






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

import cva
import numpy as np

# Parrameters
UpperCAmelCase_ :						Optional[Any]      =      (7_20, 12_80)  # Height, Width
UpperCAmelCase_ :						List[str]      =      (0.4, 0.6)  # if height or width lower than this scale, drop it.
UpperCAmelCase_ :						int      =      1 / 1_00
UpperCAmelCase_ :						int      =      ''''''
UpperCAmelCase_ :						List[str]      =      ''''''
UpperCAmelCase_ :						Optional[Any]      =      ''''''
UpperCAmelCase_ :						str      =      2_50

def    SCREAMING_SNAKE_CASE_							(							)		->			None:






	"""simple docstring"""



	UpperCamelCase     ,      UpperCamelCase     :int          =	get_dataset(__magic_name__	,			__magic_name__					)
	for index in range(__magic_name__					):
		UpperCamelCase     :int          =	random.sample(range(len(__magic_name__					)					)	,			4					)
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Dict          =	update_image_and_anno(
		    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			filter_scale=__magic_name__	,			)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  str					)		->			tuple[list, list]:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	[]
	UpperCamelCase     :str          =	[]
	for label_file in glob.glob(os.path.join(__magic_name__	,			"""*.txt"""					)					):
		UpperCamelCase     :Optional[Any]          =	label_file.split(os.sep					)[-1].rsplit("""."""	,			1					)[0]
		with open(__magic_name__					) as in_file:
			UpperCamelCase     :Union[str, Any]          =	in_file.readlines()
		UpperCamelCase     :Union[str, Any]          =	os.path.join(__magic_name__	,			f"""{label_name}.jpg"""					)

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list	,			__magic_name__					:  list	,			__magic_name__					:  list[int]	,			__magic_name__					:  tuple[int, int]	,			__magic_name__					:  tuple[float, float]	,			__magic_name__					:  float = 0.0	,			)		->			tuple[list, list, str]:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	np.zeros([output_size[0], output_size[1], 3]	,			dtype=np.uinta					)
	UpperCamelCase     :Tuple          =	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	UpperCamelCase     :Optional[Any]          =	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	UpperCamelCase     :Dict          =	int(scale_x * output_size[1]					)
	UpperCamelCase     :List[Any]          =	int(scale_y * output_size[0]					)

	UpperCamelCase     :Any          =	[]
	UpperCamelCase     :Optional[int]          =	[]
	for i, index in enumerate(__magic_name__					):
		UpperCamelCase     :Optional[int]          =	all_img_list[index]
		path_list.append(__magic_name__					)
		UpperCamelCase     :str          =	all_annos[index]
		UpperCamelCase     :List[Any]          =	cva.imread(__magic_name__					)
		if i == 0:  # top-left
			UpperCamelCase     :Union[str, Any]          =	cva.resize(__magic_name__	,			(divid_point_x, divid_point_y)					)
			UpperCamelCase     :Tuple          =	img
			for bbox in img_annos:
				UpperCamelCase     :str          =	bbox[1] * scale_x
				UpperCamelCase     :List[str]          =	bbox[2] * scale_y
				UpperCamelCase     :Dict          =	bbox[3] * scale_x
				UpperCamelCase     :List[Any]          =	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
		elif i == 1:  # top-right
			UpperCamelCase     :int          =	cva.resize(__magic_name__	,			(output_size[1] - divid_point_x, divid_point_y)					)
			UpperCamelCase     :Tuple          =	img
			for bbox in img_annos:
				UpperCamelCase     :List[str]          =	scale_x + bbox[1] * (1 - scale_x)
				UpperCamelCase     :str          =	bbox[2] * scale_y
				UpperCamelCase     :Tuple          =	scale_x + bbox[3] * (1 - scale_x)
				UpperCamelCase     :Tuple          =	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
		elif i == 2:  # bottom-left
			UpperCamelCase     :List[str]          =	cva.resize(__magic_name__	,			(divid_point_x, output_size[0] - divid_point_y)					)
			UpperCamelCase     :Optional[Any]          =	img
			for bbox in img_annos:
				UpperCamelCase     :Tuple          =	bbox[1] * scale_x
				UpperCamelCase     :Dict          =	scale_y + bbox[2] * (1 - scale_y)
				UpperCamelCase     :List[Any]          =	bbox[3] * scale_x
				UpperCamelCase     :int          =	scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
		else:  # bottom-right
			UpperCamelCase     :List[str]          =	cva.resize(
			    __magic_name__	,			(output_size[1] - divid_point_x, output_size[0] - divid_point_y)					)
			UpperCamelCase     :Any          =	img
			for bbox in img_annos:
				UpperCamelCase     :Any          =	scale_x + bbox[1] * (1 - scale_x)
				UpperCamelCase     :List[Any]          =	scale_y + bbox[2] * (1 - scale_y)
				UpperCamelCase     :List[Any]          =	scale_x + bbox[3] * (1 - scale_x)
				UpperCamelCase     :List[str]          =	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     :List[str]          =	[
		    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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			str:






	"""simple docstring"""



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


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


"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      :    str    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Optional[int]    							= {
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						int					       =   '''yolos'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=[5_12, 8_64]   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=1_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=0.1   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Optional[Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       int           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       str           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       List[str]           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       Dict           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       List[str]           =   image_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   patch_size
											SCREAMING_SNAKE_CASE__						:       List[str]           =   num_channels
											SCREAMING_SNAKE_CASE__						:       List[str]           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   num_detection_tokens
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   use_mid_position_embeddings
											SCREAMING_SNAKE_CASE__						:       List[str]           =   auxiliary_loss
											# Hungarian matcher
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   class_cost
											SCREAMING_SNAKE_CASE__						:       List[str]           =   bbox_cost
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   bbox_loss_coefficient
											SCREAMING_SNAKE_CASE__						:       List[str]           =   giou_loss_coefficient
											SCREAMING_SNAKE_CASE__						:       int           =   eos_coefficient






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Dict					       =   version.parse('''1.11'''  )



				@property
				def 	__magic_name__    (self    )					->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]    )



				@property
				def 	__magic_name__    (self    )					->		float:



											"""simple docstring"""




											return 1E-4



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return 12




