



"""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()
__a      =				logging.get_logger(__name__)
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Dict						=			[]
						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_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


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

												snake_case_ :Dict						=			in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												snake_case_ :Dict						=			in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												snake_case_ :Tuple						=			in_proj_weight[
												    -encoder_config.hidden_size :, :
												]
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			dct.pop(_SCREAMING_SNAKE_CASE	)
						snake_case_ :Dict						=			val
def    A_  (			_lowercase	):







						'''simple docstring'''


						if "handwritten" in checkpoint_url:
												snake_case_ :Tuple						=			'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:
												snake_case_ :Any						=			'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
						snake_case_ :str						=			Image.open(requests.get(_SCREAMING_SNAKE_CASE,					stream=_SCREAMING_SNAKE_CASE	).raw	).convert("""RGB"""	)
						return im
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			ViTConfig(image_size=384,					qkv_bias=_SCREAMING_SNAKE_CASE	)
						snake_case_ :Tuple						=			TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												snake_case_ :int						=			768
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												snake_case_ :str						=			1024
												snake_case_ :Optional[int]						=			4096
												snake_case_ :Tuple						=			24
												snake_case_ :List[Any]						=			16
												snake_case_ :Optional[Any]						=			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:
												snake_case_ :Optional[Any]						=			False
												snake_case_ :Optional[Any]						=			'relu'
												snake_case_ :Any						=			1024
												snake_case_ :List[Any]						=			True
												snake_case_ :Dict						=			False
												snake_case_ :Optional[Any]						=			False

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

						# load state_dict of original model, rename some keys
						snake_case_ :Optional[Any]						=			torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE,					map_location="""cpu""",					check_hash=_SCREAMING_SNAKE_CASE	)['model']

						snake_case_ :Union[str, Any]						=			create_rename_keys(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)
						for src, dest in rename_keys:
												rename_key(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)
						read_in_q_k_v(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)

						# 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():
												snake_case_ :List[Any]						=			state_dict.pop(_SCREAMING_SNAKE_CASE	)
												if key.startswith("""decoder"""	) and "output_projection" not in key:
																		snake_case_ :List[Any]						=			val
												else:
																		snake_case_ :Dict						=			val

												# load state dict
						model.load_state_dict(_SCREAMING_SNAKE_CASE	)

						# Check outputs on an image
						snake_case_ :Optional[Any]						=			ViTImageProcessor(size=encoder_config.image_size	)
						snake_case_ :Dict						=			RobertaTokenizer.from_pretrained("""roberta-large"""	)
						snake_case_ :List[str]						=			TrOCRProcessor(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE	)

						snake_case_ :Optional[Any]						=			processor(images=prepare_img(_SCREAMING_SNAKE_CASE	),					return_tensors="""pt"""	).pixel_values

						# verify logits
						snake_case_ :List[str]						=			torch.tensor([[model.config.decoder.decoder_start_token_id]]	)
						snake_case_ :List[Any]						=			model(pixel_values=_SCREAMING_SNAKE_CASE,					decoder_input_ids=_SCREAMING_SNAKE_CASE	)
						snake_case_ :Dict						=			outputs.logits

						snake_case_ :Dict						=			torch.Size([1, 1, 50265]	)
						if "trocr-base-handwritten" in checkpoint_url:
												snake_case_ :Optional[Any]						=			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:
												snake_case_ :Tuple						=			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:
												snake_case_ :Dict						=			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:
												snake_case_ :Optional[Any]						=			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],					_SCREAMING_SNAKE_CASE,					atol=1e-3	), "First elements of logits not as expected"

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


if __name__ == "__main__":
						__a      =				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      =				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad






class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							1_0






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							[1, 2, 3, 4]
         lowerCAmelCase__			:Tuple											=							[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
         lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							''
         lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							(
             'It was the year of Our Lord one thousand seven hundred and '
             'seventy-five\n\nSpiritual revelations were conceded to England '
             'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
         )
         lowerCAmelCase__							,				lowerCAmelCase__			:str											=							process_story(__UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							[
             'It was the year of Our Lord one thousand seven hundred and seventy-five.',
             'Spiritual revelations were conceded to England at that favoured period, as at this.',
         ]
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							['It was the best of times.']
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							torch.tensor([1, 2, 3, 4]      )
         lowerCAmelCase__			:List[str]											=							torch.tensor([1, 1, 1, 1]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 0      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]      )
         lowerCAmelCase__			:Optional[int]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 2_3      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([8, 2, 3, 4, 1, 1, 1]      )
         lowerCAmelCase__			:Optional[Any]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 1      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							1_0_1
         lowerCAmelCase__			:str											=							torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]      )
         lowerCAmelCase__			:Any											=							torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]      )

         lowerCAmelCase__			:List[Any]											=							compute_token_type_ids(__UpperCAmelCase    , __UpperCAmelCase      )
         np.testing.assert_array_equal(__UpperCAmelCase    , __UpperCAmelCase      )
