





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()
__A     =    logging.get_logger(__name__)





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  lowerCamelCase__:				Dict        =[]
  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_			(	__a     ,   __a		)					->		Union[str, Any]:







  """simple docstring"""





  for i in range(encoder_config.num_hidden_layers		):
    # queries, keys and values (only weights, no biases)
    lowerCamelCase__:				List[str]        =state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight"""		)

    lowerCamelCase__:				List[str]        =in_proj_weight[
        : encoder_config.hidden_size, :
    ]
    lowerCamelCase__:				Union[str, Any]        =in_proj_weight[
        encoder_config.hidden_size : encoder_config.hidden_size * 2, :
    ]
    lowerCamelCase__:				Dict        =in_proj_weight[
        -encoder_config.hidden_size :, :
    ]





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =dct.pop(__a		)
  lowerCamelCase__:				Optional[Any]        =val





def 					lowerCAmelCase_			(	__a		)					->		Tuple:







  """simple docstring"""





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





@torch.no_grad()
def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =ViTConfig(image_size=384     ,   qkv_bias=__a		)
  lowerCamelCase__:				Optional[int]        =TrOCRConfig()

  # size of the architecture
  if "base" in checkpoint_url:
    lowerCamelCase__:				Tuple        =768
  elif "large" in checkpoint_url:
    # use ViT-large encoder
    lowerCamelCase__:				Dict        =1024
    lowerCamelCase__:				Dict        =4096
    lowerCamelCase__:				List[Any]        =24
    lowerCamelCase__:				Optional[Any]        =16
    lowerCamelCase__:				Optional[int]        =1024
  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:
    lowerCamelCase__:				List[str]        =False
    lowerCamelCase__:				Dict        ="relu"
    lowerCamelCase__:				List[Any]        =1024
    lowerCamelCase__:				Tuple        =True
    lowerCamelCase__:				Optional[Any]        =False
    lowerCamelCase__:				int        =False

  # load HuggingFace model
  lowerCamelCase__:				List[str]        =ViTModel(__a     ,   add_pooling_layer=__a		)
  lowerCamelCase__:				Union[str, Any]        =TrOCRForCausalLM(__a		)
  lowerCamelCase__:				Any        =VisionEncoderDecoderModel(encoder=__a     ,   decoder=__a		)
  model.eval()

  # load state_dict of original model, rename some keys
  lowerCamelCase__:				List[str]        =torch.hub.load_state_dict_from_url(__a     ,   map_location="cpu"     ,   check_hash=__a		)["model"]

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

  # 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():
    lowerCamelCase__:				Union[str, Any]        =state_dict.pop(__a		)
    if key.startswith("decoder"		) and "output_projection" not in key:
      lowerCamelCase__:				int        =val
    else:
      lowerCamelCase__:				Dict        =val

    # load state dict
  model.load_state_dict(__a		)

  # Check outputs on an image
  lowerCamelCase__:				Tuple        =ViTImageProcessor(size=encoder_config.image_size		)
  lowerCamelCase__:				str        =RobertaTokenizer.from_pretrained("roberta-large"		)
  lowerCamelCase__:				int        =TrOCRProcessor(__a     ,   __a		)

  lowerCamelCase__:				List[str]        =processor(images=prepare_img(__a		)     ,   return_tensors="pt"		).pixel_values

  # verify logits
  lowerCamelCase__:				int        =torch.tensor([[model.config.decoder.decoder_start_token_id]]		)
  lowerCamelCase__:				Dict        =model(pixel_values=__a     ,   decoder_input_ids=__a		)
  lowerCamelCase__:				Any        =outputs.logits

  lowerCamelCase__:				List[str]        =torch.Size([1, 1, 50265]		)
  if "trocr-base-handwritten" in checkpoint_url:
    lowerCamelCase__:				str        =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:
    lowerCamelCase__:				Optional[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:
    lowerCamelCase__:				Dict        =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:
    lowerCamelCase__:				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]     ,   __a     ,   atol=1e-3		), "First elements of logits not as expected"

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


if __name__ == "__main__":
     __A     =    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."
     )
     __A     =    parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













from ..utils import DummyObject, requires_backends



class        _SCREAMING_SNAKE_CASE						(     metaclass=__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["torch", "torchsde"]

 def __init__(self    :       Union[str, Any]	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Tuple)	->Any:



   '''simple docstring'''



   requires_backends(self	,			["torch", "torchsde"])

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Dict	,			*UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])







 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Tuple	,			*UpperCAmelCase_    :       Tuple	,			**UpperCAmelCase_    :       Any)	->Any:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])

