

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

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Optional[int] )   ->       Any:
				_UpperCAmelCase       :		int 		=    dct.pop(lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    val

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
				if "handwritten" in checkpoint_url:
								_UpperCAmelCase       :		List[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:
								_UpperCAmelCase       :		Optional[int] 		=    "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       :		Tuple 		=    Image.open(requests.get(lowerCAmelCase  ,      stream=lowerCAmelCase ).raw ).convert("RGB" )
				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Union[str, Any] )   ->       Tuple:
				_UpperCAmelCase       :		Dict 		=    ViTConfig(image_size=384  ,      qkv_bias=lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    TrOCRConfig()

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

				# load HuggingFace model
				_UpperCAmelCase       :		Optional[int] 		=    ViTModel(lowerCAmelCase  ,      add_pooling_layer=lowerCAmelCase )
				_UpperCAmelCase       :		List[Any] 		=    TrOCRForCausalLM(lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    VisionEncoderDecoderModel(encoder=lowerCAmelCase  ,      decoder=lowerCAmelCase )
				model.eval()

				# load state_dict of original model, rename some keys
				_UpperCAmelCase       :		Optional[int] 		=    torch.hub.load_state_dict_from_url(lowerCAmelCase  ,      map_location="cpu"  ,      check_hash=lowerCAmelCase )["model"]

				_UpperCAmelCase       :		List[str] 		=    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       :		List[Any] 		=    state_dict.pop(lowerCAmelCase )
								if key.startswith("decoder" ) and "output_projection" not in key:
												_UpperCAmelCase       :		int 		=    val
								else:
												_UpperCAmelCase       :		int 		=    val

    # load state dict
				model.load_state_dict(lowerCAmelCase )

				# Check outputs on an image
				_UpperCAmelCase       :		Any 		=    ViTImageProcessor(size=encoder_config.image_size )
				_UpperCAmelCase       :		Union[str, Any] 		=    RobertaTokenizer.from_pretrained("roberta-large" )
				_UpperCAmelCase       :		List[str] 		=    TrOCRProcessor(lowerCAmelCase  ,      lowerCAmelCase )

				_UpperCAmelCase       :		int 		=    processor(images=prepare_img(lowerCAmelCase )  ,      return_tensors="pt" ).pixel_values

				# verify logits
				_UpperCAmelCase       :		Any 		=    torch.tensor([[model.config.decoder.decoder_start_token_id]] )
				_UpperCAmelCase       :		Tuple 		=    model(pixel_values=lowerCAmelCase  ,      decoder_input_ids=lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    outputs.logits

				_UpperCAmelCase       :		str 		=    torch.Size([1, 1, 5_0265] )
				if "trocr-base-handwritten" in checkpoint_url:
								_UpperCAmelCase       :		Optional[int] 		=    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       :		Optional[int] 		=    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       :		Tuple 		=    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       :		str 		=    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]  ,      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__":
					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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       int:
				if len(lowerCAmelCase ) != len(lowerCAmelCase ):
								raise ValueError("String lengths must match!" )

				_UpperCAmelCase       :		List[Any] 		=    0

				for chara, chara in zip(lowerCAmelCase  ,      lowerCAmelCase ):
								if chara != chara:
												count += 1

				return count


if __name__ == "__main__":
					import doctest

					doctest.testmod()


