
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 						__magic_name__ (    __a			:       Optional[Any]       ,		__a			:       str						):



     '''simple docstring'''




     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 						__magic_name__ (    __a			:       Optional[int]       ,		__a			:       Optional[Any]						):



     '''simple docstring'''




     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 						__magic_name__ (    __a			:       List[Any]       ,		__a			:       Tuple       ,		__a			:       int						):



     '''simple docstring'''




     UpperCamelCase__				=				dct.pop(lowerCAmelCase__						)
     UpperCamelCase__				=				val






def 						__magic_name__ (    __a			:       str						):



     '''simple docstring'''




     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(lowerCAmelCase__       ,		stream=lowerCAmelCase__						).raw						).convert("""RGB"""						)
     return im






@torch.no_grad()
def 						__magic_name__ (    __a			:       Any       ,		__a			:       int						):



     '''simple docstring'''




     UpperCamelCase__				=				ViTConfig(image_size=384       ,		qkv_bias=lowerCAmelCase__						)
     UpperCamelCase__				=				TrOCRConfig()

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

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

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

     UpperCamelCase__				=				create_rename_keys(lowerCAmelCase__       ,		lowerCAmelCase__						)
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__						)
     read_in_q_k_v(lowerCAmelCase__       ,		lowerCAmelCase__						)

     # 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(lowerCAmelCase__						)
          if key.startswith("""decoder"""						) and "output_projection" not in key:
               UpperCamelCase__				=				val
          else:
               UpperCamelCase__				=				val

    # load state dict
     model.load_state_dict(lowerCAmelCase__						)

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

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

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

     UpperCamelCase__				=				torch.Size([1, 1, 50_265]						)
     if "trocr-base-handwritten" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]						)
     elif "trocr-large-handwritten" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]						)
     elif "trocr-base-printed" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]						)
     elif "trocr-large-printed" in checkpoint_url:
          UpperCamelCase__				=				torch.tensor(
              [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]						)

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

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


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)









'''simple docstring'''
# Algorithm for the pigeonhole sorting







def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		Any													=				min(lowerCAmelCase__						)  # min() finds the minimum value
    UpperCAmelCase__			:		Optional[int]													=				max(lowerCAmelCase__						)  # max() finds the maximum value

    UpperCAmelCase__			:		int													=				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
    UpperCAmelCase__			:		Any													=				[0] * size

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

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





def 					a__       (					)    ->	Union[str, Any]:
    UpperCAmelCase__			:		List[str]													=				[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(lowerCAmelCase__						)
    print('''Sorted order is:'''  ,					''' '''.join(lowerCAmelCase__						)						)


if __name__ == "__main__":
  main()
