



"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=SwinConfig(image_size=192 )

   if "base" in model_name:
      SCREAMING_SNAKE_CASE_: str     			=6
      SCREAMING_SNAKE_CASE_: Any     			=128
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=(2, 2, 18, 2)
      SCREAMING_SNAKE_CASE_: Any     			=(4, 8, 16, 32)
   elif "large" in model_name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=12
      SCREAMING_SNAKE_CASE_: Optional[int]     			=192
      SCREAMING_SNAKE_CASE_: int     			=(2, 2, 18, 2)
      SCREAMING_SNAKE_CASE_: Any     			=(6, 12, 24, 48)
   else:
      raise ValueError("""Model not supported, only supports base and large variants""" )

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=window_size
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=embed_dim
   SCREAMING_SNAKE_CASE_: Tuple     			=depths
   SCREAMING_SNAKE_CASE_: str     			=num_heads

   return config





def      __magic_name__				(							lowercase ):
   if "encoder.mask_token" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""encoder.mask_token"""							,			"""embeddings.mask_token""" )
   if "encoder.patch_embed.proj" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""encoder.patch_embed.proj"""							,			"""embeddings.patch_embeddings.projection""" )
   if "encoder.patch_embed.norm" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""encoder.patch_embed.norm"""							,			"""embeddings.norm""" )
   if "attn.proj" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""attn.proj"""							,			"""attention.output.dense""" )
   if "attn" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""attn"""							,			"""attention.self""" )
   if "norm1" in name:
      SCREAMING_SNAKE_CASE_: Dict     			=name.replace("""norm1"""							,			"""layernorm_before""" )
   if "norm2" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""norm2"""							,			"""layernorm_after""" )
   if "mlp.fc1" in name:
      SCREAMING_SNAKE_CASE_: List[Any]     			=name.replace("""mlp.fc1"""							,			"""intermediate.dense""" )
   if "mlp.fc2" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""mlp.fc2"""							,			"""output.dense""" )

   if name == "encoder.norm.weight":
      SCREAMING_SNAKE_CASE_: List[str]     			="""layernorm.weight"""
   if name == "encoder.norm.bias":
      SCREAMING_SNAKE_CASE_: int     			="""layernorm.bias"""

   if "decoder" in name:
      pass
   else:
      SCREAMING_SNAKE_CASE_: str     			="""swin.""" + name

   return name





def      __magic_name__				(							lowercase							,			lowercase ):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: int     			=orig_state_dict.pop(lowercase )

      if "attn_mask" in key:
         pass
      elif "qkv" in key:
         SCREAMING_SNAKE_CASE_: Dict     			=key.split(""".""" )
         SCREAMING_SNAKE_CASE_: List[Any]     			=int(key_split[2] )
         SCREAMING_SNAKE_CASE_: Tuple     			=int(key_split[4] )
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            SCREAMING_SNAKE_CASE_: Any     			=val[:dim, :]
            SCREAMING_SNAKE_CASE_: Any     			=val[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE_: Dict     			=val[-dim:, :]
         else:
            SCREAMING_SNAKE_CASE_: Tuple     			=val[
                :dim
            ]
            SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[
                dim : dim * 2
            ]
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			=val[
                -dim:
            ]
      else:
         SCREAMING_SNAKE_CASE_: str     			=val

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.load(lowercase							,			map_location="""cpu""" )["""model"""]

   SCREAMING_SNAKE_CASE_: Tuple     			=get_swin_config(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=SwinForMaskedImageModeling(lowercase )
   model.eval()

   SCREAMING_SNAKE_CASE_: Tuple     			=convert_state_dict(lowercase							,			lowercase )
   model.load_state_dict(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			="""http://images.cocodataset.org/val2017/000000039769.jpg"""

   SCREAMING_SNAKE_CASE_: Dict     			=ViTImageProcessor(size={"""height""": 192, """width""": 192} )
   SCREAMING_SNAKE_CASE_: int     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   SCREAMING_SNAKE_CASE_: Tuple     			=image_processor(images=lowercase							,			return_tensors="""pt""" )

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_: Any     			=model(**lowercase ).logits

   print(outputs.keys() )
   print("""Looks ok!""" )

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
      model.save_pretrained(lowercase )

      print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
      image_processor.save_pretrained(lowercase )

   if push_to_hub:
      print(f'''Pushing model and image processor for {model_name} to hub''' )
      model.push_to_hub(f'''microsoft/{model_name}''' )
      image_processor.push_to_hub(f'''microsoft/{model_name}''' )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""swin-base-simmim-window6-192""",
       type=str,
       choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
       help="""Name of the Swin SimMIM model you'd like to convert.""",
   )
   parser.add_argument(
       """--checkpoint_path""",
       default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
       type=str,
       help="""Path to the original PyTorch checkpoint (.pth file).""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[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 ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: List[Any]     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""output_conv.1.weight_g"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint["""output_conv.1.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

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


if __name__ == "__main__":
   _UpperCAmelCase     =			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."""
   )

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



