





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()
UpperCAmelCase     :     List[str]							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  a__     : int  =[]
  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 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



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

    a__     : Optional[Any]  =in_proj_weight[
        : encoder_config.hidden_size, :
    ]
    a__     : Any  =in_proj_weight[
        encoder_config.hidden_size : encoder_config.hidden_size * 2, :
    ]
    a__     : List[str]  =in_proj_weight[
        -encoder_config.hidden_size :, :
    ]
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  a__     : Union[str, Any]  =dct.pop(SCREAMING_SNAKE_CASE						)
  a__     : Dict  =val
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if "handwritten" in checkpoint_url:
    a__     : Tuple  ="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:
    a__     : Dict  ="https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
  a__     : Optional[int]  =Image.open(requests.get(SCREAMING_SNAKE_CASE	,	stream=SCREAMING_SNAKE_CASE						).raw						).convert("RGB"						)
  return im
@torch.no_grad()
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  a__     : Dict  =ViTConfig(image_size=384	,	qkv_bias=SCREAMING_SNAKE_CASE						)
  a__     : Union[str, Any]  =TrOCRConfig()

  # size of the architecture
  if "base" in checkpoint_url:
    a__     : Tuple  =768
  elif "large" in checkpoint_url:
    # use ViT-large encoder
    a__     : int  =1_024
    a__     : Any  =4_096
    a__     : str  =24
    a__     : Tuple  =16
    a__     : List[str]  =1_024
  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:
    a__     : Dict  =False
    a__     : Optional[Any]  ="relu"
    a__     : Any  =1_024
    a__     : Any  =True
    a__     : Optional[Any]  =False
    a__     : str  =False

  # load HuggingFace model
  a__     : int  =ViTModel(SCREAMING_SNAKE_CASE	,	add_pooling_layer=SCREAMING_SNAKE_CASE						)
  a__     : Optional[int]  =TrOCRForCausalLM(SCREAMING_SNAKE_CASE						)
  a__     : Optional[int]  =VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE	,	decoder=SCREAMING_SNAKE_CASE						)
  model.eval()

  # load state_dict of original model, rename some keys
  a__     : Dict  =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE	,	map_location="cpu"	,	check_hash=SCREAMING_SNAKE_CASE						)["model"]

  a__     : List[str]  =create_rename_keys(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  read_in_q_k_v(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  # 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():
    a__     : Optional[Any]  =state_dict.pop(SCREAMING_SNAKE_CASE						)
    if key.startswith("decoder"						) and "output_projection" not in key:
      a__     : Optional[Any]  =val
    else:
      a__     : Optional[int]  =val

    # load state dict
  model.load_state_dict(SCREAMING_SNAKE_CASE						)

  # Check outputs on an image
  a__     : Tuple  =ViTImageProcessor(size=encoder_config.image_size						)
  a__     : Dict  =RobertaTokenizer.from_pretrained("roberta-large"						)
  a__     : List[Any]  =TrOCRProcessor(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  a__     : Tuple  =processor(images=prepare_img(SCREAMING_SNAKE_CASE						)	,	return_tensors="pt"						).pixel_values

  # verify logits
  a__     : List[Any]  =torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
  a__     : Dict  =model(pixel_values=SCREAMING_SNAKE_CASE	,	decoder_input_ids=SCREAMING_SNAKE_CASE						)
  a__     : Union[str, Any]  =outputs.logits

  a__     : str  =torch.Size([1, 1, 50_265]						)
  if "trocr-base-handwritten" in checkpoint_url:
    a__     : Union[str, Any]  =torch.tensor(
        [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]						)
  elif "trocr-large-handwritten" in checkpoint_url:
    a__     : Union[str, Any]  =torch.tensor(
        [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]						)
  elif "trocr-base-printed" in checkpoint_url:
    a__     : Tuple  =torch.tensor(
        [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]						)
  elif "trocr-large-printed" in checkpoint_url:
    a__     : str  =torch.tensor(
        [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]						)

  if "stage1" not in checkpoint_url:
    assert logits.shape == expected_shape, "Shape of logits not as expected"
    assert torch.allclose(logits[0, 0, :10]	,	SCREAMING_SNAKE_CASE	,	atol=1e-3						), "First elements of logits not as expected"

  Path(SCREAMING_SNAKE_CASE						).mkdir(exist_ok=SCREAMING_SNAKE_CASE						)
  print(f'''Saving model to {pytorch_dump_folder_path}'''						)
  model.save_pretrained(SCREAMING_SNAKE_CASE						)
  print(f'''Saving processor to {pytorch_dump_folder_path}'''						)
  processor.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     UpperCAmelCase     :     List[Any]							=	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."""
     )
     UpperCAmelCase     :     Union[str, Any]							=	parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F"""{price_plus_tax(100, 0.2_5) = }""")
     print(F"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")






