





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase :  int										=  logging.get_logger(__name__)



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=False							,     _lowerCAmelCase=False  )		->				Union[str, Any]:
 UpperCamelCase			:      Any 			=     "backbone." if is_semantic else ""

 UpperCamelCase			:      Tuple 			=     []
 for i in range(config.num_hidden_layers  ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")  )
  rename_keys.append(
      (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")  )
  rename_keys.append(
      (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")  )
  rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")  )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
         (F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
         (F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
         (F"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
     ]  )

 if has_lm_head:
  # mask token + layernorm
  rename_keys.extend(
      [
          ("mask_token", "beit.embeddings.mask_token"),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
      ]  )
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("fc_norm.weight", "beit.pooler.layernorm.weight"),
          ("fc_norm.bias", "beit.pooler.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]  )

 return rename_keys



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=False							,     _lowerCAmelCase=False  )		->				int:
 for i in range(config.num_hidden_layers  ):
  UpperCamelCase			:      Any 			=     "backbone." if is_semantic else ""
  # queries, keys and values
  UpperCamelCase			:      List[Any] 			=     state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""  )
  UpperCamelCase			:      List[str] 			=     state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""  )
  UpperCamelCase			:      Union[str, Any] 			=     state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""  )

  UpperCamelCase			:      Any 			=     in_proj_weight[
      : config.hidden_size, :
  ]
  UpperCamelCase			:      List[str] 			=     q_bias
  UpperCamelCase			:      Any 			=     in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  UpperCamelCase			:      Optional[int] 			=     in_proj_weight[
      -config.hidden_size :, :
  ]
  UpperCamelCase			:      List[str] 			=     v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  UpperCamelCase			:      str 			=     state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""  )
  UpperCamelCase			:      Dict 			=     state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""  )

  UpperCamelCase			:      Optional[Any] 			=     gamma_a
  UpperCamelCase			:      str 			=     gamma_a



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      str 			=     dct.pop(_lowerCAmelCase  )
 UpperCamelCase			:      int 			=     val



def 	A_       (  )		->				int:
 UpperCamelCase			:      Any 			=     "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase			:      List[str] 			=     Image.open(requests.get(_lowerCAmelCase							,     stream=_lowerCAmelCase  ).raw  )
 return im



@torch.no_grad()
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=False  )		->				Union[str, Any]:
 UpperCamelCase			:      Dict 			=     False if "rvlcdip" in checkpoint_url else True
 UpperCamelCase			:      List[str] 			=     BeitConfig(use_absolute_position_embeddings=_lowerCAmelCase							,     use_mask_token=_lowerCAmelCase  )

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  UpperCamelCase			:      Optional[Any] 			=     1024
  UpperCamelCase			:      int 			=     4096
  UpperCamelCase			:      Tuple 			=     24
  UpperCamelCase			:      Union[str, Any] 			=     16

 # labels
 if "rvlcdip" in checkpoint_url:
  UpperCamelCase			:      str 			=     16
  UpperCamelCase			:      Optional[Any] 			=     "huggingface/label-files"
  UpperCamelCase			:      Optional[int] 			=     "rvlcdip-id2label.json"
  UpperCamelCase			:      Optional[int] 			=     json.load(open(hf_hub_download(_lowerCAmelCase							,     _lowerCAmelCase							,     repo_type="dataset"  )							,     "r"  )  )
  UpperCamelCase			:      List[str] 			=     {int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
  UpperCamelCase			:      Dict 			=     idalabel
  UpperCamelCase			:      Union[str, Any] 			=     {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 UpperCamelCase			:      List[str] 			=     torch.hub.load_state_dict_from_url(_lowerCAmelCase							,     map_location="cpu"  )["model"]

 UpperCamelCase			:      Dict 			=     create_rename_keys(_lowerCAmelCase							,     has_lm_head=_lowerCAmelCase  )
 for src, dest in rename_keys:
  rename_key(_lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )
 read_in_q_k_v(_lowerCAmelCase							,     _lowerCAmelCase							,     has_lm_head=_lowerCAmelCase  )

 # load HuggingFace model
 UpperCamelCase			:      Dict 			=     BeitForMaskedImageModeling(_lowerCAmelCase  ) if has_lm_head else BeitForImageClassification(_lowerCAmelCase  )
 model.eval()
 model.load_state_dict(_lowerCAmelCase  )

 # Check outputs on an image
 UpperCamelCase			:      Tuple 			=     BeitImageProcessor(
     size=config.image_size							,     resample=PILImageResampling.BILINEAR							,     do_center_crop=_lowerCAmelCase  )
 UpperCamelCase			:      int 			=     prepare_img()

 UpperCamelCase			:      Union[str, Any] 			=     image_processor(images=_lowerCAmelCase							,     return_tensors="pt"  )
 UpperCamelCase			:      Tuple 			=     encoding["pixel_values"]

 UpperCamelCase			:      Optional[Any] 			=     model(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     outputs.logits

 # verify logits
 UpperCamelCase			:      Tuple 			=     [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_lowerCAmelCase  ), "Shape of logits not as expected"

 Path(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
 print(F"""Saving model to {pytorch_dump_folder_path}"""  )
 model.save_pretrained(_lowerCAmelCase  )
 print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
 image_processor.save_pretrained(_lowerCAmelCase  )

 if push_to_hub:
  if has_lm_head:
   UpperCamelCase			:      List[str] 			=     "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   UpperCamelCase			:      Optional[int] 			=     "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
  image_processor.push_to_hub(
      repo_path_or_name=Path(_lowerCAmelCase							,     _lowerCAmelCase  )							,     organization="nielsr"							,     commit_message="Add image processor"							,     use_temp_dir=_lowerCAmelCase							,     )
  model.push_to_hub(
      repo_path_or_name=Path(_lowerCAmelCase							,     _lowerCAmelCase  )							,     organization="nielsr"							,     commit_message="Add model"							,     use_temp_dir=_lowerCAmelCase							,     )


if __name__ == "__main__":
    __lowerCamelCase :  str										=  argparse.ArgumentParser()

    parser.add_argument(
        """--checkpoint_url""",
        default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
        type=str,
        help="""URL to the original PyTorch checkpoint (.pth file).""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""",
        action="""store_true""",
    )
    __lowerCamelCase :  List[str]										=  parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['image_processor', 'tokenizer']
      _UpperCAmelCase			:Tuple							=     'BlipImageProcessor'
      _UpperCAmelCase			:Optional[int]							=     'AutoTokenizer'





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     False
       super().__init__(A_ ,							A_     )
       UpperCamelCase			:      str 			=     self.image_processor





      def __call__(				self ,							A_ = None ,							A_ = None ,							A_ = True ,							A_ = False ,							A_ = None ,							A_ = None ,							A_ = 0 ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = True ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       if images is None and text is None:
        raise ValueError("You have to specify either images or text."     )

       # Get only text
       if images is None:
        UpperCamelCase			:      int 			=     self.tokenizer
        UpperCamelCase			:      Optional[int] 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
        return text_encoding

       # add pixel_values
       UpperCamelCase			:      int 			=     self.image_processor(A_ ,							return_tensors=A_     )

       if text is not None:
        UpperCamelCase			:      Dict 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
       else:
        UpperCamelCase			:      Dict 			=     None

       if text_encoding is not None:
        encoding_image_processor.update(A_     )

       return encoding_image_processor





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.tokenizer.batch_decode(*A_ ,							**A_     )





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.tokenizer.decode(*A_ ,							**A_     )





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer.model_input_names
       UpperCamelCase			:      int 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
