



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_UpperCAmelCase     =			{
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					'albert'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=3_0000       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					List[str]=4096       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Dict=1_6384       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					str="gelu_new"       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=512       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					List[Any]=0.0_2       ,		lowerCAmelCase :					Union[str, Any]=1E-12       ,		lowerCAmelCase :					Tuple=0.1       ,		lowerCAmelCase :					List[Any]="absolute"       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					Optional[int]=3       ,		**lowerCAmelCase :					int       ,		)     ->					Tuple:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: List[str]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=embedding_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=inner_group_num
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_act
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Any     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=max_position_embeddings
     SCREAMING_SNAKE_CASE_: Any     			=type_vocab_size
     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Dict     			=classifier_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=position_embedding_type







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




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








"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def      __magic_name__				(							lowercase ):
   if "cls_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""cls_token"""							,			"""vit.embeddings.cls_token""" )
   if "mask_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""mask_token"""							,			"""decoder.mask_token""" )
   if "decoder_pos_embed" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""decoder_pos_embed"""							,			"""decoder.decoder_pos_embed""" )
   if "pos_embed" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""pos_embed"""							,			"""vit.embeddings.position_embeddings""" )
   if "patch_embed.proj" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""patch_embed.proj"""							,			"""vit.embeddings.patch_embeddings.projection""" )
   if "patch_embed.norm" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""patch_embed.norm"""							,			"""vit.embeddings.norm""" )
   if "decoder_blocks" in name:
      SCREAMING_SNAKE_CASE_: List[Any]     			=name.replace("""decoder_blocks"""							,			"""decoder.decoder_layers""" )
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""blocks"""							,			"""vit.encoder.layer""" )
   if "attn.proj" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""attn.proj"""							,			"""attention.output.dense""" )
   if "attn" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""attn"""							,			"""attention.self""" )
   if "norm1" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""norm1"""							,			"""layernorm_before""" )
   if "norm2" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""norm2"""							,			"""layernorm_after""" )
   if "mlp.fc1" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""mlp.fc1"""							,			"""intermediate.dense""" )
   if "mlp.fc2" in name:
      SCREAMING_SNAKE_CASE_: Dict     			=name.replace("""mlp.fc2"""							,			"""output.dense""" )
   if "decoder_embed" in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""decoder_embed"""							,			"""decoder.decoder_embed""" )
   if "decoder_norm" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""decoder_norm"""							,			"""decoder.decoder_norm""" )
   if "decoder_pred" in name:
      SCREAMING_SNAKE_CASE_: Any     			=name.replace("""decoder_pred"""							,			"""decoder.decoder_pred""" )
   if "norm.weight" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.weight"""							,			"""vit.layernorm.weight""" )
   if "norm.bias" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.bias"""							,			"""vit.layernorm.bias""" )

   return name





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

      if "qkv" in key:
         SCREAMING_SNAKE_CASE_: Dict     			=key.split(""".""" )
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=int(key_split[1] )
         if "decoder_blocks" in key:
            SCREAMING_SNAKE_CASE_: int     			=config.decoder_hidden_size
            SCREAMING_SNAKE_CASE_: Optional[int]     			="""decoder.decoder_layers."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Dict     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: str     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[-dim:]
         else:
            SCREAMING_SNAKE_CASE_: Any     			=config.hidden_size
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""vit.encoder.layer."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: Dict     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Any     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[-dim:]

      else:
         SCREAMING_SNAKE_CASE_: Tuple     			=val

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=ViTMAEConfig()
   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: List[Any]     			=1024
      SCREAMING_SNAKE_CASE_: Dict     			=4096
      SCREAMING_SNAKE_CASE_: Tuple     			=24
      SCREAMING_SNAKE_CASE_: int     			=16
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=14
      SCREAMING_SNAKE_CASE_: Any     			=1280
      SCREAMING_SNAKE_CASE_: Dict     			=5120
      SCREAMING_SNAKE_CASE_: Optional[int]     			=32
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=16

   SCREAMING_SNAKE_CASE_: Tuple     			=ViTMAEForPreTraining(lowercase )

   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.hub.load_state_dict_from_url(lowercase							,			map_location="""cpu""" )["""model"""]

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=ViTMAEImageProcessor(size=config.image_size )

   SCREAMING_SNAKE_CASE_: str     			=convert_state_dict(lowercase							,			lowercase )

   model.load_state_dict(lowercase )
   model.eval()

   SCREAMING_SNAKE_CASE_: Tuple     			="""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   SCREAMING_SNAKE_CASE_: int     			=ViTMAEImageProcessor(size=config.image_size )
   SCREAMING_SNAKE_CASE_: int     			=image_processor(images=lowercase							,			return_tensors="""pt""" )

   # forward pass
   torch.manual_seed(2 )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(**lowercase )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=outputs.logits

   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Dict     			=torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
   else:
      SCREAMING_SNAKE_CASE_: Any     			=torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )

   # verify logits
   assert torch.allclose(logits[0, :3, :3]							,			lowercase							,			atol=1e-4 )

   print(f'''Saving model 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 __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
       type=str,
       help="""URL of the checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



