logvar			)
								_UpperCAmelCase      =			torch.exp(self.logvar			)
								if self.deterministic:
														_UpperCAmelCase      =			_UpperCAmelCase      =			torch.zeros_like(
														    self.mean						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = None			):
								# make sure sample is on the same device as the parameters and has same dtype
								_UpperCAmelCase      =			randn_tensor(
								    self.mean.shape						,		generator=_SCREAMING_SNAKE_CASE						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)
								_UpperCAmelCase      =			self.mean + self.std * sample
								return x






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								else:
														if other is None:
																				return 0.5 * torch.sum(torch.pow(self.mean						,		2			) + self.var - 1.0 - self.logvar						,		dim=[1, 2, 3]			)
														else:
																				return 0.5 * torch.sum(
																				    torch.pow(self.mean - other.mean						,		2			) / other.var
																				    + self.var / other.var
																				    - 1.0
																				    - self.logvar
																				    + other.logvar						,		dim=[1, 2, 3]						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=[1, 2, 3]			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								_UpperCAmelCase      =			np.log(2.0 * np.pi			)
								return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,		2			) / self.var						,		dim=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								return self.mean





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a													=			logging.get_logger("transformers.models.speecht5")

a													=			{
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
a													=			{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
a													=			{
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
a													=			{
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
a													=			{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a													=			{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a													=			{
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
a													=			{
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
a													=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a													=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a													=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a													=			[]
a													=			[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
						else:
												_UpperCAmelCase      =			hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
												    f" {value.shape} for {full_name}"       )

						if weight_type == "weight":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_g":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_v":
												_UpperCAmelCase      =			value
						elif weight_type == "bias":
												_UpperCAmelCase      =			value
						elif weight_type == "running_mean":
												_UpperCAmelCase      =			value
						elif weight_type == "running_var":
												_UpperCAmelCase      =			value
						elif weight_type == "num_batches_tracked":
												_UpperCAmelCase      =			value
						else:
												_UpperCAmelCase      =			value

						logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						for key in ignore_keys:
												if key.endswith(""".*"""       ):
																		if name.startswith(key[:-1]       ):
																								return True
												elif ".*." in key:
																		_UpperCAmelCase    ,					_UpperCAmelCase      =			key.split(""".*."""       )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			[]

						if task == "s2t":
												_UpperCAmelCase      =			hf_model.speechta.encoder.prenet.feature_encoder
												_UpperCAmelCase      =			MAPPING_S2T
												_UpperCAmelCase      =			IGNORE_KEYS_S2T
						elif task == "t2s":
												_UpperCAmelCase      =			None
												_UpperCAmelCase      =			MAPPING_T2S
												_UpperCAmelCase      =			IGNORE_KEYS_T2S
						elif task == "s2s":
												_UpperCAmelCase      =			hf_model.speechta.encoder.prenet.feature_encoder
												_UpperCAmelCase      =			MAPPING_S2S
												_UpperCAmelCase      =			IGNORE_KEYS_S2S
						else:
												raise ValueError(f"Unsupported task: {task}"       )

						for name, value in fairseq_dict.items():
												if should_ignore(snake_case			,     snake_case       ):
																		logger.info(f"{name} was ignored"       )
																		continue

												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
																		_UpperCAmelCase      =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																								if "*" in key:
																														_UpperCAmelCase    ,					_UpperCAmelCase      =			key.split(""".*."""       )
																														if prefix in name and suffix in name:
																																				_UpperCAmelCase      =			suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														_UpperCAmelCase      =			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														if "weight_g" in name:
																																				_UpperCAmelCase      =			"""weight_g"""
																														elif "weight_v" in name:
																																				_UpperCAmelCase      =			"""weight_v"""
																														elif "bias" in name:
																																				_UpperCAmelCase      =			"""bias"""
																														elif "weight" in name:
																																				_UpperCAmelCase      =			"""weight"""
																														elif "running_mean" in name:
																																				_UpperCAmelCase      =			"""running_mean"""
																														elif "running_var" in name:
																																				_UpperCAmelCase      =			"""running_var"""
																														elif "num_batches_tracked" in name:
																																				_UpperCAmelCase      =			"""num_batches_tracked"""
																														else:
																																				_UpperCAmelCase      =			None
																														set_recursively(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case       )

						logger.warning(f"Unused weights: {unused_weights}"       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			full_name.split("""conv_layers."""       )[-1]
						_UpperCAmelCase      =			name.split("""."""       )
						_UpperCAmelCase      =			int(items[0]       )
						_UpperCAmelCase      =			int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f"{full_name} has size {value.shape}, but"
																								    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
						else:
												unused_weights.append(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=None			,     )						->				Union[str, Any]:
						if config_path is not None:
												_UpperCAmelCase      =			SpeechTaConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			SpeechTaConfig()

						if task == "s2t":
												_UpperCAmelCase      =			config.max_text_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToText(snake_case       )
						elif task == "t2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			6_0_0
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForTextToSpeech(snake_case       )
						elif task == "s2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToSpeech(snake_case       )
						else:
												raise ValueError(f"Unknown task name: {task}"       )

						if vocab_path:
												_UpperCAmelCase      =			SpeechTaTokenizer(snake_case			,     model_max_length=config.max_text_positions       )

												# Mask token behaves like a normal word, i.e. include the space before it
												_UpperCAmelCase      =			AddedToken("""<mask>"""			,     lstrip=snake_case			,     rstrip=snake_case       )
												_UpperCAmelCase      =			mask_token
												tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
												tokenizer.add_tokens(["""<ctc_blank>"""]       )

						_UpperCAmelCase      =			SpeechTaFeatureExtractor()
						_UpperCAmelCase      =			SpeechTaProcessor(tokenizer=snake_case			,     feature_extractor=snake_case       )
						processor.save_pretrained(snake_case       )

						_UpperCAmelCase      =			torch.load(snake_case       )
						recursively_load_weights(fairseq_checkpoint["""model"""]			,     snake_case			,     snake_case       )

						model.save_pretrained(snake_case       )

						if repo_id:
												print("""Pushing to the hub..."""       )
												processor.push_to_hub(snake_case       )
												model.push_to_hub(snake_case       )


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				parser.add_argument(
				    "--task",
				    default="s2t",
				    type=str,
				    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
				)
				parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
				)

				a													=			parser.parse_args()
				convert_speechta_checkpoint(
				    args.task,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.vocab_path,
				    args.push_to_hub,
				)