

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()
SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			[]
   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 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[Any]:




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

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



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			dct.pop(A__   )
   lowerCamelCase__						=			val



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   if "handwritten" in checkpoint_url:
      lowerCamelCase__						=			'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__						=			'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__						=			Image.open(requests.get(A__	,    stream=A__   ).raw   ).convert("RGB"   )
   return im



@torch.no_grad()
def 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			ViTConfig(image_size=384	,    qkv_bias=A__   )
   lowerCamelCase__						=			TrOCRConfig()

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

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

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

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

    # load state dict
   model.load_state_dict(A__   )

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

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

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

   lowerCamelCase__						=			torch.Size([1, 1, 5_0265]   )
   if "trocr-base-handwritten" in checkpoint_url:
      lowerCamelCase__						=			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__						=			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__						=			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__						=			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]	,    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__":
     SCREAMING_SNAKE_CASE_     :			Dict	  =			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.'''
     )
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""




# Algorithm for the pigeonhole sorting





def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			min(A__   )  # min() finds the minimum value
   lowerCamelCase__						=			max(A__   )  # max() finds the maximum value

   lowerCamelCase__						=			max_val - min_val + 1  # size is difference of max and min values plus one

   # list of pigeonholes of size equal to the variable size
   lowerCamelCase__						=			[0] * size

   # Populate the pigeonholes.
   for x in a:
      assert isinstance(A__	,    A__   ), "integers only please"
      holes[x - min_val] += 1

   # Putting the elements back into the array in an order.
   lowerCamelCase__						=			0
   for count in range(A__   ):
      while holes[count] > 0:
         holes[count] -= 1
         lowerCamelCase__						=			count + min_val
         i += 1



def 	UpperCAmelCase__   (				)      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			[8, 3, 2, 7, 4, 6, 8]
   pigeonhole_sort(A__   )
   print("Sorted order is:"	,    " ".join(A__   )   )


if __name__ == "__main__":
     main()




