




import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowerCAmelCase						:   Union[str, Any]		=   (7_2_0, 1_2_8_0)  # Height, Width
_lowerCAmelCase						:   Any		=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCAmelCase						:   List[str]		=   1 / 1_0_0
_lowerCAmelCase						:   Union[str, Any]		=   ""
_lowerCAmelCase						:   Any		=   ""
_lowerCAmelCase						:   List[str]		=   ""
_lowerCAmelCase						:   Union[str, Any]		=   2_5_0



def 						lowerCAmelCase		(		):




	"""simple docstring"""







	UpperCAmelCase__ , UpperCAmelCase__							 =						get_dataset(_lowerCAmelCase						,							_lowerCAmelCase	)
	for index in range(_lowerCAmelCase	):
		UpperCAmelCase__							 =						random.sample(range(len(_lowerCAmelCase	)	)						,							4	)
		UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						update_image_and_anno(
		    _lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							filter_scale=_lowerCAmelCase						,							)

		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		UpperCAmelCase__							 =						random_chars(32	)
		UpperCAmelCase__							 =						path.split(os.sep	)[-1].rsplit("."						,							1	)[0]
		UpperCAmelCase__							 =						F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
		cva.imwrite(F'''{file_root}.jpg'''						,							_lowerCAmelCase						,							[cva.IMWRITE_JPEG_QUALITY, 85]	)
		print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''	)
		UpperCAmelCase__							 =						[]
		for anno in new_annos:
			UpperCAmelCase__							 =						anno[3] - anno[1]
			UpperCAmelCase__							 =						anno[4] - anno[2]
			UpperCAmelCase__							 =						anno[1] + width / 2
			UpperCAmelCase__							 =						anno[2] + height / 2
			UpperCAmelCase__							 =						F'''{anno[0]} {x_center} {y_center} {width} {height}'''
			annos_list.append(_lowerCAmelCase	)
		with open(F'''{file_root}.txt'''						,							"w"	) as outfile:
			outfile.write("\n".join(line for line in annos_list	)	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




	"""simple docstring"""







	UpperCAmelCase__							 =						[]
	UpperCAmelCase__							 =						[]
	for label_file in glob.glob(os.path.join(_lowerCAmelCase						,							"*.txt"	)	):
		UpperCAmelCase__							 =						label_file.split(os.sep	)[-1].rsplit("."						,							1	)[0]
		with open(_lowerCAmelCase	) as in_file:
			UpperCAmelCase__							 =						in_file.readlines()
		UpperCAmelCase__							 =						os.path.join(_lowerCAmelCase						,							F'''{label_name}.jpg'''	)

		UpperCAmelCase__							 =						[]
		for obj_list in obj_lists:
			UpperCAmelCase__							 =						obj_list.rstrip("\n"	).split(" "	)
			UpperCAmelCase__							 =						float(obj[1]	) - float(obj[3]	) / 2
			UpperCAmelCase__							 =						float(obj[2]	) - float(obj[4]	) / 2
			UpperCAmelCase__							 =						float(obj[1]	) + float(obj[3]	) / 2
			UpperCAmelCase__							 =						float(obj[2]	) + float(obj[4]	) / 2

			boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
		if not boxes:
			continue
		img_paths.append(_lowerCAmelCase	)
		labels.append(_lowerCAmelCase	)
	return img_paths, labels



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list[int]						,							_lowerCAmelCase							:   tuple[int, int]						,							_lowerCAmelCase							:   tuple[float, float]						,							_lowerCAmelCase							:   float = 0.0						,							):




	"""simple docstring"""







	UpperCAmelCase__							 =						np.zeros([output_size[0], output_size[1], 3]						,							dtype=np.uinta	)
	UpperCAmelCase__							 =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	UpperCAmelCase__							 =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	UpperCAmelCase__							 =						int(scale_x * output_size[1]	)
	UpperCAmelCase__							 =						int(scale_y * output_size[0]	)

	UpperCAmelCase__							 =						[]
	UpperCAmelCase__							 =						[]
	for i, index in enumerate(_lowerCAmelCase	):
		UpperCAmelCase__							 =						all_img_list[index]
		path_list.append(_lowerCAmelCase	)
		UpperCAmelCase__							 =						all_annos[index]
		UpperCAmelCase__							 =						cva.imread(_lowerCAmelCase	)
		if i == 0:  # top-left
			UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(divid_point_x, divid_point_y)	)
			UpperCAmelCase__							 =						img
			for bbox in img_annos:
				UpperCAmelCase__							 =						bbox[1] * scale_x
				UpperCAmelCase__							 =						bbox[2] * scale_y
				UpperCAmelCase__							 =						bbox[3] * scale_x
				UpperCAmelCase__							 =						bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
		elif i == 1:  # top-right
			UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(output_size[1] - divid_point_x, divid_point_y)	)
			UpperCAmelCase__							 =						img
			for bbox in img_annos:
				UpperCAmelCase__							 =						scale_x + bbox[1] * (1 - scale_x)
				UpperCAmelCase__							 =						bbox[2] * scale_y
				UpperCAmelCase__							 =						scale_x + bbox[3] * (1 - scale_x)
				UpperCAmelCase__							 =						bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
		elif i == 2:  # bottom-left
			UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(divid_point_x, output_size[0] - divid_point_y)	)
			UpperCAmelCase__							 =						img
			for bbox in img_annos:
				UpperCAmelCase__							 =						bbox[1] * scale_x
				UpperCAmelCase__							 =						scale_y + bbox[2] * (1 - scale_y)
				UpperCAmelCase__							 =						bbox[3] * scale_x
				UpperCAmelCase__							 =						scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
		else:  # bottom-right
			UpperCAmelCase__							 =						cva.resize(
			    _lowerCAmelCase						,							(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
			UpperCAmelCase__							 =						img
			for bbox in img_annos:
				UpperCAmelCase__							 =						scale_x + bbox[1] * (1 - scale_x)
				UpperCAmelCase__							 =						scale_y + bbox[2] * (1 - scale_y)
				UpperCAmelCase__							 =						scale_x + bbox[3] * (1 - scale_x)
				UpperCAmelCase__							 =						scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

				# Remove bounding box small than scale of filter
	if filter_scale > 0:
		UpperCAmelCase__							 =						[
		    anno
		    for anno in new_anno
		    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
		]

	return output_img, new_anno, path_list[0]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	):




	"""simple docstring"""







	assert number_char > 1, "The number of character should greater than 1"
	UpperCAmelCase__							 =						ascii_lowercase + digits
	return "".join(random.choice(_lowerCAmelCase	) for _ in range(_lowerCAmelCase	)	)


if __name__ == "__main__":
		main()
		print("DONE ✅")











import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						BigBirdConfig.from_json_file(_lowerCAmelCase	)
 print(F'''Building PyTorch model from configuration: {config}'''	)

 if is_trivia_qa:
  UpperCAmelCase__							 =						BigBirdForQuestionAnswering(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						BigBirdForPreTraining(_lowerCAmelCase	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_lowerCAmelCase						,							_lowerCAmelCase						,							is_trivia_qa=_lowerCAmelCase	)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   List[Any]		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--big_bird_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained BERT model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
  )
  _lowerCAmelCase						:   Dict		=   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )





