

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			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     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

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


if __name__ == "__main__":
 lowercase__         =      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__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )







'''simple docstring'''

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase__         =      {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase__         =      logging.get_logger(__name__)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """maskformer"""
     UpperCAmelCase_						:     Union[str, Any]  		=  {"""hidden_size""": """mask_feature_size"""}
     UpperCAmelCase_						:     Union[str, Any]  		=  ["""resnet""", """swin"""]
     UpperCAmelCase_						:     Optional[int]  		=  ["""detr"""]


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					int = 256					,      lowercase_			:					int = 256					,      lowercase_			:					float = 0.1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					float = 0.02					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 20.0					,      lowercase_			:					Optional[bool] = None					,      **lowercase_			:					int					,      )						->     Optional[Any]:
      if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       UpperCAmelCase	:     List[Any]						 =			SwinConfig(
           image_size=384					,      in_channels=3					,      patch_size=4					,      embed_dim=128					,      depths=[2, 2, 18, 2]					,      num_heads=[4, 8, 16, 32]					,      window_size=12					,      drop_path_rate=0.3					,      out_features=['stage1', 'stage2', 'stage3', 'stage4']					,      )

      if isinstance(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			backbone_config.pop('model_type'	)
       UpperCAmelCase	:     Optional[Any]						 =			CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase	:     List[Any]						 =			config_class.from_dict(lowercase_	)

      # verify that the backbone is supported
      if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
           f"""Supported model types: {",".join(self.backbones_supported	)}"""	)

      if decoder_config is None:
       # fall back to https://huggingface.co/facebook/detr-resnet-50
       UpperCAmelCase	:     Dict						 =			DetrConfig()
      else:
       # verify that the decoder is supported
       UpperCAmelCase	:     Tuple						 =			(
           decoder_config.pop('model_type'	) if isinstance(lowercase_					,      lowercase_	) else decoder_config.model_type
       )
       if decoder_type not in self.decoders_supported:
        raise ValueError(
            f"""Transformer Decoder {decoder_type} not supported, please use one of"""
            f""" {",".join(self.decoders_supported	)}"""	)
       if isinstance(lowercase_					,      lowercase_	):
        UpperCAmelCase	:     Union[str, Any]						 =			CONFIG_MAPPING[decoder_type]
        UpperCAmelCase	:     str						 =			config_class.from_dict(lowercase_	)

      UpperCAmelCase	:     int						 =			backbone_config
      UpperCAmelCase	:     Tuple						 =			decoder_config
      # main feature dimension for the model
      UpperCAmelCase	:     Union[str, Any]						 =			fpn_feature_size
      UpperCAmelCase	:     Optional[Any]						 =			mask_feature_size
      # initializer
      UpperCAmelCase	:     Union[str, Any]						 =			init_std
      UpperCAmelCase	:     List[str]						 =			init_xavier_std
      # Hungarian matcher && loss
      UpperCAmelCase	:     int						 =			cross_entropy_weight
      UpperCAmelCase	:     Tuple						 =			dice_weight
      UpperCAmelCase	:     int						 =			mask_weight
      UpperCAmelCase	:     Any						 =			use_auxiliary_loss
      UpperCAmelCase	:     Tuple						 =			no_object_weight
      UpperCAmelCase	:     str						 =			output_auxiliary_logits

      UpperCAmelCase	:     Dict						 =			self.decoder_config.encoder_attention_heads
      UpperCAmelCase	:     Union[str, Any]						 =			self.decoder_config.num_hidden_layers
      super().__init__(**lowercase_	)


     @classmethod
     def        UpperCAmelCase_	(		cls			:					Any					,      lowercase_			:					PretrainedConfig					,      lowercase_			:					PretrainedConfig					,      **lowercase_			:					Optional[Any]	)						->     str:
      return cls(
          backbone_config=lowercase_					,      decoder_config=lowercase_					,      **lowercase_					,      )




     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict[str, any]:
      UpperCAmelCase	:     Optional[int]						 =			copy.deepcopy(self.__dict__	)
      UpperCAmelCase	:     List[str]						 =			self.backbone_config.to_dict()
      UpperCAmelCase	:     Any						 =			self.decoder_config.to_dict()
      UpperCAmelCase	:     Union[str, Any]						 =			self.__class__.model_type
      return output




