






'''simple docstring'''
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()
UpperCamelCase__ =			logging.get_logger(__name__)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    Any           =     "backbone." if is_semantic else ""

   lowercase_	:    Optional[int]           =     []
   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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




   for i in range(config.num_hidden_layers							):
      lowercase_	:    List[str]           =     "backbone." if is_semantic else ""
      # queries, keys and values
      lowercase_	:    List[Any]           =     state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""							)
      lowercase_	:    Dict           =     state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""							)
      lowercase_	:    Optional[int]           =     state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""							)

      lowercase_	:    Any           =     in_proj_weight[
          : config.hidden_size, :
      ]
      lowercase_	:    Tuple           =     q_bias
      lowercase_	:    Optional[Any]           =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase_	:    Optional[Any]           =     in_proj_weight[
          -config.hidden_size :, :
      ]
      lowercase_	:    int           =     v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      lowercase_	:    Tuple           =     state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""							)
      lowercase_	:    List[Any]           =     state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""							)

      lowercase_	:    Any           =     gamma_a
      lowercase_	:    List[str]           =     gamma_a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     dct.pop(_UpperCamelCase							)
   lowercase_	:    Optional[Any]           =     val




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_	:    Optional[int]           =     Image.open(requests.get(_UpperCamelCase						,		stream=_UpperCamelCase							).raw							)
   return im




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     False if "rvlcdip" in checkpoint_url else True
   lowercase_	:    Optional[int]           =     BeitConfig(use_absolute_position_embeddings=_UpperCamelCase						,		use_mask_token=_UpperCamelCase							)

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

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

   # load state_dict of original model, remove and rename some keys
   lowercase_	:    Optional[Any]           =     torch.hub.load_state_dict_from_url(_UpperCamelCase						,		map_location="cpu"							)["model"]

   lowercase_	:    Any           =     create_rename_keys(_UpperCamelCase						,		has_lm_head=_UpperCamelCase							)
   for src, dest in rename_keys:
      rename_key(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   read_in_q_k_v(_UpperCamelCase						,		_UpperCamelCase						,		has_lm_head=_UpperCamelCase							)

   # load HuggingFace model
   lowercase_	:    List[Any]           =     BeitForMaskedImageModeling(_UpperCamelCase							) if has_lm_head else BeitForImageClassification(_UpperCamelCase							)
   model.eval()
   model.load_state_dict(_UpperCamelCase							)

   # Check outputs on an image
   lowercase_	:    str           =     BeitImageProcessor(
       size=config.image_size						,		resample=PILImageResampling.BILINEAR						,		do_center_crop=_UpperCamelCase							)
   lowercase_	:    Union[str, Any]           =     prepare_img()

   lowercase_	:    Optional[Any]           =     image_processor(images=_UpperCamelCase						,		return_tensors="pt"							)
   lowercase_	:    Any           =     encoding["pixel_values"]

   lowercase_	:    Any           =     model(_UpperCamelCase							)
   lowercase_	:    Any           =     outputs.logits

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

   Path(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
   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 push_to_hub:
      if has_lm_head:
         lowercase_	:    Any           =     "dit-base" if "base" in checkpoint_url else "dit-large"
      else:
         lowercase_	:    Tuple           =     "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(_UpperCamelCase						,		_UpperCamelCase							)						,		organization="nielsr"						,		commit_message="Add image processor"						,		use_temp_dir=_UpperCamelCase						,		)
      model.push_to_hub(
          repo_path_or_name=Path(_UpperCamelCase						,		_UpperCamelCase							)						,		organization="nielsr"						,		commit_message="Add model"						,		use_temp_dir=_UpperCamelCase						,		)


if __name__ == "__main__":
 UpperCamelCase__ =			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',
 )
 UpperCamelCase__ =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''
from typing import List, Optional, Union

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


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   str		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   Dict		       =    'Pix2StructImageProcessor'
    __lowerCamelCase:   Union[str, Any]		       =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(			self  :       str  ,     a  :       Dict  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     False
       super().__init__(a  ,     a			)




    def __call__(			self  :       Tuple  ,     a  :       int=None  ,     a  :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     a  :       bool = True  ,     a  :       Union[bool, str, PaddingStrategy] = False  ,     a  :       Union[bool, str, TruncationStrategy] = None  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = 2_0_4_8  ,     a  :       int = 0  ,     a  :       Optional[int] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = True  ,     a  :       Optional[Union[str, TensorType]] = None  ,     **a  :       List[str]  ,     ):






       '''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 and not self.image_processor.is_vqa:
          lowercase_	:    Dict           =     self.tokenizer
          lowercase_	:    Tuple           =     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

       if not self.image_processor.is_vqa:
          # add pixel_values
          lowercase_	:    Optional[int]           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     **a			)
       else:
          # add pixel_values and bbox
          lowercase_	:    Any           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     header_text=a  ,     **a			)

       if text is not None and not self.image_processor.is_vqa:
          lowercase_	:    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  ,     )

          if "attention_mask" in text_encoding:
             lowercase_	:    str           =     text_encoding.pop("attention_mask"			)
          if "input_ids" in text_encoding:
             lowercase_	:    Dict           =     text_encoding.pop("input_ids"			)
       else:
          lowercase_	:    str           =     None

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

       return encoding_image_processor




    def 				lowerCAmelCase__				(			self  :       Any  ,     *a  :       str  ,     **a  :       Tuple			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       str  ,     *a  :       Optional[int]  ,     **a  :       Any			):






       '''simple docstring'''






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





    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.tokenizer.model_input_names
       lowercase_	:    Dict           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




