


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		hf_model.apply_weight_norm()

		lowerCAmelCase__       :      List[str]   					= checkpoint['''input_conv.weight_g''']
		lowerCAmelCase__       :      List[str]   					= checkpoint['''input_conv.weight_v''']
		lowerCAmelCase__       :      Any   					= checkpoint['''input_conv.bias''']

		for i in range(len(config.upsample_rates	)	):
				lowerCAmelCase__       :      int   					= checkpoint[f'upsamples.{i}.1.weight_g']
				lowerCAmelCase__       :      Optional[int]   					= checkpoint[f'upsamples.{i}.1.weight_v']
				lowerCAmelCase__       :      Dict   					= 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__       :      Tuple   					= checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
						lowerCAmelCase__       :      int   					= checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
						lowerCAmelCase__       :      Any   					= checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

						lowerCAmelCase__       :      Union[str, Any]   					= checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
						lowerCAmelCase__       :      List[str]   					= checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
						lowerCAmelCase__       :      Union[str, Any]   					= checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

		lowerCAmelCase__       :      str   					= checkpoint['''output_conv.1.weight_g''']
		lowerCAmelCase__       :      Dict   					= checkpoint['''output_conv.1.weight_v''']
		lowerCAmelCase__       :      Dict   					= checkpoint['''output_conv.1.bias''']

		hf_model.remove_weight_norm()






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None     ,       _a=None     ,       ):




		"""simple docstring"""






		if config_path is not None:
				lowerCAmelCase__       :      List[Any]   					= SpeechTaHifiGanConfig.from_pretrained(_a	)
		else:
				lowerCAmelCase__       :      Union[str, Any]   					= SpeechTaHifiGanConfig()

		lowerCAmelCase__       :      Optional[int]   					= SpeechTaHifiGan(_a	)

		lowerCAmelCase__       :      Optional[int]   					= torch.load(_a	)
		load_weights(orig_checkpoint['''model''']['''generator''']     ,       _a     ,       _a	)

		lowerCAmelCase__       :      List[str]   					= np.load(_a	)
		lowerCAmelCase__       :      int   					= stats[0].reshape(-1	)
		lowerCAmelCase__       :      Optional[Any]   					= stats[1].reshape(-1	)
		lowerCAmelCase__       :      Any   					= torch.from_numpy(_a	).float()
		lowerCAmelCase__       :      Optional[Any]   					= 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__":
		lowerCamelCase						=		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.'''
		)

		lowerCamelCase						=		parser.parse_args()
		convert_hifigan_checkpoint(
		    args.checkpoint_path,
		    args.stats_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class  _a							(    _lowercase):
	_a  :  Optional[int]				   =							'''xlm-roberta-xl'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		str=25_0880		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2560		, _SCREAMING_SNAKE_CASE						:		int=36		, _SCREAMING_SNAKE_CASE						:		Optional[int]=32		, _SCREAMING_SNAKE_CASE						:		Any=1_0240		, _SCREAMING_SNAKE_CASE						:		List[str]="gelu"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=514		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1		, _SCREAMING_SNAKE_CASE						:		Tuple=0.02		, _SCREAMING_SNAKE_CASE						:		Dict=1E-05		, _SCREAMING_SNAKE_CASE						:		Tuple=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Tuple=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="absolute"		, _SCREAMING_SNAKE_CASE						:		Any=True		, _SCREAMING_SNAKE_CASE						:		Any=None		, **_SCREAMING_SNAKE_CASE						:		Tuple		, )->	str:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      int   					= num_hidden_layers
			lowerCAmelCase__       :      Dict   					= num_attention_heads
			lowerCAmelCase__       :      str   					= hidden_act
			lowerCAmelCase__       :      List[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      List[str]   					= max_position_embeddings
			lowerCAmelCase__       :      Any   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      Optional[int]   					= layer_norm_eps
			lowerCAmelCase__       :      Optional[int]   					= position_embedding_type
			lowerCAmelCase__       :      Any   					= use_cache
			lowerCAmelCase__       :      List[Any]   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Any		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      int   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[Any]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			    ]		)




