



"""simple docstring"""
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase     =			logging.get_logger(__name__)

_UpperCAmelCase     =			{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				int     						=					'sew'



  def __init__(						self :					Any       ,		lowerCAmelCase :					Tuple=32       ,		lowerCAmelCase :					Any=768       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					List[Any]=3072       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					str="gelu"       ,		lowerCAmelCase :					int=0.1       ,		lowerCAmelCase :					Dict=0.1       ,		lowerCAmelCase :					Union[str, Any]=0.1       ,		lowerCAmelCase :					str=0.0       ,		lowerCAmelCase :					str=0.1       ,		lowerCAmelCase :					Optional[Any]=0.1       ,		lowerCAmelCase :					Optional[Any]=0.0_2       ,		lowerCAmelCase :					Optional[Any]=1E-5       ,		lowerCAmelCase :					Dict="group"       ,		lowerCAmelCase :					List[str]="gelu"       ,		lowerCAmelCase :					Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,		lowerCAmelCase :					str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,		lowerCAmelCase :					List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,		lowerCAmelCase :					Optional[int]=False       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					int=16       ,		lowerCAmelCase :					List[str]=True       ,		lowerCAmelCase :					Optional[Any]=0.0_5       ,		lowerCAmelCase :					Optional[Any]=10       ,		lowerCAmelCase :					Union[str, Any]=2       ,		lowerCAmelCase :					List[Any]=0.0       ,		lowerCAmelCase :					str=10       ,		lowerCAmelCase :					int=0       ,		lowerCAmelCase :					Dict="mean"       ,		lowerCAmelCase :					Any=False       ,		lowerCAmelCase :					Dict=False       ,		lowerCAmelCase :					Optional[Any]=256       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Union[str, Any]=1       ,		lowerCAmelCase :					Tuple=2       ,		**lowerCAmelCase :					Dict       ,		)     ->					str:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase       ,		pad_token_id=lowerCAmelCase       ,		bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=hidden_size
     SCREAMING_SNAKE_CASE_: Dict     			=feat_extract_norm
     SCREAMING_SNAKE_CASE_: Dict     			=feat_extract_activation
     SCREAMING_SNAKE_CASE_: Tuple     			=list(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=list(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=list(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=conv_bias
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_conv_pos_embeddings
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_conv_pos_embedding_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=len(self.conv_dim		)
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Tuple     			=squeeze_factor
     SCREAMING_SNAKE_CASE_: Dict     			=hidden_act
     SCREAMING_SNAKE_CASE_: Tuple     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=hidden_dropout
     SCREAMING_SNAKE_CASE_: int     			=attention_dropout
     SCREAMING_SNAKE_CASE_: Dict     			=activation_dropout
     SCREAMING_SNAKE_CASE_: List[str]     			=feat_proj_dropout
     SCREAMING_SNAKE_CASE_: Dict     			=final_dropout
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=layerdrop
     SCREAMING_SNAKE_CASE_: Any     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=initializer_range
     SCREAMING_SNAKE_CASE_: int     			=vocab_size

     if (
         (len(self.conv_stride		) != self.num_feat_extract_layers)
         or (len(self.conv_kernel		) != self.num_feat_extract_layers)
         or (len(self.conv_dim		) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            """Configuration for convolutional layers is incorrect."""
            """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
            f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
            f'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     SCREAMING_SNAKE_CASE_: Dict     			=apply_spec_augment
     SCREAMING_SNAKE_CASE_: Optional[int]     			=mask_time_prob
     SCREAMING_SNAKE_CASE_: Optional[int]     			=mask_time_length
     SCREAMING_SNAKE_CASE_: Dict     			=mask_time_min_masks
     SCREAMING_SNAKE_CASE_: Dict     			=mask_feature_prob
     SCREAMING_SNAKE_CASE_: Tuple     			=mask_feature_length
     SCREAMING_SNAKE_CASE_: Tuple     			=mask_feature_min_masks

     # ctc loss
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ctc_loss_reduction
     SCREAMING_SNAKE_CASE_: List[Any]     			=ctc_zero_infinity

     # sequence classification
     SCREAMING_SNAKE_CASE_: str     			=use_weighted_layer_sum
     SCREAMING_SNAKE_CASE_: List[str]     			=classifier_proj_size





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


     '''simple docstring'''




     return functools.reduce(operator.mul       ,		self.conv_stride       ,		1		)








"""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)



