






'''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:      Union[str, Any]  				=				logging.get_logger(__name__)






def 	_UpperCAmelCase    (  a		:	List[str]						,			a		:	Optional[int]							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   str        =			[]
						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 	_UpperCAmelCase    (  a		:	str						,			a		:	List[Any]							)			->			List[Any]:





						"""simple docstring"""
						for i in range(encoder_config.num_hidden_layers							):
												# queries, keys and values (only weights, no biases)
												lowercase_      :   Optional[int]        =			state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight"							)

												lowercase_      :   List[Any]        =			in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												lowercase_      :   Tuple        =			in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												lowercase_      :   int        =			in_proj_weight[
												    -encoder_config.hidden_size :, :
												]






def 	_UpperCAmelCase    (  a		:	int						,			a		:	List[Any]						,			a		:	Tuple							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			dct.pop(a							)
						lowercase_      :   Union[str, Any]        =			val






def 	_UpperCAmelCase    (  a		:	int							)			->			Dict:





						"""simple docstring"""
						if "handwritten" in checkpoint_url:
												lowercase_      :   Optional[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:
												lowercase_      :   List[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'
						lowercase_      :   str        =			Image.open(requests.get(a						,			stream=a							).raw							).convert('RGB'							)
						return im






@torch.no_grad()
def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	Optional[Any]							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   Dict        =			ViTConfig(image_size=3_8_4						,			qkv_bias=a							)
						lowercase_      :   List[str]        =			TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												lowercase_      :   Optional[int]        =			7_6_8
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												lowercase_      :   int        =			1_0_2_4
												lowercase_      :   str        =			4_0_9_6
												lowercase_      :   Optional[Any]        =			2_4
												lowercase_      :   Dict        =			1_6
												lowercase_      :   List[Any]        =			1_0_2_4
						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:
												lowercase_      :   List[Any]        =			False
												lowercase_      :   Union[str, Any]        =			'relu'
												lowercase_      :   Optional[Any]        =			1_0_2_4
												lowercase_      :   Optional[Any]        =			True
												lowercase_      :   Optional[int]        =			False
												lowercase_      :   str        =			False

						# load HuggingFace model
						lowercase_      :   Optional[int]        =			ViTModel(a						,			add_pooling_layer=a							)
						lowercase_      :   Tuple        =			TrOCRForCausalLM(a							)
						lowercase_      :   Optional[int]        =			VisionEncoderDecoderModel(encoder=a						,			decoder=a							)
						model.eval()

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

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

    # load state dict
						model.load_state_dict(a							)

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

						lowercase_      :   Optional[int]        =			processor(images=prepare_img(a							)						,			return_tensors='pt'							).pixel_values

						# verify logits
						lowercase_      :   int        =			torch.tensor([[model.config.decoder.decoder_start_token_id]]							)
						lowercase_      :   List[str]        =			model(pixel_values=a						,			decoder_input_ids=a							)
						lowercase_      :   Optional[int]        =			outputs.logits

						lowercase_      :   List[Any]        =			torch.Size([1, 1, 5_0_2_6_5]							)
						if "trocr-base-handwritten" in checkpoint_url:
												lowercase_      :   Tuple        =			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:
												lowercase_      :   Tuple        =			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:
												lowercase_      :   Optional[Any]        =			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:
												lowercase_      :   List[Any]        =			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, :1_0]						,			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__":
						A:      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."
						)
						A:      Union[str, Any]  				=				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			float:





						"""simple docstring"""

						def get_matched_characters(a		:	str						,			a		:	str							) -> str:
												lowercase_      :   Union[str, Any]        =			[]
												lowercase_      :   Tuple        =			min(len(_stra							)						,			len(_stra							)							) // 2
												for i, l in enumerate(_stra							):
																		lowercase_      :   Optional[int]        =			int(max(0						,			i - limit							)							)
																		lowercase_      :   Union[str, Any]        =			int(min(i + limit + 1						,			len(_stra							)							)							)
																		if l in _stra[left:right]:
																								matched.append(a							)
																								lowercase_      :   Union[str, Any]        =			f"{_stra[0:_stra.index(a							)]} {_stra[_stra.index(a							) + 1:]}"

												return "".join(a							)

						# matching characters
						lowercase_      :   Union[str, Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[int]        =			len(a							)

						# transposition
						lowercase_      :   Dict        =			(
						    len([(ca, ca) for ca, ca in zip(a						,			a							) if ca != ca]							) // 2
						)

						if not match_count:
												lowercase_      :   List[str]        =			0.0
						else:
												lowercase_      :   Any        =			(
												    1
												    / 3
												    * (
												        match_count / len(a							)
												        + match_count / len(a							)
												        + (match_count - transpositions) / match_count
												    )
												)

						# common prefix up to 4 characters
						lowercase_      :   Optional[Any]        =			0
						for ca, ca in zip(stra[:4]						,			stra[:4]							):
												if ca == ca:
																		prefix_len += 1
												else:
																		break

						return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler("hello", "world"))
