






'''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()
SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(     __UpperCamelCase		,						__UpperCamelCase   )->			int:
      UpperCamelCase      = []
      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		,						__UpperCamelCase   )->			List[str]:
      for i in range(encoder_config.num_hidden_layers   ):
            # queries, keys and values (only weights, no biases)
            UpperCamelCase      = state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"   )

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







def    lowercase__			(     __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = dct.pop(lowercase_   )
      UpperCamelCase      = val







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







@torch.no_grad()
def    lowercase__			(     __UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = ViTConfig(image_size=384		,						qkv_bias=lowercase_   )
      UpperCamelCase      = TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            UpperCamelCase      = 768
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            UpperCamelCase      = 1024
            UpperCamelCase      = 4096
            UpperCamelCase      = 24
            UpperCamelCase      = 16
            UpperCamelCase      = 1024
      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:
            UpperCamelCase      = False
            UpperCamelCase      = """relu"""
            UpperCamelCase      = 1024
            UpperCamelCase      = True
            UpperCamelCase      = False
            UpperCamelCase      = False

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

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

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

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

    # load state dict
      model.load_state_dict(lowercase_   )

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

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

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

      UpperCamelCase      = torch.Size([1, 1, 50265]   )
      if "trocr-base-handwritten" in checkpoint_url:
            UpperCamelCase      = torch.tensor(
                [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]   )
      elif "trocr-large-handwritten" in checkpoint_url:
            UpperCamelCase      = torch.tensor(
                [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]   )
      elif "trocr-base-printed" in checkpoint_url:
            UpperCamelCase      = torch.tensor(
                [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]   )
      elif "trocr-large-printed" in checkpoint_url:
            UpperCamelCase      = torch.tensor(
                [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]   )

      if "stage1" not in checkpoint_url:
            assert logits.shape == expected_shape, "Shape of logits not as expected"
            assert torch.allclose(logits[0, 0, :10]		,						lowercase_		,						atol=1E-3   ), "First elements of logits not as expected"

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


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)








'''simple docstring'''

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
     import PIL

# soft dependency
if is_pytesseract_available():
     import pytesseract

SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			int:
      return [
          int(1000 * (box[0] / width)   ),
          int(1000 * (box[1] / height)   ),
          int(1000 * (box[2] / width)   ),
          int(1000 * (box[3] / height)   ),
      ]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = None   )->			Dict:
      UpperCamelCase      = tesseract_config if tesseract_config is not None else """"""

      # apply OCR
      UpperCamelCase      = to_pil_image(__UpperCamelCase   )
      UpperCamelCase  ,UpperCamelCase      = pil_image.size
      UpperCamelCase      = pytesseract.image_to_data(__UpperCamelCase		,						lang=__UpperCamelCase		,						output_type="""dict"""		,						config=__UpperCamelCase   )
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

      # filter empty words and corresponding coordinates
      UpperCamelCase      = [idx for idx, word in enumerate(__UpperCamelCase   ) if not word.strip()]
      UpperCamelCase      = [word for idx, word in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      UpperCamelCase      = []
      for x, y, w, h in zip(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   ):
            UpperCamelCase      = [x, y, x + w, y + h]
            actual_boxes.append(__UpperCamelCase   )

      # finally, normalize the bounding boxes
      UpperCamelCase      = []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )   )

      assert len(__UpperCamelCase   ) == len(__UpperCamelCase   ), "Not as many words as there are bounding boxes"

      return words, normalized_boxes






class     a_						(      lowerCamelCase    ):
  lowercase							=				["""pixel_values"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ""      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = size if size is not None else {"""height""": 224, """width""": 224}
        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = resample
        UpperCamelCase      = apply_ocr
        UpperCamelCase      = ocr_lang
        UpperCamelCase      = tesseract_config

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)
        if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"				)
        UpperCamelCase      = (size["""height"""], size["""width"""])
        return resize(_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE      ,		data_format=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				PIL.Image.Image:



        """simple docstring"""






        UpperCamelCase      = do_resize if do_resize is not None else self.do_resize
        UpperCamelCase      = size if size is not None else self.size
        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = resample if resample is not None else self.resample
        UpperCamelCase      = apply_ocr if apply_ocr is not None else self.apply_ocr
        UpperCamelCase      = ocr_lang if ocr_lang is not None else self.ocr_lang
        UpperCamelCase      = tesseract_config if tesseract_config is not None else self.tesseract_config

        UpperCamelCase      = make_list_of_images(_SCREAMING_SNAKE_CASE				)

        if not valid_images(_SCREAMING_SNAKE_CASE				):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray."""				)

        if do_resize and size is None:
              raise ValueError("""Size must be specified if do_resize is True."""				)

        # All transformations expect numpy arrays.
        UpperCamelCase      = [to_numpy_array(_SCREAMING_SNAKE_CASE				) for image in images]

        if apply_ocr:
              requires_backends(self      ,		"""pytesseract"""				)
              UpperCamelCase      = []
              UpperCamelCase      = []
              for image in images:
                    UpperCamelCase  ,UpperCamelCase      = apply_tesseract(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
                    words_batch.append(_SCREAMING_SNAKE_CASE				)
                    boxes_batch.append(_SCREAMING_SNAKE_CASE				)

        if do_resize:
              UpperCamelCase      = [self.resize(image=_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE				) for image in images]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        UpperCamelCase      = [flip_channel_order(_SCREAMING_SNAKE_CASE				) for image in images]
        UpperCamelCase      = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for image in images]

        UpperCamelCase      = BatchFeature(data={"""pixel_values""": images}      ,		tensor_type=_SCREAMING_SNAKE_CASE				)

        if apply_ocr:
              UpperCamelCase      = words_batch
              UpperCamelCase      = boxes_batch
        return data
