





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        =				logging.get_logger(__name__)




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  []
       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_  )     ->		Optional[Any]:
       for i in range(encoder_config.num_hidden_layers  ):
              # queries, keys and values (only weights, no biases)
              UpperCamelCase_          =  state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''  )

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  dct.pop(UpperCamelCase_  )
       UpperCamelCase_          =  val




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		int:
       if "handwritten" in checkpoint_url:
              UpperCamelCase_          =  "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_          =  "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_          =  Image.open(requests.get(UpperCamelCase_					,		stream=UpperCamelCase_  ).raw  ).convert("RGB"  )
       return im




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  ViTConfig(image_size=384					,		qkv_bias=UpperCamelCase_  )
       UpperCamelCase_          =  TrOCRConfig()

       # size of the architecture
       if "base" in checkpoint_url:
              UpperCamelCase_          =  768
       elif "large" in checkpoint_url:
              # use ViT-large encoder
              UpperCamelCase_          =  1024
              UpperCamelCase_          =  4096
              UpperCamelCase_          =  24
              UpperCamelCase_          =  16
              UpperCamelCase_          =  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:
              UpperCamelCase_          =  False
              UpperCamelCase_          =  "relu"
              UpperCamelCase_          =  1024
              UpperCamelCase_          =  True
              UpperCamelCase_          =  False
              UpperCamelCase_          =  False

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

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

       UpperCamelCase_          =  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():
              UpperCamelCase_          =  state_dict.pop(UpperCamelCase_  )
              if key.startswith("decoder"  ) and "output_projection" not in key:
                     UpperCamelCase_          =  val
              else:
                     UpperCamelCase_          =  val

    # load state dict
       model.load_state_dict(UpperCamelCase_  )

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

       UpperCamelCase_          =  processor(images=prepare_img(UpperCamelCase_  )					,		return_tensors="pt"  ).pixel_values

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

       UpperCamelCase_          =  torch.Size([1, 1, 50265]  )
       if "trocr-base-handwritten" in checkpoint_url:
              UpperCamelCase_          =  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_          =  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_          =  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_          =  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, :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__":
     _UpperCAmelCase        =				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        =				parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import sys




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  len(UpperCamelCase_  )
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]

       for chain_length in range(2					,		UpperCamelCase_  ):
              for a in range(1					,		n - chain_length + 1  ):
                     UpperCamelCase_          =  a + chain_length - 1

                     UpperCamelCase_          =  sys.maxsize
                     for c in range(UpperCamelCase_					,		UpperCamelCase_  ):
                            UpperCamelCase_          =  (
                                matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                            )
                            if cost < matrix[a][b]:
                                   UpperCamelCase_          =  cost
                                   UpperCamelCase_          =  c
       return matrix, sol




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       if i == j:
              print("A" + str(UpperCamelCase_  )					,		end=" "  )
       else:
              print("("					,		end=" "  )
              print_optiomal_solution(UpperCamelCase_					,		UpperCamelCase_					,		optimal_solution[i][j]  )
              print_optiomal_solution(UpperCamelCase_					,		optimal_solution[i][j] + 1					,		UpperCamelCase_  )
              print(")"					,		end=" "  )




def  lowerCAmelCase_ (							)     ->		Any:
       UpperCamelCase_          =  [30, 35, 15, 5, 10, 20, 25]
       UpperCamelCase_          =  len(UpperCamelCase_  )
       # Size of matrix created from above array will be
       # 30*35 35*15 15*5 5*10 10*20 20*25
       UpperCamelCase_       , UpperCamelCase_          =  matrix_chain_order(UpperCamelCase_  )

       print("No. of Operation required: " + str(matrix[1][n - 1]  )  )
       print_optiomal_solution(UpperCamelCase_					,		1					,		n - 1  )


if __name__ == "__main__":
     main()

