



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()
__snake_case						:Dict      		=     logging.get_logger(__name__)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				[]
    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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    for i in range(encoder_config.num_hidden_layers				):
        # queries, keys and values (only weights, no biases)
        __a						       =				state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight'				)

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				dct.pop(_UpperCAmelCase				)
    __a						       =				val





def      __snake_case	(			_UpperCAmelCase				):
    if "handwritten" in checkpoint_url:
        __a						       =				'''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						       =				'''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						       =				Image.open(requests.get(_UpperCAmelCase   , stream=_UpperCAmelCase				).raw				).convert('''RGB'''				)
    return im





@torch.no_grad()
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				ViTConfig(image_size=384   , qkv_bias=_UpperCAmelCase				)
    __a						       =				TrOCRConfig()

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

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

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

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

    # load state dict
    model.load_state_dict(_UpperCAmelCase				)

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

    __a						       =				processor(images=prepare_img(_UpperCAmelCase				)   , return_tensors='''pt'''				).pixel_values

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

    __a						       =				torch.Size([1, 1, 50265]				)
    if "trocr-base-handwritten" in checkpoint_url:
        __a						       =				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						       =				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						       =				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						       =				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__":
 __snake_case						:Any      		=     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.'''
 )
 __snake_case						:List[Any]      		=     parser.parse_args()
 convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase=2_8_1_2_3 )  ->  Any:
   lowercase__:   Optional[Any]					=      [1] * (limit + 1)

   for i in range(2       ,    int(limit**0.5 ) + 1 ):
      sum_divs[i * i] += i
      for k in range(i + 1       ,    limit // i + 1 ):
         sum_divs[k * i] += k + i

   lowercase__:   Union[str, Any]					=      set()
   lowercase__:   Optional[Any]					=      0

   for n in range(1       ,    limit + 1 ):
      if sum_divs[n] > n:
         abundants.add(__UpperCAmelCase )

      if not any((n - a in abundants) for a in abundants ):
         res += n

   return res


if __name__ == "__main__":
       print(solution())
