





'''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   : Tuple             =			logging.get_logger(__name__)





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Optional[Any]:
						_snake_case:   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       lowercase_      (		lowercase__				,  lowercase__						)   ->Any:
						for i in range(encoder_config.num_hidden_layers						):
												# queries, keys and values (only weights, no biases)
												_snake_case:   List[str]  					=		state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''						)

												_snake_case:   Any  					=		in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												_snake_case:   Union[str, Any]  					=		in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												_snake_case:   str  					=		in_proj_weight[
												    -encoder_config.hidden_size :, :
												]





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__						)   ->Dict:
						_snake_case:   Any  					=		dct.pop(lowercase__						)
						_snake_case:   Optional[Any]  					=		val





def       lowercase_      (		lowercase__						)   ->int:
						if "handwritten" in checkpoint_url:
												_snake_case:   Any  					=		'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:   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'
						_snake_case:   str  					=		Image.open(requests.get(lowercase__				,  stream=lowercase__						).raw						).convert('RGB'						)
						return im





@torch.no_grad()
def       lowercase_      (		lowercase__				,  lowercase__						)   ->Tuple:
						_snake_case:   Optional[Any]  					=		ViTConfig(image_size=384				,  qkv_bias=lowercase__						)
						_snake_case:   int  					=		TrOCRConfig()

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

						# load HuggingFace model
						_snake_case:   List[str]  					=		ViTModel(lowercase__				,  add_pooling_layer=lowercase__						)
						_snake_case:   int  					=		TrOCRForCausalLM(lowercase__						)
						_snake_case:   Dict  					=		VisionEncoderDecoderModel(encoder=lowercase__				,  decoder=lowercase__						)
						model.eval()

						# load state_dict of original model, rename some keys
						_snake_case:   Tuple  					=		torch.hub.load_state_dict_from_url(lowercase__				,  map_location='cpu'				,  check_hash=lowercase__						)['model']

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

						# 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[str]  					=		state_dict.pop(lowercase__						)
												if key.startswith('decoder'						) and "output_projection" not in key:
																		_snake_case:   str  					=		val
												else:
																		_snake_case:   Optional[Any]  					=		val

    # load state dict
						model.load_state_dict(lowercase__						)

						# Check outputs on an image
						_snake_case:   int  					=		ViTImageProcessor(size=encoder_config.image_size						)
						_snake_case:   str  					=		RobertaTokenizer.from_pretrained('roberta-large'						)
						_snake_case:   Tuple  					=		TrOCRProcessor(lowercase__				,  lowercase__						)

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

						# verify logits
						_snake_case:   Optional[int]  					=		torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
						_snake_case:   str  					=		model(pixel_values=lowercase__				,  decoder_input_ids=lowercase__						)
						_snake_case:   Tuple  					=		outputs.logits

						_snake_case:   Dict  					=		torch.Size([1, 1, 5_0265]						)
						if "trocr-base-handwritten" in checkpoint_url:
												_snake_case:   List[Any]  					=		torch.tensor(
												    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]						)
						elif "trocr-large-handwritten" in checkpoint_url:
												_snake_case:   str  					=		torch.tensor(
												    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]						)
						elif "trocr-base-printed" in checkpoint_url:
												_snake_case:   Optional[int]  					=		torch.tensor(
												    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]						)
						elif "trocr-large-printed" in checkpoint_url:
												_snake_case:   List[Any]  					=		torch.tensor(
												    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]						)

						if "stage1" not in checkpoint_url:
												assert logits.shape == expected_shape, "Shape of logits not as expected"
												assert torch.allclose(logits[0, 0, :10]				,  lowercase__				,  atol=1e-3						), "First elements of logits not as expected"

						Path(lowercase__						).mkdir(exist_ok=lowercase__						)
						print(F'''Saving model to {pytorch_dump_folder_path}'''						)
						model.save_pretrained(lowercase__						)
						print(F'''Saving processor to {pytorch_dump_folder_path}'''						)
						processor.save_pretrained(lowercase__						)


if __name__ == "__main__":
		A   : Optional[int]             =			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   : Any             =			parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












'''simple docstring'''






from ..utils import DummyObject, requires_backends







class 			lowerCamelCase					(						metaclass=__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["torch", "scipy"]


					def __init__(					self	:  Tuple       ,     *__snake_case	:  List[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											requires_backends(self       ,     ['torch', 'scipy']       )


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )





