




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()
lowerCAmelCase_					    = logging.get_logger(__name__)



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  List[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			(					UpperCamelCase	,      UpperCamelCase      )     ->	int:
  for i in range(encoder_config.num_hidden_layers      ):
    # queries, keys and values (only weights, no biases)
    lowerCAmelCase__				:  Optional[int]		       =     state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight"""      )

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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  Dict		       =     dct.pop(UpperCamelCase      )
  lowerCAmelCase__				:  Any		       =     val



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[Any]:
  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__				:  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'''
  lowerCAmelCase__				:  List[Any]		       =     Image.open(requests.get(UpperCamelCase	,      stream=UpperCamelCase      ).raw      ).convert('''RGB'''      )
  return im



@torch.no_grad()
def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	str:
  lowerCAmelCase__				:  List[str]		       =     ViTConfig(image_size=384	,      qkv_bias=UpperCamelCase      )
  lowerCAmelCase__				:  Any		       =     TrOCRConfig()

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

  # load HuggingFace model
  lowerCAmelCase__				:  Dict		       =     ViTModel(UpperCamelCase	,      add_pooling_layer=UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     TrOCRForCausalLM(UpperCamelCase      )
  lowerCAmelCase__				:  Dict		       =     VisionEncoderDecoderModel(encoder=UpperCamelCase	,      decoder=UpperCamelCase      )
  model.eval()

  # load state_dict of original model, rename some keys
  lowerCAmelCase__				:  Union[str, Any]		       =     torch.hub.load_state_dict_from_url(UpperCamelCase	,      map_location='''cpu'''	,      check_hash=UpperCamelCase      )['''model''']

  lowerCAmelCase__				:  Optional[int]		       =     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():
    lowerCAmelCase__				:  List[Any]		       =     state_dict.pop(UpperCamelCase      )
    if key.startswith('''decoder'''      ) and "output_projection" not in key:
      lowerCAmelCase__				:  Dict		       =     val
    else:
      lowerCAmelCase__				:  int		       =     val

    # load state dict
  model.load_state_dict(UpperCamelCase      )

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

  lowerCAmelCase__				:  str		       =     processor(images=prepare_img(UpperCamelCase      )	,      return_tensors='''pt'''      ).pixel_values

  # verify logits
  lowerCAmelCase__				:  Tuple		       =     torch.tensor([[model.config.decoder.decoder_start_token_id]]      )
  lowerCAmelCase__				:  int		       =     model(pixel_values=UpperCamelCase	,      decoder_input_ids=UpperCamelCase      )
  lowerCAmelCase__				:  Optional[int]		       =     outputs.logits

  lowerCAmelCase__				:  Optional[Any]		       =     torch.Size([1, 1, 50265]      )
  if "trocr-base-handwritten" in checkpoint_url:
    lowerCAmelCase__				:  Optional[int]		       =     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:
    lowerCAmelCase__				:  Dict		       =     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:
    lowerCAmelCase__				:  Any		       =     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:
    lowerCAmelCase__				:  Any		       =     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__":
     lowerCAmelCase_					    = 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."""
     )
     lowerCAmelCase_					    = parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import math



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * power_factor



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * math.sqrt(1 - power_factor**2      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

