



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()
SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
		UpperCAmelCase_						=				dct.pop(snake_case__ )
		UpperCAmelCase_						=				val







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		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(snake_case__			,     stream=snake_case__ ).raw ).convert("RGB" )
		return im







@torch.no_grad()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		UpperCAmelCase_						=				ViTConfig(image_size=384			,     qkv_bias=snake_case__ )
		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(snake_case__			,     add_pooling_layer=snake_case__ )
		UpperCAmelCase_						=				TrOCRForCausalLM(snake_case__ )
		UpperCAmelCase_						=				VisionEncoderDecoderModel(encoder=snake_case__			,     decoder=snake_case__ )
		model.eval()

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

		UpperCAmelCase_						=				create_rename_keys(snake_case__			,     snake_case__ )
		for src, dest in rename_keys:
				rename_key(snake_case__			,     snake_case__			,     snake_case__ )
		read_in_q_k_v(snake_case__			,     snake_case__ )

		# 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(snake_case__ )
				if key.startswith("decoder" ) and "output_projection" not in key:
						UpperCAmelCase_						=				val
				else:
						UpperCAmelCase_						=				val

    # load state dict
		model.load_state_dict(snake_case__ )

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

		UpperCAmelCase_						=				processor(images=prepare_img(snake_case__ )			,     return_tensors="pt" ).pixel_values

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

		UpperCAmelCase_						=				torch.Size([1, 1, 5_0265] )
		if "trocr-base-handwritten" in checkpoint_url:
				UpperCAmelCase_						=				torch.tensor(
				    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
		elif "trocr-large-handwritten" in checkpoint_url:
				UpperCAmelCase_						=				torch.tensor(
				    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
		elif "trocr-base-printed" in checkpoint_url:
				UpperCAmelCase_						=				torch.tensor(
				    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
		elif "trocr-large-printed" in checkpoint_url:
				UpperCAmelCase_						=				torch.tensor(
				    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )

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

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


if __name__ == "__main__":
			SCREAMING_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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				int(__SCREAMING_SNAKE_CASE )
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_					,						UpperCAmelCase_						=				divmod(__SCREAMING_SNAKE_CASE			,     2 )
		return binary_recursive(__SCREAMING_SNAKE_CASE ) + str(__SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				str(__SCREAMING_SNAKE_CASE ).strip()
		if not number:
				raise ValueError("No input value was provided" )
		UpperCAmelCase_						=				"-" if number.startswith("-" ) else ""
		UpperCAmelCase_						=				number.lstrip("-" )
		if not number.isnumeric():
				raise ValueError("Input value is not an integer" )
		return f'''{negative}0b{binary_recursive(int(__SCREAMING_SNAKE_CASE ) )}'''


if __name__ == "__main__":
			from doctest import testmod

			testmod()

