


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  SwinConfig(image_size=1_92				)

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

       a_					:		List[str]							=  window_size
       a_					:		Any							=  embed_dim
       a_					:		Optional[int]							=  depths
       a_					:		List[Any]							=  num_heads

       return config







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		Optional[int]:

       """simple docstring"""

       if "encoder.mask_token" in name:
              a_					:		Dict							=  name.replace('encoder.mask_token'  ,    'embeddings.mask_token'				)
       if "encoder.patch_embed.proj" in name:
              a_					:		int							=  name.replace('encoder.patch_embed.proj'  ,    'embeddings.patch_embeddings.projection'				)
       if "encoder.patch_embed.norm" in name:
              a_					:		Tuple							=  name.replace('encoder.patch_embed.norm'  ,    'embeddings.norm'				)
       if "attn.proj" in name:
              a_					:		int							=  name.replace('attn.proj'  ,    'attention.output.dense'				)
       if "attn" in name:
              a_					:		Union[str, Any]							=  name.replace('attn'  ,    'attention.self'				)
       if "norm1" in name:
              a_					:		Dict							=  name.replace('norm1'  ,    'layernorm_before'				)
       if "norm2" in name:
              a_					:		int							=  name.replace('norm2'  ,    'layernorm_after'				)
       if "mlp.fc1" in name:
              a_					:		Tuple							=  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_					:		Optional[int]							=  'layernorm.weight'
       if name == "encoder.norm.bias":
              a_					:		Union[str, Any]							=  'layernorm.bias'

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

       return name







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]  ,    __A :			Tuple				)	->		List[str]:

       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              a_					:		List[str]							=  orig_state_dict.pop(__A				)

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

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

       return orig_state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Any  ,    __A :			str  ,    __A :			Optional[int]				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		str							=  torch.load(__A  ,    map_location='cpu'				)['model']

       a_					:		Union[str, Any]							=  get_swin_config(__A				)
       a_					:		List[str]							=  SwinForMaskedImageModeling(__A				)
       model.eval()

       a_					:		Dict							=  convert_state_dict(__A  ,    __A				)
       model.load_state_dict(__A				)

       a_					:		List[Any]							=  'http://images.cocodataset.org/val2017/000000039769.jpg'

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

       with torch.no_grad():
              a_					:		str							=  model(**__A				).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(__A				)

              print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
              image_processor.save_pretrained(__A				)

       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_      : List[Any]  					=       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_      : List[Any]  					=       parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_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


__lowerCamelCase       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





      def __init__( self       ,							A_=5_0265       ,							A_=768       ,							A_=12       ,							A_=12       ,							A_=3072       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=2       ,							A_=0.02       ,							A_=1e-12       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_="absolute"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							**A_   )

       UpperCamelCase	:					Optional[int]       	=   vocab_size
       UpperCamelCase	:					Dict       	=   hidden_size
       UpperCamelCase	:					str       	=   num_hidden_layers
       UpperCamelCase	:					Any       	=   num_attention_heads
       UpperCamelCase	:					List[str]       	=   hidden_act
       UpperCamelCase	:					Optional[Any]       	=   intermediate_size
       UpperCamelCase	:					Tuple       	=   hidden_dropout_prob
       UpperCamelCase	:					Tuple       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Tuple       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   type_vocab_size
       UpperCamelCase	:					int       	=   initializer_range
       UpperCamelCase	:					str       	=   layer_norm_eps
       UpperCamelCase	:					Dict       	=   position_embedding_type
       UpperCamelCase	:					Any       	=   use_cache
       UpperCamelCase	:					Union[str, Any]       	=   classifier_dropout



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )
