


'''simple docstring'''






import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase							=logging.get_logger(__name__)





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




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

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
           ('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
           ('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
           ('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
           ('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
           ('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
       ]							)

   return rename_keys





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Union[str, Any]							):







   '''simple docstring'''




   for i in range(encoder_config.num_hidden_layers							):
      # queries, keys and values (only weights, no biases)
      _UpperCAmelCase       :       int							   =state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight"							)

      _UpperCAmelCase       :       int							   =in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      _UpperCAmelCase       :       Any							   =in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      _UpperCAmelCase       :       Dict							   =in_proj_weight[
          -encoder_config.hidden_size :, :
      ]





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =dct.pop(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =val





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   if "handwritten" in checkpoint_url:
      _UpperCAmelCase       :       Optional[Any]							   ='https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      _UpperCAmelCase       :       Optional[Any]							   ='https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
   _UpperCAmelCase       :       int							   =Image.open(requests.get(__lowerCamelCase	,   stream=__lowerCamelCase							).raw							).convert('RGB'							)
   return im





@torch.no_grad()
def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Tuple							   =ViTConfig(image_size=3_8_4	,   qkv_bias=__lowerCamelCase							)
   _UpperCAmelCase       :       Dict							   =TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      _UpperCAmelCase       :       Optional[Any]							   =7_6_8
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      _UpperCAmelCase       :       Dict							   =1_0_2_4
      _UpperCAmelCase       :       Union[str, Any]							   =4_0_9_6
      _UpperCAmelCase       :       List[Any]							   =2_4
      _UpperCAmelCase       :       str							   =1_6
      _UpperCAmelCase       :       List[str]							   =1_0_2_4
   else:
      raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'							)

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      _UpperCAmelCase       :       Any							   =False
      _UpperCAmelCase       :       Any							   ='relu'
      _UpperCAmelCase       :       Optional[int]							   =1_0_2_4
      _UpperCAmelCase       :       List[str]							   =True
      _UpperCAmelCase       :       int							   =False
      _UpperCAmelCase       :       int							   =False

   # load HuggingFace model
   _UpperCAmelCase       :       Optional[Any]							   =ViTModel(__lowerCamelCase	,   add_pooling_layer=__lowerCamelCase							)
   _UpperCAmelCase       :       Dict							   =TrOCRForCausalLM(__lowerCamelCase							)
   _UpperCAmelCase       :       List[Any]							   =VisionEncoderDecoderModel(encoder=__lowerCamelCase	,   decoder=__lowerCamelCase							)
   model.eval()

   # load state_dict of original model, rename some keys
   _UpperCAmelCase       :       int							   =torch.hub.load_state_dict_from_url(__lowerCamelCase	,   map_location='cpu'	,   check_hash=__lowerCamelCase							)['model']

   _UpperCAmelCase       :       Tuple							   =create_rename_keys(__lowerCamelCase	,   __lowerCamelCase							)
   for src, dest in rename_keys:
      rename_key(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   read_in_q_k_v(__lowerCamelCase	,   __lowerCamelCase							)

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      _UpperCAmelCase       :       Optional[Any]							   =state_dict.pop(__lowerCamelCase							)
      if key.startswith('decoder'							) and "output_projection" not in key:
         _UpperCAmelCase       :       Tuple							   =val
      else:
         _UpperCAmelCase       :       Any							   =val

    # load state dict
   model.load_state_dict(__lowerCamelCase							)

   # Check outputs on an image
   _UpperCAmelCase       :       Tuple							   =ViTImageProcessor(size=encoder_config.image_size							)
   _UpperCAmelCase       :       List[Any]							   =RobertaTokenizer.from_pretrained('roberta-large'							)
   _UpperCAmelCase       :       str							   =TrOCRProcessor(__lowerCamelCase	,   __lowerCamelCase							)

   _UpperCAmelCase       :       Dict							   =processor(images=prepare_img(__lowerCamelCase							)	,   return_tensors='pt'							).pixel_values

   # verify logits
   _UpperCAmelCase       :       Any							   =torch.tensor([[model.config.decoder.decoder_start_token_id]]							)
   _UpperCAmelCase       :       str							   =model(pixel_values=__lowerCamelCase	,   decoder_input_ids=__lowerCamelCase							)
   _UpperCAmelCase       :       Optional[Any]							   =outputs.logits

   _UpperCAmelCase       :       Tuple							   =torch.Size([1, 1, 5_0_2_6_5]							)
   if "trocr-base-handwritten" in checkpoint_url:
      _UpperCAmelCase       :       Optional[Any]							   =torch.tensor(
          [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]							)
   elif "trocr-large-handwritten" in checkpoint_url:
      _UpperCAmelCase       :       List[Any]							   =torch.tensor(
          [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]							)
   elif "trocr-base-printed" in checkpoint_url:
      _UpperCAmelCase       :       Tuple							   =torch.tensor(
          [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]							)
   elif "trocr-large-printed" in checkpoint_url:
      _UpperCAmelCase       :       Dict							   =torch.tensor(
          [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]							)

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :1_0]	,   __lowerCamelCase	,   atol=1e-3							), "First elements 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 processor to {pytorch_dump_folder_path}"							)
   processor.save_pretrained(__lowerCamelCase							)


if __name__ == "__main__":
 lowercase							=argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_url',
     default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
     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.'
 )
 lowercase							=parser.parse_args()
 convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


lowercase							=logging.get_logger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							*snake_case  ,							**snake_case)						->  None:




          '''simple docstring'''
          warnings.warn(
              'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
              ' Please use DeformableDetrImageProcessor instead.'  ,							snake_case  ,							)
          super().__init__(*snake_case  ,							**snake_case)

