



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()
_snake_case				=					logging.get_logger(__name__)
def     _a							(     __lowercase				, __lowercase		)     ->						Optional[int]:

							"""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     _a							(     __lowercase				, __lowercase		)     ->						Union[str, Any]:

							"""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     _a							(     __lowercase				, __lowercase				, __lowercase		)     ->						Optional[int]:

							"""simple docstring"""





							__UpperCamelCase          =  dct.pop(_lowerCAmelCase		)
							__UpperCamelCase          =  val
def     _a							(     __lowercase		)     ->						Any:

							"""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     _a							(     __lowercase				, __lowercase		)     ->						List[str]:

							"""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, 5_0265]		)
							if "trocr-base-handwritten" in checkpoint_url:
														__UpperCamelCase          =  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:
														__UpperCamelCase          =  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:
														__UpperCamelCase          =  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:
														__UpperCamelCase          =  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]				, _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__":
			_snake_case				=					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.'
			)
			_snake_case				=					parser.parse_args()
			convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







def 						_lowerCAmelCase							(     _lowerCAmelCase = 100			)    ->						int:


						'''simple docstring'''






						__snake_case				=		n * (n + 1) * (2 * n + 1) / 6
						__snake_case				=		(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
	print(f'''{solution() = }''')





