


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







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict  ,    __A :			List[Any]				)	->		int:

       """simple docstring"""

       a_					:		Dict							=  []
       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        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			str				)	->		Tuple:

       """simple docstring"""

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

              a_					:		str							=  in_proj_weight[
                  : encoder_config.hidden_size, :
              ]
              a_					:		Any							=  in_proj_weight[
                  encoder_config.hidden_size : encoder_config.hidden_size * 2, :
              ]
              a_					:		List[str]							=  in_proj_weight[
                  -encoder_config.hidden_size :, :
              ]







def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			Optional[Any]  ,    __A :			Optional[Any]				)	->		Any:

       """simple docstring"""

       a_					:		Optional[int]							=  dct.pop(__A				)
       a_					:		List[str]							=  val







def        SCREAMING_SNAKE_CASE_						(			__A :			Any				)	->		List[str]:

       """simple docstring"""

       if "handwritten" in checkpoint_url:
              a_					:		Dict							=  '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_					:		List[Any]							=  Image.open(requests.get(__A  ,    stream=__A				).raw				).convert('RGB'				)
       return im







@torch.no_grad()
def        SCREAMING_SNAKE_CASE_						(			__A :			int  ,    __A :			Dict				)	->		List[Any]:

       """simple docstring"""

       a_					:		Tuple							=  ViTConfig(image_size=3_84  ,    qkv_bias=__A				)
       a_					:		Union[str, Any]							=  TrOCRConfig()

       # size of the architecture
       if "base" in checkpoint_url:
              a_					:		List[Any]							=  7_68
       elif "large" in checkpoint_url:
              # use ViT-large encoder
              a_					:		Optional[int]							=  10_24
              a_					:		Optional[Any]							=  40_96
              a_					:		Tuple							=  24
              a_					:		Optional[Any]							=  16
              a_					:		str							=  10_24
       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_					:		Optional[int]							=  False
              a_					:		int							=  'relu'
              a_					:		Tuple							=  10_24
              a_					:		List[Any]							=  True
              a_					:		List[str]							=  False
              a_					:		str							=  False

       # load HuggingFace model
       a_					:		List[str]							=  ViTModel(__A  ,    add_pooling_layer=__A				)
       a_					:		Dict							=  TrOCRForCausalLM(__A				)
       a_					:		List[str]							=  VisionEncoderDecoderModel(encoder=__A  ,    decoder=__A				)
       model.eval()

       # load state_dict of original model, rename some keys
       a_					:		Optional[int]							=  torch.hub.load_state_dict_from_url(__A  ,    map_location='cpu'  ,    check_hash=__A				)['model']

       a_					:		int							=  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():
              a_					:		int							=  state_dict.pop(__A				)
              if key.startswith('decoder'				) and "output_projection" not in key:
                     a_					:		Optional[int]							=  val
              else:
                     a_					:		Dict							=  val

    # load state dict
       model.load_state_dict(__A				)

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

       a_					:		Any							=  processor(images=prepare_img(__A				)  ,    return_tensors='pt'				).pixel_values

       # verify logits
       a_					:		Tuple							=  torch.tensor([[model.config.decoder.decoder_start_token_id]]				)
       a_					:		Dict							=  model(pixel_values=__A  ,    decoder_input_ids=__A				)
       a_					:		List[str]							=  outputs.logits

       a_					:		Any							=  torch.Size([1, 1, 5_02_65]				)
       if "trocr-base-handwritten" in checkpoint_url:
              a_					:		Any							=  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_					:		Union[str, Any]							=  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_					:		Union[str, Any]							=  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_					:		Tuple							=  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__":
   UpperCAmelCase_      : List[str]  					=       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.'
   )
   UpperCAmelCase_      : Union[str, Any]  					=       parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






UpperCAmelCase_      : Optional[int]  					=       [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_      : str  					=       [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_      : str  					=       {
    0: 'Sunday',
    1: 'Monday',
    2: 'Tuesday',
    3: 'Wednesday',
    4: 'Thursday',
    5: 'Friday',
    6: 'Saturday',
}







def        SCREAMING_SNAKE_CASE_						(			__A :			int  ,    __A :			int  ,    __A :			int				)	->		str:

       """simple docstring"""

       assert len(str(__A				)				) > 2, "year should be in YYYY format"
       assert 1 <= month <= 12, "month should be between 1 to 12"
       assert 1 <= day <= 31, "day should be between 1 to 31"

       # Doomsday algorithm:
       a_					:		List[str]							=  year // 1_00
       a_					:		Optional[int]							=  (5 * (century % 4) + 2) % 7
       a_					:		List[str]							=  year % 1_00
       a_					:		str							=  centurian % 12
       a_					:		List[str]							=  (
           (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
       ) % 7
       a_					:		Any							=  (
           DOOMSDAY_NOT_LEAP[month - 1]
           if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
           else DOOMSDAY_LEAP[month - 1]
       )
       a_					:		Any							=  (dooms_day + day - day_anchor) % 7
       return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


