


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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		[]
						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			)    ->						Union[str, Any]:


						'''simple docstring'''






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

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		dct.pop(_lowerCAmelCase			)
						__snake_case				=		val





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						if "handwritten" in checkpoint_url:
												__snake_case				=		"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				=		"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				=		Image.open(requests.get(_lowerCAmelCase   ,						stream=_lowerCAmelCase			).raw			).convert("RGB"			)
						return im





@torch.no_grad()
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						__snake_case				=		ViTConfig(image_size=384   ,						qkv_bias=_lowerCAmelCase			)
						__snake_case				=		TrOCRConfig()

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

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

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

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

    # load state dict
						model.load_state_dict(_lowerCAmelCase			)

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

						__snake_case				=		processor(images=prepare_img(_lowerCAmelCase			)   ,						return_tensors="pt"			).pixel_values

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

						__snake_case				=		torch.Size([1, 1, 5_0265]			)
						if "trocr-base-handwritten" in checkpoint_url:
												__snake_case				=		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:
												__snake_case				=		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:
												__snake_case				=		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:
												__snake_case				=		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__":
	A				:       Optional[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.'
	)
	A				:       Optional[Any]      =  parser.parse_args()
	convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from __future__ import annotations





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						list[int]:


						'''simple docstring'''






						__snake_case				=		2
						__snake_case				=		[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase			)
						if n > 1:
												factors.append(_lowerCAmelCase			)
						return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()





