
'''simple docstring'''







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Union[str, Any]													=       flatten_dict(__SCREAMING_SNAKE_CASE							)
						return flax_params
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       {}

						lowercase_	:     List[Any]													=       {
						    '''token_embedder''': '''embeddings''',
						    '''encoder_norm''': '''layernorm''',
						    '''kernel''': '''weight''',
						    '''.out''': '''.output''',
						    '''scale''': '''weight''',
						    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
						    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
						}

						lowercase_	:     str													=       {
						    '''query''': '''attention.query''',
						    '''key''': '''attention.key''',
						    '''value''': '''attention.value''',
						    '''output.dense''': '''output''',
						    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
						    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
						    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
						    '''mlp.''': '''mlp.DenseReluDense.''',
						    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
						    '''self_attention.o''': '''self_attention.attention.o''',
						    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
						    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
						    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
						    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase_	:     Dict													=       '''.'''.join(key[1:]							)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase_	:     List[str]													=       new_key.replace(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase_	:     int													=       new_key.replace(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase_	:     List[str]													=       re.sub(R'''layers_(\d+)'''      ,      R'''layer.\1'''      ,      __SCREAMING_SNAKE_CASE							)
																								lowercase_	:     List[str]													=       new_key.replace('''encoder'''      ,      '''encoder.encoder'''							)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase_	:     Any													=       re.sub(R'''layers_(\d+)'''      ,      R'''layer.\1'''      ,      __SCREAMING_SNAKE_CASE							)

																		lowercase_	:     str													=       flax_dict[key]

						lowercase_	:     List[str]													=       {}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase_	:     Tuple													=       torch.from_numpy(converted_dict[key].T							)
												else:
																		lowercase_	:     Dict													=       torch.from_numpy(converted_dict[key]							)

						return converted_torch_dict
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=False      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]=False							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       get_flax_param(__SCREAMING_SNAKE_CASE							)

						if not use_large:
												lowercase_	:     Any													=       PixaStructVisionConfig()
												lowercase_	:     Optional[Any]													=       PixaStructTextConfig()
						else:
												lowercase_	:     Tuple													=       PixaStructVisionConfig(
												    hidden_size=1536      ,      d_ff=3968      ,      num_attention_heads=24      ,      num_hidden_layers=18							)
												lowercase_	:     int													=       PixaStructTextConfig(hidden_size=1536      ,      d_ff=3968      ,      num_heads=24      ,      num_layers=18							)
						lowercase_	:     Any													=       PixaStructConfig(
						    vision_config=encoder_config.to_dict()      ,      text_config=decoder_config.to_dict()      ,      is_vqa=__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Any													=       PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Optional[Any]													=       rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
						lowercase_	:     List[str]													=       PixaStructImageProcessor()
						lowercase_	:     Tuple													=       PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE      ,      tokenizer=__SCREAMING_SNAKE_CASE							)

						if use_large:
												lowercase_	:     str													=       4096

						lowercase_	:     Dict													=       True

						# mkdir if needed
						os.makedirs(__SCREAMING_SNAKE_CASE      ,      exist_ok=__SCREAMING_SNAKE_CASE							)

						model.save_pretrained(__SCREAMING_SNAKE_CASE							)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							_lowercase :  Tuple    							=       argparse.ArgumentParser()
							parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--use_large", action="store_true", help="Use large model.")
							parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
							_lowercase :  Tuple    							=       parser.parse_args()

							convert_pixastruct_original_pytorch_checkpoint_to_hf(
							    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
							)










import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				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_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



