



"""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__              =							logging.get_logger(__name__)






def 			snake_case_    ( A_   :						int,							A_   :						Tuple		):


       '''simple docstring'''





       _lowerCamelCase		:							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 			snake_case_    ( A_   :						List[Any],							A_   :						Tuple		):


       '''simple docstring'''





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

              _lowerCamelCase		:							Dict            =					in_proj_weight[
                  : encoder_config.hidden_size, :
              ]
              _lowerCamelCase		:							Dict            =					in_proj_weight[
                  encoder_config.hidden_size : encoder_config.hidden_size * 2, :
              ]
              _lowerCamelCase		:							str            =					in_proj_weight[
                  -encoder_config.hidden_size :, :
              ]






def 			snake_case_    ( A_   :						List[str],							A_   :						int,							A_   :						int		):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					dct.pop(A_		)
       _lowerCamelCase		:							str            =					val






def 			snake_case_    ( A_   :						Dict		):


       '''simple docstring'''





       if "handwritten" in checkpoint_url:
              _lowerCamelCase		:							int            =					'''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:
              _lowerCamelCase		:							List[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'''
       _lowerCamelCase		:							Dict            =					Image.open(requests.get(A_,							stream=A_		).raw		).convert('''RGB'''		)
       return im






@torch.no_grad()
def 			snake_case_    ( A_   :						Tuple,							A_   :						Dict		):


       '''simple docstring'''





       _lowerCamelCase		:							Union[str, Any]            =					ViTConfig(image_size=3_84,							qkv_bias=A_		)
       _lowerCamelCase		:							int            =					TrOCRConfig()

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

       # load HuggingFace model
       _lowerCamelCase		:							List[Any]            =					ViTModel(A_,							add_pooling_layer=A_		)
       _lowerCamelCase		:							List[str]            =					TrOCRForCausalLM(A_		)
       _lowerCamelCase		:							List[Any]            =					VisionEncoderDecoderModel(encoder=A_,							decoder=A_		)
       model.eval()

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

       _lowerCamelCase		:							Tuple            =					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():
              _lowerCamelCase		:							Union[str, Any]            =					state_dict.pop(A_		)
              if key.startswith('''decoder'''		) and "output_projection" not in key:
                     _lowerCamelCase		:							Dict            =					val
              else:
                     _lowerCamelCase		:							Dict            =					val

    # load state dict
       model.load_state_dict(A_		)

       # Check outputs on an image
       _lowerCamelCase		:							str            =					ViTImageProcessor(size=encoder_config.image_size		)
       _lowerCamelCase		:							List[str]            =					RobertaTokenizer.from_pretrained('''roberta-large'''		)
       _lowerCamelCase		:							List[str]            =					TrOCRProcessor(A_,							A_		)

       _lowerCamelCase		:							Dict            =					processor(images=prepare_img(A_		),							return_tensors='''pt'''		).pixel_values

       # verify logits
       _lowerCamelCase		:							str            =					torch.tensor([[model.config.decoder.decoder_start_token_id]]		)
       _lowerCamelCase		:							Tuple            =					model(pixel_values=A_,							decoder_input_ids=A_		)
       _lowerCamelCase		:							Optional[Any]            =					outputs.logits

       _lowerCamelCase		:							str            =					torch.Size([1, 1, 5_02_65]		)
       if "trocr-base-handwritten" in checkpoint_url:
              _lowerCamelCase		:							Dict            =					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:
              _lowerCamelCase		:							Optional[int]            =					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:
              _lowerCamelCase		:							Tuple            =					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:
              _lowerCamelCase		:							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__":
   lowerCAmelCase__              =							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__              =							parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 				lowercase_					(     __A			:							Tuple      )							->   List[List[ImageInput]]:



 """simple docstring"""




 if isinstance(__A       ,   (list, tuple)      ) and isinstance(videos[0]       ,   (list, tuple)      ) and is_valid_image(videos[0][0]      ):
  return videos

 elif isinstance(__A       ,   (list, tuple)      ) and is_valid_image(videos[0]      ):
  return [videos]

 elif is_valid_image(__A      ):
  return [[videos]]

 raise ValueError(F'Could not make batched video from {videos}'      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''pixel_values''']






      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Union[int, float] = 1 / 255  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase       : Tuple						=size if size is not None else {'''shortest_edge''': 224}
       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : List[str]						=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase       : List[Any]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

       lowercase       : Tuple						=do_resize
       lowercase       : Any						=size
       lowercase       : Optional[Any]						=do_center_crop
       lowercase       : str						=crop_size
       lowercase       : Any						=resample
       lowercase       : List[Any]						=do_rescale
       lowercase       : Dict						=rescale_factor
       lowercase       : List[str]						=do_normalize
       lowercase       : Optional[int]						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase       : List[Any]						=image_std if image_std is not None else IMAGENET_STANDARD_STD






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		List[str]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       if "shortest_edge" in size:
        lowercase       : int						=get_resize_output_image_size(UpperCAmelCase  ,   size['''shortest_edge''']  ,   default_to_square=UpperCAmelCase		)
       elif "height" in size and "width" in size:
        lowercase       : str						=(size['''height'''], size['''width'''])
       else:
        raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
       return resize(UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Optional[Any]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : List[str]						=get_size_dict(UpperCAmelCase		)
       if "height" not in size or "width" not in size:
        raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
       return center_crop(UpperCAmelCase  ,   size=(size['''height'''], size['''width'''])  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[int, float]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	List[Any]:


       '''simple docstring'''

       return rescale(UpperCAmelCase  ,   scale=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		int  ,   )	->	np.ndarray:


       '''simple docstring'''

       return normalize(UpperCAmelCase  ,   mean=UpperCAmelCase  ,   std=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[ChannelDimension] = ChannelDimension.FIRST  ,   )	->	np.ndarray:


       '''simple docstring'''

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

       if do_center_crop and crop_size is None:
        raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

       if do_rescale and rescale_factor is None:
        raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

       # All transformations expect numpy arrays.
       lowercase       : Dict						=to_numpy_array(UpperCAmelCase		)

       if do_resize:
        lowercase       : Union[str, Any]						=self.resize(image=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase		)

       if do_center_crop:
        lowercase       : Optional[Any]						=self.center_crop(UpperCAmelCase  ,   size=UpperCAmelCase		)

       if do_rescale:
        lowercase       : Dict						=self.rescale(image=UpperCAmelCase  ,   scale=UpperCAmelCase		)

       if do_normalize:
        lowercase       : int						=self.normalize(image=UpperCAmelCase  ,   mean=UpperCAmelCase  ,   std=UpperCAmelCase		)

       lowercase       : Optional[Any]						=to_channel_dimension_format(UpperCAmelCase  ,   UpperCAmelCase		)
       return image






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[str, TensorType]] = None  ,   UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST  ,   **UpperCAmelCase      :		Optional[int]  ,   )	->	PIL.Image.Image:


       '''simple docstring'''

       lowercase       : Any						=do_resize if do_resize is not None else self.do_resize
       lowercase       : Union[str, Any]						=resample if resample is not None else self.resample
       lowercase       : Tuple						=do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase       : List[Any]						=do_rescale if do_rescale is not None else self.do_rescale
       lowercase       : str						=rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase       : List[str]						=do_normalize if do_normalize is not None else self.do_normalize
       lowercase       : List[str]						=image_mean if image_mean is not None else self.image_mean
       lowercase       : Optional[int]						=image_std if image_std is not None else self.image_std

       lowercase       : Optional[Any]						=size if size is not None else self.size
       lowercase       : Any						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : Union[str, Any]						=crop_size if crop_size is not None else self.crop_size
       lowercase       : Optional[int]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

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

       lowercase       : List[str]						=make_batched(UpperCAmelCase		)

       lowercase       : Union[str, Any]						=[
           [
               self._preprocess_image(
                   image=UpperCAmelCase  ,   do_resize=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   do_center_crop=UpperCAmelCase  ,   crop_size=UpperCAmelCase  ,   do_rescale=UpperCAmelCase  ,   rescale_factor=UpperCAmelCase  ,   do_normalize=UpperCAmelCase  ,   image_mean=UpperCAmelCase  ,   image_std=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   )
               for img in video
           ]
           for video in videos
       ]

       lowercase       : Dict						={'''pixel_values''': videos}
       return BatchFeature(data=UpperCAmelCase  ,   tensor_type=UpperCAmelCase		)



