
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__		:List[Any]			  =   logging.get_logger("transformers.models.speecht5")



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					hf_model.apply_weight_norm()

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

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

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

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

					hf_model.remove_weight_norm()



@torch.no_grad()
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=None			,      lowerCAmelCase__=None			,      ):
					'''simple docstring'''
					if config_path is not None:
										lowercase						     =						SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__		)
					else:
										lowercase						     =						SpeechTaHifiGanConfig()

					lowercase						     =						SpeechTaHifiGan(lowerCAmelCase__		)

					lowercase						     =						torch.load(lowerCAmelCase__		)
					load_weights(orig_checkpoint['''model''']['''generator''']			,      lowerCAmelCase__			,      lowerCAmelCase__		)

					lowercase						     =						np.load(lowerCAmelCase__		)
					lowercase						     =						stats[0].reshape(-1		)
					lowercase						     =						stats[1].reshape(-1		)
					lowercase						     =						torch.from_numpy(lowerCAmelCase__		).float()
					lowercase						     =						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__":
							lowercase__		:List[str]			  =   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__		:str			  =   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


lowercase__		:Optional[int]			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''camembert'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-12       ,A__=1       ,A__=0       ,A__=2       ,A__="absolute"       ,A__=True       ,A__=None       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						use_cache
												lowercase						     =						classifier_dropout







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							@property
							def 			A__				(    self):
												if self.task == "multiple-choice":
																	lowercase						     =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	lowercase						     =						{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ])




