"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__A  =				logging.get_logger(__name__)

__A  =				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__A  =				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def    lowercase_   (   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Optional[Any]      ) ->      Union[str, Any]:



							'''simple docstring'''

							for attribute in key.split("."      ):
														__lowerCamelCase					:		Optional[Any]		   =     getattr(a_						,   a_      )

							if weight_type is not None:
														__lowerCamelCase					:		Optional[int]		   =     getattr(a_						,   a_      ).shape
							else:
														__lowerCamelCase					:		int		   =     hf_pointer.shape

							assert hf_shape == value.shape, (
							    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":
														__lowerCamelCase					:		Any		   =     value
							elif weight_type == "weight_g":
														__lowerCamelCase					:		str		   =     value
							elif weight_type == "weight_v":
														__lowerCamelCase					:		str		   =     value
							elif weight_type == "bias":
														__lowerCamelCase					:		Dict		   =     value
							else:
														__lowerCamelCase					:		List[str]		   =     value

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


def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	int      ) ->      Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[str]		   =     []
							__lowerCamelCase					:		Tuple		   =     fairseq_model.state_dict()

							__lowerCamelCase					:		Optional[int]		   =     hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							__lowerCamelCase					:		Union[str, Any]		   =     None

							for name, value in fairseq_dict.items():
														__lowerCamelCase					:		List[str]		   =     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    a_						,   a_						,   a_						,   a_						,   hf_model.config.feat_extract_norm == "group"						,   )
																					__lowerCamelCase					:		Optional[int]		   =     True
														elif name.split("."      )[0] == "proj":
																					__lowerCamelCase					:		Any		   =     fairseq_model.proj
																					__lowerCamelCase					:		int		   =     True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
																																			__lowerCamelCase					:		Tuple		   =     True
																																			if "*" in mapped_key:
																																										__lowerCamelCase					:		str		   =     name.split(a_      )[0].split("."      )[-2]
																																										__lowerCamelCase					:		int		   =     mapped_key.replace("*"						,   a_      )
																																			if "weight_g" in name:
																																										__lowerCamelCase					:		Optional[int]		   =     '''weight_g'''
																																			elif "weight_v" in name:
																																										__lowerCamelCase					:		Union[str, Any]		   =     '''weight_v'''
																																			elif "bias" in name:
																																										__lowerCamelCase					:		Optional[Any]		   =     '''bias'''
																																			elif "weight" in name:
																																										__lowerCamelCase					:		str		   =     '''weight'''
																																			else:
																																										__lowerCamelCase					:		List[str]		   =     None
																																			set_recursively(a_						,   a_						,   a_						,   a_						,   a_      )
																												continue
														if not is_used:
																					unused_weights.append(a_      )

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

							return proj_weight


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	Tuple      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     full_name.split("conv_layers."      )[-1]
							__lowerCamelCase					:		Union[str, Any]		   =     name.split("."      )
							__lowerCamelCase					:		Dict		   =     int(items[0]      )
							__lowerCamelCase					:		Optional[int]		   =     int(items[1]      )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					__lowerCamelCase					:		List[str]		   =     value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					__lowerCamelCase					:		str		   =     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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					__lowerCamelCase					:		Tuple		   =     value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					__lowerCamelCase					:		Dict		   =     value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
							else:
														unused_weights.append(a_      )


def    lowercase_   (   _lowerCamelCase:	Tuple      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		List[str]		   =     emb.weight.shape
							__lowerCamelCase					:		int		   =     nn.Linear(a_						,   a_						,   bias=a_      )
							__lowerCamelCase					:		Any		   =     emb.weight.data
							return lin_layer


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]      ) ->      List[str]:



							'''simple docstring'''

							with open(a_						,   "r"						,   encoding="utf-8"      ) as f:
														__lowerCamelCase					:		Union[str, Any]		   =     f.readlines()
														__lowerCamelCase					:		Union[str, Any]		   =     [line.split(" "      )[0] for line in lines]

							__lowerCamelCase					:		Tuple		   =     len(a_      )

							__lowerCamelCase					:		Optional[Any]		   =     {
							    '''<s>''': 0,
							    '''<pad>''': 1,
							    '''</s>''': 2,
							    '''<unk>''': 3,
							}

							vocab_dict.update(dict(zip(a_						,   range(4						,   num_words + 4      )      )      )      )
							return vocab_dict


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[str]						,   ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     WavaVecaConfig.from_pretrained(a_      )
							__lowerCamelCase					:		List[str]		   =     SpeechaTextaConfig.from_pretrained(
							    a_						,   vocab_size=a_						,   decoder_layers=a_						,   do_stable_layer_norm=a_      )

							__lowerCamelCase					:		Optional[int]		   =     WavaVecaFeatureExtractor(
							    feature_size=1						,   sampling_rate=16000						,   padding_value=0						,   do_normalize=a_						,   return_attention_mask=a_						,   )

							__lowerCamelCase					:		Any		   =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]						,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
							__lowerCamelCase					:		Tuple		   =     model[0].eval()

							# set weights for wav2vec2 encoder
							__lowerCamelCase					:		Dict		   =     WavaVecaModel(a_      )
							__lowerCamelCase					:		List[Any]		   =     recursively_load_weights_wavaveca(model.encoder						,   a_      )

							__lowerCamelCase					:		int		   =     SpeechaTextaForCausalLM(a_      )
							__lowerCamelCase					:		Union[str, Any]		   =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,   strict=a_      )

							# set output linear layer
							unexpected_keys.remove("embed_out"      )
							__lowerCamelCase					:		List[str]		   =     nn.Parameter(model.decoder.embed_out.detach()      )

							# layer norm is init to identity matrix so leaving it is fine
							logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
							logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

							__lowerCamelCase					:		Optional[Any]		   =     SpeechEncoderDecoderModel(encoder=a_						,   decoder=a_      )
							__lowerCamelCase					:		Optional[int]		   =     False

							# add projection layer
							__lowerCamelCase					:		Any		   =     nn.Parameter(projection_layer.weight      )
							__lowerCamelCase					:		str		   =     nn.Parameter(projection_layer.bias      )

							__lowerCamelCase					:		Any		   =     create_vocab_dict(a_      )

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

							__lowerCamelCase					:		List[Any]		   =     SpeechaTextaTokenizer(os.path.join(a_						,   "vocab.json"      )      )
							tokenizer.save_pretrained(a_      )

							__lowerCamelCase					:		int		   =     hf_wavavec.config.to_dict()
							__lowerCamelCase					:		Tuple		   =     tokenizer.pad_token_id
							__lowerCamelCase					:		Tuple		   =     tokenizer.bos_token_id
							__lowerCamelCase					:		str		   =     tokenizer.eos_token_id
							__lowerCamelCase					:		str		   =     '''speech_to_text_2'''
							__lowerCamelCase					:		Any		   =     '''wav2vec2'''

							__lowerCamelCase					:		Optional[int]		   =     SpeechEncoderDecoderConfig.from_dict(a_      )

							hf_wavavec.save_pretrained(a_      )
							feature_extractor.save_pretrained(a_      )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
				parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
				parser.add_argument(
				    '''--encoder_config_path''',
				    default='''facebook/wav2vec2-large-lv60''',
				    type=str,
				    help='''Path to hf encoder wav2vec2 checkpoint config''',
				)
				parser.add_argument(
				    '''--decoder_config_path''',
				    default='''facebook/s2t-small-mustc-en-fr-st''',
				    type=str,
				    help='''Path to hf decoder s2t checkpoint config''',
				)
				parser.add_argument('''--vocab_size''', default=10224, type=int, help='''Vocab size of decoder''')
				parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

				__A  =				parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    vocab_size=args.vocab_size,
				    num_decoder_layers=args.num_decoder_layers,
				)
"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__A  =				logging.get_logger('''transformers.models.speecht5''')


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates      )      ):
														__lowerCamelCase					:		Dict		   =     checkpoint[F"""upsamples.{i}.1.weight_g"""]
														__lowerCamelCase					:		Optional[int]		   =     checkpoint[F"""upsamples.{i}.1.weight_v"""]
														__lowerCamelCase					:		Any		   =     checkpoint[F"""upsamples.{i}.1.bias"""]

							for i in range(len(config.upsample_rates      ) * len(config.resblock_kernel_sizes      )      ):
														for j in range(len(config.resblock_dilation_sizes      )      ):
																					__lowerCamelCase					:		List[str]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					__lowerCamelCase					:		Union[str, Any]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

							__lowerCamelCase					:		Any		   =     checkpoint["output_conv.1.weight_g"]
							__lowerCamelCase					:		Tuple		   =     checkpoint["output_conv.1.weight_v"]
							__lowerCamelCase					:		int		   =     checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		List[str]		   =     torch.from_numpy(_lowerCamelCase      ).float()

							model.save_pretrained(_lowerCamelCase      )

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


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
				parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
				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_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)