from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class 					lowercase__						(   _lowerCamelCase):
						UpperCamelCase_     =		None
						UpperCamelCase_     =		None
						UpperCamelCase_     =		None
						UpperCamelCase_     =		None






class 					lowercase__						(   _lowerCamelCase):


						def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Any=1    ,		UpperCamelCase__	:				Union[str, Any]=0    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Dict=512    ,		UpperCamelCase__	:				List[str]="cls"    ,		UpperCamelCase__	:				Tuple=False    ,		UpperCamelCase__	:				Optional[int]=True    ,		**UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





												super().__init__(pad_token_id=lowerCAmelCase_    ,		bos_token_id=lowerCAmelCase_    ,		eos_token_id=lowerCAmelCase_    ,		**lowerCAmelCase_						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							project_dim
												SCREAMING_SNAKE_CASE							:    Dict							=							pooler_fn
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							learn_encoder
												SCREAMING_SNAKE_CASE							:    int							=							use_attention_mask






class 					lowercase__						(   _lowerCamelCase):
						UpperCamelCase_     =		[r"""pooler""", r"""logit_scale"""]
						UpperCamelCase_     =		[r"""position_ids""", r"""predictions.decoder.bias"""]
						UpperCamelCase_     =		"""roberta"""
						UpperCamelCase_     =		RobertaSeriesConfig


						def __init__(       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





												'''simple docstring'''





												super().__init__(lowerCAmelCase_						)
												SCREAMING_SNAKE_CASE							:    int							=							XLMRobertaModel(lowerCAmelCase_						)
												SCREAMING_SNAKE_CASE							:    int							=							nn.Linear(config.hidden_size    ,		config.project_dim						)
												SCREAMING_SNAKE_CASE							:    int							=							getattr(lowerCAmelCase_    ,		'''has_pre_transformation'''    ,		lowerCAmelCase_						)
												if self.has_pre_transformation:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							nn.Linear(config.hidden_size    ,		config.project_dim						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							nn.LayerNorm(config.hidden_size    ,		eps=config.layer_norm_eps						)
												self.post_init()



						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				int = None    ,		UpperCamelCase__	:				int = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				int = None    ,		UpperCamelCase__	:				str = None    ,		UpperCamelCase__	:				str = None    ,		UpperCamelCase__	:				int = None    ,		UpperCamelCase__	:				Tuple = None    ,		UpperCamelCase__	:				str = None    ,		UpperCamelCase__	:				int = None    ,		UpperCamelCase__	:				Dict = None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							return_dict if return_dict is not None else self.config.use_return_dict

												SCREAMING_SNAKE_CASE							:    Tuple							=							self.base_model(
												    input_ids=lowerCAmelCase_    ,		attention_mask=lowerCAmelCase_    ,		token_type_ids=lowerCAmelCase_    ,		position_ids=lowerCAmelCase_    ,		head_mask=lowerCAmelCase_    ,		inputs_embeds=lowerCAmelCase_    ,		encoder_hidden_states=lowerCAmelCase_    ,		encoder_attention_mask=lowerCAmelCase_    ,		output_attentions=lowerCAmelCase_    ,		output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,		return_dict=lowerCAmelCase_    ,		)

												if self.has_pre_transformation:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							outputs['''hidden_states'''][-2]
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.pre_LN(lowerCAmelCase_						)
																		SCREAMING_SNAKE_CASE							:    str							=							self.transformation_pre(lowerCAmelCase_						)

																		return TransformationModelOutput(
																		    projection_state=lowerCAmelCase_    ,		last_hidden_state=outputs.last_hidden_state    ,		hidden_states=outputs.hidden_states    ,		attentions=outputs.attentions    ,		)
												else:
																		SCREAMING_SNAKE_CASE							:    int							=							self.transformation(outputs.last_hidden_state						)
																		return TransformationModelOutput(
																		    projection_state=lowerCAmelCase_    ,		last_hidden_state=outputs.last_hidden_state    ,		hidden_states=outputs.hidden_states    ,		attentions=outputs.attentions    ,		)







'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase    :							List[str]      =					logging.get_logger("transformers.models.speecht5")







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	hf_model.apply_weight_norm()

	_snake_case   						=		checkpoint['input_conv.weight_g']
	_snake_case   						=		checkpoint['input_conv.weight_v']
	_snake_case   						=		checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates	)	):
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_g']
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_v']
		_snake_case   						=		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	)	):
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

	_snake_case   						=		checkpoint['output_conv.1.weight_g']
	_snake_case   						=		checkpoint['output_conv.1.weight_v']
	_snake_case   						=		checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A=None			,				__A=None			,				)       ->	List[Any]:
	if config_path is not None:
		_snake_case   						=		SpeechTaHifiGanConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		SpeechTaHifiGanConfig()

	_snake_case   						=		SpeechTaHifiGan(__A	)

	_snake_case   						=		torch.load(__A	)
	load_weights(orig_checkpoint['model']['generator']			,				__A			,				__A	)

	_snake_case   						=		np.load(__A	)
	_snake_case   						=		stats[0].reshape(-1	)
	_snake_case   						=		stats[1].reshape(-1	)
	_snake_case   						=		torch.from_numpy(__A	).float()
	_snake_case   						=		torch.from_numpy(__A	).float()

	model.save_pretrained(__A	)

	if repo_id:
		print('Pushing to the hub...'	)
		model.push_to_hub(__A	)


if __name__ == "__main__":
					lowercase    :							Dict      =					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."
					)

					lowercase    :							List[Any]      =					parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)

