





"""simple docstring"""





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =k_size // 2
							__lowerCamelCase    ,						__lowerCamelCase      :      int         =mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							__lowerCamelCase      :      Union[str, Any]         =1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE   ) + square(SCREAMING_SNAKE_CASE   )) / (2 * square(SCREAMING_SNAKE_CASE   ))   )
							return g



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =image.shape[0], image.shape[1]
							# dst image height and width
							__lowerCamelCase      :      Tuple         =height - k_size + 1
							__lowerCamelCase      :      Any         =width - k_size + 1

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							__lowerCamelCase      :      Dict         =zeros((dst_height * dst_width, k_size * k_size)   )
							__lowerCamelCase      :      Tuple         =0
							for i, j in product(range(SCREAMING_SNAKE_CASE   )    ,						range(SCREAMING_SNAKE_CASE   )   ):
														__lowerCamelCase      :      Any         =ravel(image[i : i + k_size, j : j + k_size]   )
														__lowerCamelCase      :      Union[str, Any]         =window
														row += 1

							#  turn the kernel into shape(k*k, 1)
							__lowerCamelCase      :      Dict         =gen_gaussian_kernel(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Tuple         =ravel(SCREAMING_SNAKE_CASE   )

							# reshape and get the dst image
							__lowerCamelCase      :      Any         =dot(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ).reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ).astype(SCREAMING_SNAKE_CASE   )

							return dst


if __name__ == "__main__":
							# read original image
							_UpperCamelCase							=      imread(r'../image_data/lena.jpg')
							# turn image in gray scale value
							_UpperCamelCase							=      cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							_UpperCamelCase							=      gaussian_filter(gray, 3, sigma=1)
							_UpperCamelCase							=      gaussian_filter(gray, 5, sigma=0.8)

							# show result images
							imshow('gaussian filter with 3x3 mask', gaussianaxa)
							imshow('gaussian filter with 5x5 mask', gaussianaxa)
							waitKey()











"""simple docstring"""





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()
_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							for i in range(encoder_config.num_hidden_layers   ):
														# queries, keys and values (only weights, no biases)
														__lowerCamelCase      :      str         =state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight'   )

														__lowerCamelCase      :      Dict         =in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														__lowerCamelCase      :      Any         =in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														__lowerCamelCase      :      Dict         =in_proj_weight[
														    -encoder_config.hidden_size :, :
														]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =dct.pop(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =val



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							if "handwritten" in checkpoint_url:
														__lowerCamelCase      :      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:
														__lowerCamelCase      :      List[Any]         ='''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
							__lowerCamelCase      :      int         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   ).convert('''RGB'''   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =ViTConfig(image_size=384    ,						qkv_bias=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =TrOCRConfig()

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

							# load HuggingFace model
							__lowerCamelCase      :      Dict         =ViTModel(SCREAMING_SNAKE_CASE    ,						add_pooling_layer=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Any         =TrOCRForCausalLM(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      List[Any]         =VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE    ,						decoder=SCREAMING_SNAKE_CASE   )
							model.eval()

							# load state_dict of original model, rename some keys
							__lowerCamelCase      :      Optional[Any]         =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,						map_location='''cpu'''    ,						check_hash=SCREAMING_SNAKE_CASE   )['''model''']

							__lowerCamelCase      :      Optional[int]         =create_rename_keys(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							read_in_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_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():
														__lowerCamelCase      :      List[Any]         =state_dict.pop(SCREAMING_SNAKE_CASE   )
														if key.startswith('''decoder'''   ) and "output_projection" not in key:
																					__lowerCamelCase      :      List[Any]         =val
														else:
																					__lowerCamelCase      :      Optional[Any]         =val

    # load state dict
							model.load_state_dict(SCREAMING_SNAKE_CASE   )

							# Check outputs on an image
							__lowerCamelCase      :      int         =ViTImageProcessor(size=encoder_config.image_size   )
							__lowerCamelCase      :      Dict         =RobertaTokenizer.from_pretrained('''roberta-large'''   )
							__lowerCamelCase      :      str         =TrOCRProcessor(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      str         =processor(images=prepare_img(SCREAMING_SNAKE_CASE   )    ,						return_tensors='''pt'''   ).pixel_values

							# verify logits
							__lowerCamelCase      :      List[Any]         =torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
							__lowerCamelCase      :      Optional[int]         =model(pixel_values=SCREAMING_SNAKE_CASE    ,						decoder_input_ids=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =outputs.logits

							__lowerCamelCase      :      Optional[Any]         =torch.Size([1, 1, 50265]   )
							if "trocr-base-handwritten" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =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:
														__lowerCamelCase      :      Any         =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:
														__lowerCamelCase      :      Optional[Any]         =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:
														__lowerCamelCase      :      Optional[Any]         =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]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-3   ), "First elements of logits not as expected"

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


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




