



import copy
import os
from typing import Union

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


a_								=       logging.get_logger(__name__)

a_								=       {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""pix2struct_text_model"""
 lowerCAmelCase__							       =	["""past_key_values"""]
 lowerCAmelCase__							       =	{
     """hidden_size""": """hidden_size""",
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }




 def __init__(    self						,					__UpperCAmelCase=50244						,					__UpperCAmelCase=768						,					__UpperCAmelCase=64						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=12						,					__UpperCAmelCase=12						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase="gelu_new"						,					__UpperCAmelCase=0						,					__UpperCAmelCase=False						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					__UpperCAmelCase=False						,					__UpperCAmelCase=True						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     hidden_size
    __lowerCamelCase    				=     d_kv
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     num_layers
    __lowerCamelCase    				=     num_heads
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     layer_norm_epsilon
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     use_cache

    __lowerCamelCase    				=     eos_token_id
    __lowerCamelCase    				=     decoder_start_token_id

    # for backwards compatibility
    __lowerCamelCase    				=     dense_act_fn

    super().__init__(
        pad_token_id=__UpperCAmelCase						,					eos_token_id=__UpperCAmelCase						,					decoder_start_token_id=__UpperCAmelCase						,					tie_word_embeddings=__UpperCAmelCase						,					is_decoder=__UpperCAmelCase						,					**__UpperCAmelCase						,					)




 @classmethod
 def 							lowerCamelCase   (    cls						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    cls._set_token_in_kwargs(__UpperCAmelCase					)

    __lowerCamelCase				,__lowerCamelCase    				=     cls.get_config_dict(__UpperCAmelCase						,					**__UpperCAmelCase					)

    # get the text config dict if we are loading from Pix2StructConfig
    if config_dict.get('''model_type'''					) == "pix2struct":
       __lowerCamelCase    				=     config_dict['''text_config''']

    if "model_type" in config_dict and hasattr(cls						,					'''model_type'''					) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
           F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

    return cls.from_dict(__UpperCAmelCase						,					**__UpperCAmelCase					)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""pix2struct_vision_model"""




 def __init__(    self						,					__UpperCAmelCase=768						,					__UpperCAmelCase=768						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=64						,					__UpperCAmelCase=12						,					__UpperCAmelCase=12						,					__UpperCAmelCase="gelu_new"						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=0.0						,					__UpperCAmelCase=0.0						,					__UpperCAmelCase=1E-1_0						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase=4096						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase					)

    __lowerCamelCase    				=     hidden_size
    __lowerCamelCase    				=     patch_embed_hidden_size
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     num_hidden_layers
    __lowerCamelCase    				=     num_attention_heads
    __lowerCamelCase    				=     initializer_range
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     attention_dropout
    __lowerCamelCase    				=     layer_norm_eps
    __lowerCamelCase    				=     dense_act_fn
    __lowerCamelCase    				=     seq_len
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     d_kv




 @classmethod
 def 							lowerCamelCase   (    cls						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    cls._set_token_in_kwargs(__UpperCAmelCase					)

    __lowerCamelCase				,__lowerCamelCase    				=     cls.get_config_dict(__UpperCAmelCase						,					**__UpperCAmelCase					)

    # get the vision config dict if we are loading from Pix2StructConfig
    if config_dict.get('''model_type'''					) == "pix2struct":
       __lowerCamelCase    				=     config_dict['''vision_config''']

    if "model_type" in config_dict and hasattr(cls						,					'''model_type'''					) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
           F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

    return cls.from_dict(__UpperCAmelCase						,					**__UpperCAmelCase					)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""pix2struct"""
 lowerCAmelCase__							       =	True




 def __init__(    self						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase=0.02						,					__UpperCAmelCase=False						,					__UpperCAmelCase=False						,					__UpperCAmelCase=True						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(tie_word_embeddings=__UpperCAmelCase						,					is_encoder_decoder=__UpperCAmelCase						,					**__UpperCAmelCase					)

    if text_config is None:
       __lowerCamelCase    				=     {}
       logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''					)

    if vision_config is None:
       __lowerCamelCase    				=     {}
       logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''					)

    __lowerCamelCase    				=     PixaStructTextConfig(**__UpperCAmelCase					)
    __lowerCamelCase    				=     PixaStructVisionConfig(**__UpperCAmelCase					)

    __lowerCamelCase    				=     self.text_config.decoder_start_token_id
    __lowerCamelCase    				=     self.text_config.pad_token_id
    __lowerCamelCase    				=     self.text_config.eos_token_id

    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     initializer_range

    __lowerCamelCase    				=     self.initializer_range
    __lowerCamelCase    				=     self.initializer_range

    __lowerCamelCase    				=     is_vqa




 @classmethod
 def 							lowerCamelCase   (    cls						,					__UpperCAmelCase						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    return cls(text_config=text_config.to_dict()						,					vision_config=vision_config.to_dict()						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     copy.deepcopy(self.__dict__					)
    __lowerCamelCase    				=     self.text_config.to_dict()
    __lowerCamelCase    				=     self.vision_config.to_dict()
    __lowerCamelCase    				=     self.__class__.model_type
    return output











import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





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

   return name





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict				):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase    				=     orig_state_dict.pop(_UpperCamelCase				)

      if "qkv" in key:
         __lowerCamelCase    				=     key.split('''.'''				)
         __lowerCamelCase    				=     int(key_split[1]				)
         if "decoder_blocks" in key:
            __lowerCamelCase    				=     config.decoder_hidden_size
            __lowerCamelCase    				=     '''decoder.decoder_layers.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]
         else:
            __lowerCamelCase    				=     config.hidden_size
            __lowerCamelCase    				=     '''vit.encoder.layer.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]

      else:
         __lowerCamelCase    				=     val

   return orig_state_dict





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     ViTMAEConfig()
   if "large" in checkpoint_url:
      __lowerCamelCase    				=     10_24
      __lowerCamelCase    				=     40_96
      __lowerCamelCase    				=     24
      __lowerCamelCase    				=     16
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     14
      __lowerCamelCase    				=     12_80
      __lowerCamelCase    				=     51_20
      __lowerCamelCase    				=     32
      __lowerCamelCase    				=     16

   __lowerCamelCase    				=     ViTMAEForPreTraining(_UpperCamelCase				)

   __lowerCamelCase    				=     torch.hub.load_state_dict_from_url(_UpperCamelCase			,map_location='''cpu'''				)['''model''']

   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)

   __lowerCamelCase    				=     convert_state_dict(_UpperCamelCase			,_UpperCamelCase				)

   model.load_state_dict(_UpperCamelCase				)
   model.eval()

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

   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				)
   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)
   __lowerCamelCase    				=     image_processor(images=_UpperCamelCase			,return_tensors='''pt'''				)

   # forward pass
   torch.manual_seed(2				)
   __lowerCamelCase    				=     model(**_UpperCamelCase				)
   __lowerCamelCase    				=     outputs.logits

   if "large" in checkpoint_url:
      __lowerCamelCase    				=     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:
      __lowerCamelCase    				=     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:
      __lowerCamelCase    				=     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]			,_UpperCamelCase			,atol=1e-4				)

   print(F"""Saving model to {pytorch_dump_folder_path}"""				)
   model.save_pretrained(_UpperCamelCase				)

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


if __name__ == "__main__":
  a_								=       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."""
  )

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






