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										=						logging.get_logger(__name__)






def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:Dict)						->   int:
       _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  (       snake_case__			:Tuple   ,	snake_case__			:str)						->   Tuple:
       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  (       snake_case__			:str   ,	snake_case__			:List[Any]   ,	snake_case__			:Any)						->   Union[str, Any]:
       _A        =  dct.pop(snake_case__)
       _A        =  val






def  snake_case  (       snake_case__			:Dict)						->   int:
       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(snake_case__   ,	stream=snake_case__).raw).convert("""RGB""")
       return im






@torch.no_grad()
def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Tuple)						->   List[Any]:
       _A        =  ViTConfig(image_size=384   ,	qkv_bias=snake_case__)
       _A        =  TrOCRConfig()

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

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

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

       _A        =  create_rename_keys(snake_case__   ,	snake_case__)
       for src, dest in rename_keys:
              rename_key(snake_case__   ,	snake_case__   ,	snake_case__)
       read_in_q_k_v(snake_case__   ,	snake_case__)

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

    # load state dict
       model.load_state_dict(snake_case__)

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

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

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

       _A        =  torch.Size([1, 1, 50_265])
       if "trocr-base-handwritten" in checkpoint_url:
              _A        =  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:
              _A        =  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:
              _A        =  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:
              _A        =  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]   ,	snake_case__   ,	atol=1E-3), "First elements of logits not as expected"

       Path(snake_case__).mkdir(exist_ok=snake_case__)
       print(F'''Saving model to {pytorch_dump_folder_path}''')
       model.save_pretrained(snake_case__)
       print(F'''Saving processor to {pytorch_dump_folder_path}''')
       processor.save_pretrained(snake_case__)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						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										=						parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import math
import sys






def  snake_case  (       snake_case__			:int)						->   int:
       if number != int(snake_case__):
              raise ValueError("""the value of input must be a natural number""")
       if number < 0:
              raise ValueError("""the value of input must not be a negative number""")
       if number == 0:
              return 1
       _A        =  [-1] * (number + 1)
       _A        =  0
       for i in range(1   ,	number + 1):
              _A        =  sys.maxsize
              _A        =  int(math.sqrt(snake_case__))
              for j in range(1   ,	root + 1):
                     _A        =  1 + answers[i - (j**2)]
                     _A        =  min(snake_case__   ,	snake_case__)
              _A        =  answer
       return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




