




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	A ( _SCREAMING_SNAKE_CASE       )      ->					int:
       lowerCamelCase  : Union[str, Any]     					=   SwinConfig(image_size=192       )

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

       lowerCamelCase  : int     					=   window_size
       lowerCamelCase  : Optional[int]     					=   embed_dim
       lowerCamelCase  : List[Any]     					=   depths
       lowerCamelCase  : Dict     					=   num_heads

       return config

def 	A ( _SCREAMING_SNAKE_CASE       )      ->					Dict:
       if "encoder.mask_token" in name:
              lowerCamelCase  : str     					=   name.replace("encoder.mask_token"					,"embeddings.mask_token"       )
       if "encoder.patch_embed.proj" in name:
              lowerCamelCase  : Optional[int]     					=   name.replace("encoder.patch_embed.proj"					,"embeddings.patch_embeddings.projection"       )
       if "encoder.patch_embed.norm" in name:
              lowerCamelCase  : str     					=   name.replace("encoder.patch_embed.norm"					,"embeddings.norm"       )
       if "attn.proj" in name:
              lowerCamelCase  : Optional[Any]     					=   name.replace("attn.proj"					,"attention.output.dense"       )
       if "attn" in name:
              lowerCamelCase  : str     					=   name.replace("attn"					,"attention.self"       )
       if "norm1" in name:
              lowerCamelCase  : Any     					=   name.replace("norm1"					,"layernorm_before"       )
       if "norm2" in name:
              lowerCamelCase  : int     					=   name.replace("norm2"					,"layernorm_after"       )
       if "mlp.fc1" in name:
              lowerCamelCase  : str     					=   name.replace("mlp.fc1"					,"intermediate.dense"       )
       if "mlp.fc2" in name:
              lowerCamelCase  : Dict     					=   name.replace("mlp.fc2"					,"output.dense"       )

       if name == "encoder.norm.weight":
              lowerCamelCase  : str     					=   'layernorm.weight'
       if name == "encoder.norm.bias":
              lowerCamelCase  : List[Any]     					=   'layernorm.bias'

       if "decoder" in name:
              pass
       else:
              lowerCamelCase  : List[str]     					=   'swin.' + name

       return name

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE       )      ->					List[str]:
       for key in orig_state_dict.copy().keys():
              lowerCamelCase  : List[str]     					=   orig_state_dict.pop(__snake_case       )

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

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

       return orig_state_dict

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE       )      ->					Union[str, Any]:
       lowerCamelCase  : Optional[int]     					=   torch.load(__snake_case					,map_location="cpu"       )['model']

       lowerCamelCase  : str     					=   get_swin_config(__snake_case       )
       lowerCamelCase  : str     					=   SwinForMaskedImageModeling(__snake_case       )
       model.eval()

       lowerCamelCase  : Tuple     					=   convert_state_dict(__snake_case					,__snake_case       )
       model.load_state_dict(__snake_case       )

       lowerCamelCase  : List[str]     					=   'http://images.cocodataset.org/val2017/000000039769.jpg'

       lowerCamelCase  : Optional[int]     					=   ViTImageProcessor(size={"height": 192, "width": 192}       )
       lowerCamelCase  : Tuple     					=   Image.open(requests.get(__snake_case					,stream=__snake_case       ).raw       )
       lowerCamelCase  : List[Any]     					=   image_processor(images=__snake_case					,return_tensors="pt"       )

       with torch.no_grad():
              lowerCamelCase  : List[str]     					=   model(**__snake_case       ).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(__snake_case       )

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

       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__":
       SCREAMING_SNAKE_CASE__			: int    =  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.'
       )

       SCREAMING_SNAKE_CASE__			: List[Any]    =  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 torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : str         =       [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

     if k.startswith('encoder'     ):
          __A		:					Any						=			k.replace('.attn'					,				'.self_attn'     )
          __A		:					Any						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          __A		:					Tuple						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'encoder_attn_layer_norm'     )
          __A		:					int						=			k.replace('norm3'					,				'final_layer_norm'     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					Optional[int]						=			[
         'model.encoder.layernorm_embedding.weight',
         'model.encoder.layernorm_embedding.bias',
         'model.decoder.layernorm_embedding.weight',
         'model.decoder.layernorm_embedding.bias',
     ]
     for k in keys:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[Any]     )	->	int:
     __A		:					List[str]						=			torch.load(__snake_case					,				map_location='cpu'     )
     __A		:					Tuple						=			model['model']
     __A		:					str						=			BlenderbotConfig.from_json_file(__snake_case     )
     __A		:					int						=			BlenderbotForConditionalGeneration(__snake_case     )
     __A		:					List[Any]						=			m.model.state_dict().keys()
     __A		:					Optional[int]						=			[]
     __A		:					Optional[int]						=			{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __A		:					Union[str, Any]						=			rename_state_dict_key(__snake_case     )
          if new_k not in valid_keys:
               failures.append([k, new_k]     )
          else:
               __A		:					Optional[Any]						=			v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__snake_case     )
     m.model.load_state_dict(__snake_case					,				strict=__snake_case     )
     m.half()
     m.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
  parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
  parser.add_argument(
      '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)