

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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



  """simple docstring"""






  _snake_case						=							[]
  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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






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

    _snake_case						=							in_proj_weight[
        : encoder_config.hidden_size, :
    ]
    _snake_case						=							in_proj_weight[
        encoder_config.hidden_size : encoder_config.hidden_size * 2, :
    ]
    _snake_case						=							in_proj_weight[
        -encoder_config.hidden_size :, :
    ]



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							dct.pop(_UpperCamelCase					)
  _snake_case						=							val



def 				snake_case_(_UpperCamelCase					)							->     Dict:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Dict:



  """simple docstring"""






  _snake_case						=							ViTConfig(image_size=384			,	qkv_bias=_UpperCamelCase					)
  _snake_case						=							TrOCRConfig()

  # size of the architecture
  if "base" in checkpoint_url:
    _snake_case						=							768
  elif "large" in checkpoint_url:
    # use ViT-large encoder
    _snake_case						=							1_024
    _snake_case						=							4_096
    _snake_case						=							24
    _snake_case						=							16
    _snake_case						=							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:
    _snake_case						=							False
    _snake_case						=							'''relu'''
    _snake_case						=							1_024
    _snake_case						=							True
    _snake_case						=							False
    _snake_case						=							False

  # load HuggingFace model
  _snake_case						=							ViTModel(_UpperCamelCase			,	add_pooling_layer=_UpperCamelCase					)
  _snake_case						=							TrOCRForCausalLM(_UpperCamelCase					)
  _snake_case						=							VisionEncoderDecoderModel(encoder=_UpperCamelCase			,	decoder=_UpperCamelCase					)
  model.eval()

  # load state_dict of original model, rename some keys
  _snake_case						=							torch.hub.load_state_dict_from_url(_UpperCamelCase			,	map_location='''cpu'''			,	check_hash=_UpperCamelCase					)['''model''']

  _snake_case						=							create_rename_keys(_UpperCamelCase			,	_UpperCamelCase					)
  for src, dest in rename_keys:
    rename_key(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)
  read_in_q_k_v(_UpperCamelCase			,	_UpperCamelCase					)

  # 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():
    _snake_case						=							state_dict.pop(_UpperCamelCase					)
    if key.startswith('''decoder'''					) and "output_projection" not in key:
      _snake_case						=							val
    else:
      _snake_case						=							val

    # load state dict
  model.load_state_dict(_UpperCamelCase					)

  # Check outputs on an image
  _snake_case						=							ViTImageProcessor(size=encoder_config.image_size					)
  _snake_case						=							RobertaTokenizer.from_pretrained('''roberta-large'''					)
  _snake_case						=							TrOCRProcessor(_UpperCamelCase			,	_UpperCamelCase					)

  _snake_case						=							processor(images=prepare_img(_UpperCamelCase					)			,	return_tensors='''pt'''					).pixel_values

  # verify logits
  _snake_case						=							torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
  _snake_case						=							model(pixel_values=_UpperCamelCase			,	decoder_input_ids=_UpperCamelCase					)
  _snake_case						=							outputs.logits

  _snake_case						=							torch.Size([1, 1, 50_265]					)
  if "trocr-base-handwritten" in checkpoint_url:
    _snake_case						=							torch.tensor(
        [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]					)
  elif "trocr-large-handwritten" in checkpoint_url:
    _snake_case						=							torch.tensor(
        [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]					)
  elif "trocr-base-printed" in checkpoint_url:
    _snake_case						=							torch.tensor(
        [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]					)
  elif "trocr-large-printed" in checkpoint_url:
    _snake_case						=							torch.tensor(
        [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]					)

  if "stage1" not in checkpoint_url:
    assert logits.shape == expected_shape, "Shape of logits not as expected"
    assert torch.allclose(logits[0, 0, :10]			,	_UpperCamelCase			,	atol=1E-3					), "First elements 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 processor to {pytorch_dump_folder_path}"""					)
  processor.save_pretrained(_UpperCamelCase					)


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)




def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''					)
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]  # remove the leading "0b"
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]
  _snake_case						=							max(len(_UpperCamelCase					)			,	len(_UpperCamelCase					)					)
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)					)					)
      for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase					)			,	b_binary.zfill(_UpperCamelCase					)					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

