




'''simple docstring'''



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_       =							logging.get_logger(__name__)




def    lowercase__(       __UpperCamelCase: Tuple	,__UpperCamelCase: Union[str, Any]  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Optional[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    lowercase__(       __UpperCamelCase: int	,__UpperCamelCase: Any  ):

      """simple docstring"""






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

            SCREAMING_SNAKE_CASE					:	List[str]   =		in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE					:	int   =		in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE					:	Dict   =		in_proj_weight[
                -encoder_config.hidden_size :, :
            ]




def    lowercase__(       __UpperCamelCase: List[Any]	,__UpperCamelCase: Optional[int]	,__UpperCamelCase: str  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	int   =		dct.pop(__UpperCamelCase  )
      SCREAMING_SNAKE_CASE					:	Tuple   =		val




def    lowercase__(       __UpperCamelCase: Optional[int]  ):

      """simple docstring"""






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




@torch.no_grad()
def    lowercase__(       __UpperCamelCase: Any	,__UpperCamelCase: str  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Dict   =		ViTConfig(image_size=3_84	,qkv_bias=__UpperCamelCase  )
      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		TrOCRConfig()

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

      # load HuggingFace model
      SCREAMING_SNAKE_CASE					:	Dict   =		ViTModel(__UpperCamelCase	,add_pooling_layer=__UpperCamelCase  )
      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		TrOCRForCausalLM(__UpperCamelCase  )
      SCREAMING_SNAKE_CASE					:	str   =		VisionEncoderDecoderModel(encoder=__UpperCamelCase	,decoder=__UpperCamelCase  )
      model.eval()

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

      SCREAMING_SNAKE_CASE					:	int   =		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():
            SCREAMING_SNAKE_CASE					:	Dict   =		state_dict.pop(__UpperCamelCase  )
            if key.startswith('decoder'  ) and "output_projection" not in key:
                  SCREAMING_SNAKE_CASE					:	int   =		val
            else:
                  SCREAMING_SNAKE_CASE					:	int   =		val

    # load state dict
      model.load_state_dict(__UpperCamelCase  )

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

      SCREAMING_SNAKE_CASE					:	List[str]   =		processor(images=prepare_img(__UpperCamelCase  )	,return_tensors='pt'  ).pixel_values

      # verify logits
      SCREAMING_SNAKE_CASE					:	int   =		torch.tensor([[model.config.decoder.decoder_start_token_id]]  )
      SCREAMING_SNAKE_CASE					:	List[Any]   =		model(pixel_values=__UpperCamelCase	,decoder_input_ids=__UpperCamelCase  )
      SCREAMING_SNAKE_CASE					:	Tuple   =		outputs.logits

      SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.Size([1, 1, 5_02_65]  )
      if "trocr-base-handwritten" in checkpoint_url:
            SCREAMING_SNAKE_CASE					:	List[str]   =		torch.tensor(
                [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]  )
      elif "trocr-large-handwritten" in checkpoint_url:
            SCREAMING_SNAKE_CASE					:	Any   =		torch.tensor(
                [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]  )
      elif "trocr-base-printed" in checkpoint_url:
            SCREAMING_SNAKE_CASE					:	str   =		torch.tensor(
                [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]  )
      elif "trocr-large-printed" in checkpoint_url:
            SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor(
                [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]  )

      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__":
      UpperCamelCase_       =							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_       =							parser.parse_args()
      convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



from ....configuration_utils import PretrainedConfig
from ....utils import logging


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


