





'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	int:
     __A		:					Union[str, Any]						=			SwinConfig(image_size=1_92     )

     if "base" in model_name:
          __A		:					Any						=			6
          __A		:					Tuple						=			1_28
          __A		:					str						=			(2, 2, 18, 2)
          __A		:					Dict						=			(4, 8, 16, 32)
     elif "large" in model_name:
          __A		:					str						=			12
          __A		:					Tuple						=			1_92
          __A		:					str						=			(2, 2, 18, 2)
          __A		:					List[Any]						=			(6, 12, 24, 48)
     else:
          raise ValueError('Model not supported, only supports base and large variants'     )

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

     return config






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

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

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

     return name






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					Any     )	->	List[str]:
     for key in orig_state_dict.copy().keys():
          __A		:					List[str]						=			orig_state_dict.pop(__snake_case     )

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

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

     return orig_state_dict






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					Optional[int]						=			torch.load(__snake_case					,				map_location='cpu'     )['model']

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

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

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

     __A		:					Optional[int]						=			ViTImageProcessor(size={'height': 1_92, 'width': 1_92}     )
     __A		:					Tuple						=			Image.open(requests.get(__snake_case					,				stream=__snake_case     ).raw     )
     __A		:					List[Any]						=			image_processor(images=__snake_case					,				return_tensors='pt'     )

     with torch.no_grad():
          __A		:					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__":
  lowercase__       : 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.'''
  )

  lowercase__       : 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)