






"""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_	:    List[Any]            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			dct.pop(lowerCamelCase__		)
       UpperCAmelCase	    =			val






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       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(lowerCamelCase__	,      stream=lowerCamelCase__		).raw		).convert("""RGB"""		)
       return im






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			ViTConfig(image_size=384	,      qkv_bias=lowerCamelCase__		)
       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(lowerCamelCase__	,      add_pooling_layer=lowerCamelCase__		)
       UpperCAmelCase	    =			TrOCRForCausalLM(lowerCamelCase__		)
       UpperCAmelCase	    =			VisionEncoderDecoderModel(encoder=lowerCamelCase__	,      decoder=lowerCamelCase__		)
       model.eval()

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

       UpperCAmelCase	    =			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():
              UpperCAmelCase	    =			state_dict.pop(lowerCamelCase__		)
              if key.startswith("""decoder"""		) and "output_projection" not in key:
                     UpperCAmelCase	    =			val
              else:
                     UpperCAmelCase	    =			val

    # load state dict
       model.load_state_dict(lowerCamelCase__		)

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

       UpperCAmelCase	    =			processor(images=prepare_img(lowerCamelCase__		)	,      return_tensors="""pt"""		).pixel_values

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

       UpperCAmelCase	    =			torch.Size([1, 1, 50265]		)
       if "trocr-base-handwritten" in checkpoint_url:
              UpperCAmelCase	    =			torch.tensor(
                  [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]		)
       elif "trocr-large-handwritten" in checkpoint_url:
              UpperCAmelCase	    =			torch.tensor(
                  [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]		)
       elif "trocr-base-printed" in checkpoint_url:
              UpperCAmelCase	    =			torch.tensor(
                  [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]		)
       elif "trocr-large-printed" in checkpoint_url:
              UpperCAmelCase	    =			torch.tensor(
                  [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]		)

       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_	:    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.'''
 )
 lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()
 convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""







import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor





class   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = True ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = [0.48_145_466, 0.4_578_275, 0.40_821_073] ,		snake_case__ = [0.26_862_954, 0.26_130_258, 0.27_577_711] ,		snake_case__ = True ,		snake_case__=7 ,		snake_case__=30 ,		snake_case__=4_00 ,		snake_case__=3 ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_88}
         UpperCAmelCase	    =			size_divisor
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			image_mean
         UpperCAmelCase	    =			image_std
         UpperCAmelCase	    =			do_pad
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			min_resolution
         UpperCAmelCase	    =			max_resolution



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=False				)			->			Optional[int]:



         """simple docstring"""





         if not batched:
                UpperCAmelCase	    =			self.size["""shortest_edge"""]
                UpperCAmelCase	    =			image_inputs[0]
                if isinstance(snake_case__ ,		Image.Image				):
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.size
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.shape[1], image.shape[2]
                UpperCAmelCase	    =			size / min(snake_case__ ,		snake_case__				)
                if h < w:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			size, scale * w
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			scale * h, size

                UpperCAmelCase	    =			int((13_33 / 8_00) * size				)
                if max(snake_case__ ,		snake_case__				) > max_size:
                       UpperCAmelCase	    =			max_size / max(snake_case__ ,		snake_case__				)
                       UpperCAmelCase	    =			newh * scale
                       UpperCAmelCase	    =			neww * scale

                UpperCAmelCase    ,       UpperCAmelCase	    =			int(newh + 0.5				), int(neww + 0.5				)
                UpperCAmelCase    ,       UpperCAmelCase	    =			(
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

         else:
                UpperCAmelCase	    =			[]
                for image in image_inputs:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_expected_values([image]				)
                       expected_values.append((expected_height, expected_width)				)
                UpperCAmelCase	    =			max(snake_case__ ,		key=lambda snake_case__				: item[0]				)[0]
                UpperCAmelCase	    =			max(snake_case__ ,		key=lambda snake_case__				: item[1]				)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					int				=	BridgeTowerImageProcessor if is_vision_available() else None



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			BridgeTowerImageProcessingTester(self				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_mean"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_std"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_normalize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_resize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size_divisor"""				)				)



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		Image.Image				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		numpify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		np.ndarray				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		torchify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		torch.Tensor				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)
