



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a__      :				Optional[Any]   	=			logging.get_logger(__name__)

def   _lowerCAmelCase   (						A__						,  A__=False						,  A__=False	):
						lowercase__     =  'backbone.' if is_semantic else ''

						lowercase__     =  []
						for i in range(config.num_hidden_layers	):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')	)
												rename_keys.append(
												    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')	)
												rename_keys.append(
												    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')	)
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')	)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (F'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
						        (F'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
						        (F'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
						        (F'''{prefix}pos_embed''', 'beit.embeddings.position_embeddings'),
						    ]	)

						if has_lm_head:
												# mask token + layernorm
												rename_keys.extend(
												    [
												        ('mask_token', 'beit.embeddings.mask_token'),
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]	)
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
												        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]	)

						return rename_keys

def   _lowerCAmelCase   (						A__						,  A__						,  A__=False						,  A__=False	):
						for i in range(config.num_hidden_layers	):
												lowercase__     =  'backbone.' if is_semantic else ''
												# queries, keys and values
												lowercase__     =  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''	)
												lowercase__     =  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''	)
												lowercase__     =  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''	)

												lowercase__     =  in_proj_weight[
												    : config.hidden_size, :
												]
												lowercase__     =  q_bias
												lowercase__     =  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowercase__     =  in_proj_weight[
												    -config.hidden_size :, :
												]
												lowercase__     =  v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												lowercase__     =  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''	)
												lowercase__     =  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''	)

												lowercase__     =  gamma_a
												lowercase__     =  gamma_a

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
						lowercase__     =  dct.pop(A__	)
						lowercase__     =  val

def   _lowerCAmelCase   (						):
						lowercase__     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowercase__     =  Image.open(requests.get(A__						,  stream=A__	).raw	)
						return im

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__=False	):
						lowercase__     =  False if 'rvlcdip' in checkpoint_url else True
						lowercase__     =  BeitConfig(use_absolute_position_embeddings=A__						,  use_mask_token=A__	)

						# size of the architecture
						if "large" in checkpoint_url or "dit-l" in checkpoint_url:
												lowercase__     =  1_024
												lowercase__     =  4_096
												lowercase__     =  24
												lowercase__     =  16

						# labels
						if "rvlcdip" in checkpoint_url:
												lowercase__     =  16
												lowercase__     =  'huggingface/label-files'
												lowercase__     =  'rvlcdip-id2label.json'
												lowercase__     =  json.load(open(hf_hub_download(A__						,  A__						,  repo_type='dataset'	)						,  'r'	)	)
												lowercase__     =  {int(A__	): v for k, v in idalabel.items()}
												lowercase__     =  idalabel
												lowercase__     =  {v: k for k, v in idalabel.items()}

						# load state_dict of original model, remove and rename some keys
						lowercase__     =  torch.hub.load_state_dict_from_url(A__						,  map_location='cpu'	)['model']

						lowercase__     =  create_rename_keys(A__						,  has_lm_head=A__	)
						for src, dest in rename_keys:
												rename_key(A__						,  A__						,  A__	)
						read_in_q_k_v(A__						,  A__						,  has_lm_head=A__	)

						# load HuggingFace model
						lowercase__     =  BeitForMaskedImageModeling(A__	) if has_lm_head else BeitForImageClassification(A__	)
						model.eval()
						model.load_state_dict(A__	)

						# Check outputs on an image
						lowercase__     =  BeitImageProcessor(
						    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=A__	)
						lowercase__     =  prepare_img()

						lowercase__     =  image_processor(images=A__						,  return_tensors='pt'	)
						lowercase__     =  encoding['pixel_values']

						lowercase__     =  model(A__	)
						lowercase__     =  outputs.logits

						# verify logits
						lowercase__     =  [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
						assert logits.shape == torch.Size(A__	), "Shape 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 image processor to {pytorch_dump_folder_path}'''	)
						image_processor.save_pretrained(A__	)

						if push_to_hub:
												if has_lm_head:
																		lowercase__     =  'dit-base' if 'base' in checkpoint_url else 'dit-large'
												else:
																		lowercase__     =  'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
												image_processor.push_to_hub(
												    repo_path_or_name=Path(A__						,  A__	)						,  organization='nielsr'						,  commit_message='Add image processor'						,  use_temp_dir=A__						,  )
												model.push_to_hub(
												    repo_path_or_name=Path(A__						,  A__	)						,  organization='nielsr'						,  commit_message='Add model'						,  use_temp_dir=A__						,  )


if __name__ == "__main__":
							a__      :				List[Any]   	=			argparse.ArgumentParser()

							parser.add_argument(
							    "--checkpoint_url",
							    default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
							    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."
							)
							parser.add_argument(
							    "--push_to_hub",
							    action="store_true",
							)
							a__      :				str   	=			parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





import cva
import numpy as np



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			float			,				lowerCAmelCase  :			int)	->				Dict:




         """simple docstring"""

         if k in (0.04, 0.06):
               lowercase__     =  k
               lowercase__     =  window_size
         else:
               raise ValueError('invalid k value')


   def __str__(						self  :			Tuple)	->				str:




         """simple docstring"""

         return str(self.k)



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				tuple[cva.Mat, list[list[int]]]:




         """simple docstring"""

         lowercase__     =  cva.imread(lowerCAmelCase			,				0)
         lowercase__,	lowercase__     =  img.shape
         lowercase__     =  []
         lowercase__     =  img.copy()
         lowercase__     =  cva.cvtColor(lowerCAmelCase			,				cva.COLOR_GRAY2RGB)
         lowercase__,	lowercase__     =  np.gradient(lowerCAmelCase)
         lowercase__     =  dx**2
         lowercase__     =  dy**2
         lowercase__     =  dx * dy
         lowercase__     =  0.04
         lowercase__     =  self.window_size // 2
         for y in range(lowerCAmelCase			,				h - offset):
               for x in range(lowerCAmelCase			,				w - offset):
                     lowercase__     =  ixx[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  iyy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  ixy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()

                     lowercase__     =  (wxx * wyy) - (wxy**2)
                     lowercase__     =  wxx + wyy
                     lowercase__     =  det - k * (trace**2)
                     # Can change the value
                     if r > 0.5:
                           corner_list.append([x, y, r])
                           color_img.itemset((y, x, 0)			,				0)
                           color_img.itemset((y, x, 1)			,				0)
                           color_img.itemset((y, x, 2)			,				2_55)
         return color_img, corner_list


if __name__ == "__main__":
       a__      :				Dict   	=			HarrisCorner(0.0_4, 3)
       a__	,		a__      :				Dict   	=			edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)
