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()
_lowercase							:      Optional[int]      							=logging.get_logger(__name__)



def A__  (			lowercase:     int,		lowercase:     Tuple					)				->		Dict:

       '''simple docstring'''





       A						:     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 A__  (			lowercase:     Union[str, Any],		lowercase:     Tuple					)				->		str:

       '''simple docstring'''





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

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



def A__  (			lowercase:     str,		lowercase:     Dict,		lowercase:     int					)				->		Tuple:

       '''simple docstring'''





       A						:     Union[str, Any]         =dct.pop(_UpperCAmelCase					)
       A						:     List[str]         =val



def A__  (			lowercase:     str					)				->		Tuple:

       '''simple docstring'''





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



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

       '''simple docstring'''





       A						:     Union[str, Any]         =ViTConfig(image_size=384,		qkv_bias=_UpperCAmelCase					)
       A						:     Any         =TrOCRConfig()

       # size of the architecture
       if "base" in checkpoint_url:
              A						:     Union[str, Any]         =768
       elif "large" in checkpoint_url:
              # use ViT-large encoder
              A						:     Optional[Any]         =1_024
              A						:     Any         =4_096
              A						:     Optional[int]         =24
              A						:     List[Any]         =16
              A						:     Optional[Any]         =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:
              A						:     Any         =False
              A						:     str         ='relu'
              A						:     Any         =1_024
              A						:     List[str]         =True
              A						:     str         =False
              A						:     List[Any]         =False

       # load HuggingFace model
       A						:     Any         =ViTModel(_UpperCAmelCase,		add_pooling_layer=_UpperCAmelCase					)
       A						:     Dict         =TrOCRForCausalLM(_UpperCAmelCase					)
       A						:     Any         =VisionEncoderDecoderModel(encoder=_UpperCAmelCase,		decoder=_UpperCAmelCase					)
       model.eval()

       # load state_dict of original model, rename some keys
       A						:     List[str]         =torch.hub.load_state_dict_from_url(_UpperCAmelCase,		map_location='cpu',		check_hash=_UpperCAmelCase					)['model']

       A						:     List[str]         =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():
              A						:     Tuple         =state_dict.pop(_UpperCAmelCase					)
              if key.startswith('decoder'					) and "output_projection" not in key:
                     A						:     Optional[int]         =val
              else:
                     A						:     int         =val

    # load state dict
       model.load_state_dict(_UpperCAmelCase					)

       # Check outputs on an image
       A						:     Tuple         =ViTImageProcessor(size=encoder_config.image_size					)
       A						:     Optional[int]         =RobertaTokenizer.from_pretrained('roberta-large'					)
       A						:     str         =TrOCRProcessor(_UpperCAmelCase,		_UpperCAmelCase					)

       A						:     Any         =processor(images=prepare_img(_UpperCAmelCase					),		return_tensors='pt'					).pixel_values

       # verify logits
       A						:     Tuple         =torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
       A						:     int         =model(pixel_values=_UpperCAmelCase,		decoder_input_ids=_UpperCAmelCase					)
       A						:     Dict         =outputs.logits

       A						:     int         =torch.Size([1, 1, 50_265]					)
       if "trocr-base-handwritten" in checkpoint_url:
              A						:     Optional[Any]         =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:
              A						:     Optional[Any]         =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:
              A						:     Tuple         =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:
              A						:     Any         =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__":
    _lowercase							:      int      							=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.'''
    )
    _lowercase							:      Optional[int]      							=parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







def A__  (			lowercase:     int					)				->		int:
       if not isinstance(lowercase,		lowercase					) or number < 0:
              raise ValueError('Input must be a non-negative integer'					)

       A						:     Any         =0
       while number:
              # This way we arrive at next set bit (next 1) instead of looping
              # through each bit and checking for 1s hence the
              # loop won't run 32 times it will only run the number of `1` times
              number &= number - 1
              count += 1
       return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()






