





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				:    List[str]       =   logging.get_logger(__name__)





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				Optional[int]:







			'''simple docstring'''

			UpperCAmelCase   		=				dct.pop(UpperCamelCase__ )
			UpperCAmelCase   		=				val





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[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(UpperCamelCase__			,			stream=UpperCamelCase__ ).raw ).convert('''RGB''' )
			return im





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				ViTConfig(image_size=384			,			qkv_bias=UpperCamelCase__ )
			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(UpperCamelCase__			,			add_pooling_layer=UpperCamelCase__ )
			UpperCAmelCase   		=				TrOCRForCausalLM(UpperCamelCase__ )
			UpperCAmelCase   		=				VisionEncoderDecoderModel(encoder=UpperCamelCase__			,			decoder=UpperCamelCase__ )
			model.eval()

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

			UpperCAmelCase   		=				create_rename_keys(UpperCamelCase__			,			UpperCamelCase__ )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )
			read_in_q_k_v(UpperCamelCase__			,			UpperCamelCase__ )

			# 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(UpperCamelCase__ )
						if key.startswith('''decoder''' ) and "output_projection" not in key:
									UpperCAmelCase   		=				val
						else:
									UpperCAmelCase   		=				val

    # load state dict
			model.load_state_dict(UpperCamelCase__ )

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

			UpperCAmelCase   		=				processor(images=prepare_img(UpperCamelCase__ )			,			return_tensors='''pt''' ).pixel_values

			# verify logits
			UpperCAmelCase   		=				torch.tensor([[model.config.decoder.decoder_start_token_id]] )
			UpperCAmelCase   		=				model(pixel_values=UpperCamelCase__			,			decoder_input_ids=UpperCamelCase__ )
			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]			,			UpperCamelCase__			,			atol=1E-3 ), "First elements of logits not as expected"

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


if __name__ == "__main__":
				__A				:    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."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			exponent // 2			,			UpperCamelCase__ ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase__			,			exponent - 1			,			UpperCamelCase__ )) % modulo_value





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ = 1777			,			UpperCamelCase__ = 1855			,			UpperCamelCase__ = 8 )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				base
			for _ in range(1			,			UpperCamelCase__ ):
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			UpperCamelCase__			,			10**digits )

			return result


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



