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()
A   :     Any       =				logging.get_logger(__name__)


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

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


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


def    a__       (						__UpperCamelCase ):
      if "handwritten" in checkpoint_url:
            SCREAMING_SNAKE_CASE_      =     "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_      =     "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_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw ).convert("RGB" )
      return im


@torch.no_grad()
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     ViTConfig(image_size=3_8_4   ,					qkv_bias=__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            SCREAMING_SNAKE_CASE_      =     7_6_8
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     4_0_9_6
            SCREAMING_SNAKE_CASE_      =     2_4
            SCREAMING_SNAKE_CASE_      =     1_6
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
      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_      =     False
            SCREAMING_SNAKE_CASE_      =     "relu"
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     True
            SCREAMING_SNAKE_CASE_      =     False
            SCREAMING_SNAKE_CASE_      =     False

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

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

      SCREAMING_SNAKE_CASE_      =     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_      =     state_dict.pop(__UpperCamelCase )
            if key.startswith("decoder" ) and "output_projection" not in key:
                  SCREAMING_SNAKE_CASE_      =     val
            else:
                  SCREAMING_SNAKE_CASE_      =     val

    # load state dict
      model.load_state_dict(__UpperCamelCase )

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

      SCREAMING_SNAKE_CASE_      =     processor(images=prepare_img(__UpperCamelCase )   ,					return_tensors="pt" ).pixel_values

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

      SCREAMING_SNAKE_CASE_      =     torch.Size([1, 1, 5_0_2_6_5] )
      if "trocr-base-handwritten" in checkpoint_url:
            SCREAMING_SNAKE_CASE_      =     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:
            SCREAMING_SNAKE_CASE_      =     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:
            SCREAMING_SNAKE_CASE_      =     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:
            SCREAMING_SNAKE_CASE_      =     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, :1_0]   ,					__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__":
      A   :     Dict       =				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."
      )
      A   :     List[str]       =				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


