



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()
lowerCAmelCase__		:					Optional[int]							=logging.get_logger(__name__)






def      __lowercase       (			a__							,       a__       )    ->			Tuple:
							__SCREAMING_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      __lowercase       (			a__							,       a__       )    ->			List[str]:
							for i in range(encoder_config.num_hidden_layers       ):
														# queries, keys and values (only weights, no biases)
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""       )

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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			str:
							__SCREAMING_SNAKE_CASE									= dct.pop(a__       )
							__SCREAMING_SNAKE_CASE									= val






def      __lowercase       (			a__       )    ->			List[str]:
							if "handwritten" in checkpoint_url:
														__SCREAMING_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:
														__SCREAMING_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'
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       ).convert('RGB'       )
							return im






@torch.no_grad()
def      __lowercase       (			a__							,       a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= ViTConfig(image_size=3_84							,       qkv_bias=a__       )
							__SCREAMING_SNAKE_CASE									= TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														__SCREAMING_SNAKE_CASE									= 7_68
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														__SCREAMING_SNAKE_CASE									= 10_24
														__SCREAMING_SNAKE_CASE									= 40_96
														__SCREAMING_SNAKE_CASE									= 24
														__SCREAMING_SNAKE_CASE									= 16
														__SCREAMING_SNAKE_CASE									= 10_24
							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:
														__SCREAMING_SNAKE_CASE									= False
														__SCREAMING_SNAKE_CASE									= 'relu'
														__SCREAMING_SNAKE_CASE									= 10_24
														__SCREAMING_SNAKE_CASE									= True
														__SCREAMING_SNAKE_CASE									= False
														__SCREAMING_SNAKE_CASE									= False

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

							# load state_dict of original model, rename some keys
							__SCREAMING_SNAKE_CASE									= torch.hub.load_state_dict_from_url(a__							,       map_location='cpu'							,       check_hash=a__       )['model']

							__SCREAMING_SNAKE_CASE									= 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():
														__SCREAMING_SNAKE_CASE									= state_dict.pop(a__       )
														if key.startswith('decoder'       ) and "output_projection" not in key:
																					__SCREAMING_SNAKE_CASE									= val
														else:
																					__SCREAMING_SNAKE_CASE									= val

    # load state dict
							model.load_state_dict(a__       )

							# Check outputs on an image
							__SCREAMING_SNAKE_CASE									= ViTImageProcessor(size=encoder_config.image_size       )
							__SCREAMING_SNAKE_CASE									= RobertaTokenizer.from_pretrained('roberta-large'       )
							__SCREAMING_SNAKE_CASE									= TrOCRProcessor(a__							,       a__       )

							__SCREAMING_SNAKE_CASE									= processor(images=prepare_img(a__       )							,       return_tensors='pt'       ).pixel_values

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

							__SCREAMING_SNAKE_CASE									= torch.Size([1, 1, 5_02_65]       )
							if "trocr-base-handwritten" in checkpoint_url:
														__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_SNAKE_CASE									= 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]							,       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__":
			lowerCAmelCase__		:					Tuple							=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.'''
			)
			lowerCAmelCase__		:					List[Any]							=parser.parse_args()
			convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 0
							for a in range(1							,       n // 3       ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


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






