'''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()
lowerCAmelCase_ : int						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      []
      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 (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str]				)	->							Any:
      '''simple docstring'''


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

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


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[int]				)	->							str:
      '''simple docstring'''


      lowercase_     =      dct.pop(__lowerCamelCase				)
      lowercase_     =      val


def __a (	__lowerCamelCase			:						int				)	->							Dict:
      '''simple docstring'''


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


@torch.no_grad()
def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						List[str]				)	->							str:
      '''simple docstring'''


      lowercase_     =      ViTConfig(image_size=384 ,		qkv_bias=__lowerCamelCase				)
      lowercase_     =      TrOCRConfig()

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

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

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

      lowercase_     =      create_rename_keys(__lowerCamelCase ,		__lowerCamelCase				)
      for src, dest in rename_keys:
            rename_key(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
      read_in_q_k_v(__lowerCamelCase ,		__lowerCamelCase				)

      # 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():
            lowercase_     =      state_dict.pop(__lowerCamelCase				)
            if key.startswith("decoder"				) and "output_projection" not in key:
                  lowercase_     =      val
            else:
                  lowercase_     =      val

    # load state dict
      model.load_state_dict(__lowerCamelCase				)

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

      lowercase_     =      processor(images=prepare_img(__lowerCamelCase				) ,		return_tensors="pt"				).pixel_values

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

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

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


if __name__ == "__main__":
       lowerCAmelCase_ : Union[str, 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."
       )
       lowerCAmelCase_ : Any						  =      parser.parse_args()
       convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''

from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	Callable   ,		__lowerCAmelCase				:	Optional[Features] = None   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[dict] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					List[Any]:
         super().__init__(
             features=__lowerCAmelCase   ,		cache_dir=__lowerCAmelCase   ,		keep_in_memory=__lowerCAmelCase   ,		streaming=__lowerCAmelCase   ,		num_proc=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)
         lowercase_     =      Generator(
             cache_dir=__lowerCAmelCase   ,		features=__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		gen_kwargs=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[str]:
         # Build iterable dataset
         if self.streaming:
               lowercase_     =      self.builder.as_streaming_dataset(split="train")
         # Build regular (map-style) dataset
         else:
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None

               self.builder.download_and_prepare(
                   download_config=__lowerCAmelCase   ,		download_mode=__lowerCAmelCase   ,		verification_mode=__lowerCAmelCase   ,		base_path=__lowerCAmelCase   ,		num_proc=self.num_proc   ,		)
               lowercase_     =      self.builder.as_dataset(
                   split="train"   ,		verification_mode=__lowerCAmelCase   ,		in_memory=self.keep_in_memory)
         return dataset






