





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def __init__(      self      :      Dict		,   _A      :      List[str]		,   _A      :      Optional[Any]=7		,   _A      :      Optional[int]=3		,   _A      :      Any=18		,   _A      :      Optional[Any]=30		,   _A      :      Optional[Any]=400		,   _A      :      Union[str, Any]=True		,   _A      :      Optional[Any]=None		,   _A      :      str=True		,   _A      :      Tuple=None		,   _A      :      Optional[Any]=True		,   _A      :      str=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]		,   _A      :      int=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]		,   _A      :      int=True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			Any											=	size if size is not None else {'height': 224, 'width': 224}
							snake_case_		:			Optional[int]											=	crop_size if crop_size is not None else {'height': 18, 'width': 18}
							snake_case_		:			List[str]											=	parent
							snake_case_		:			Optional[Any]											=	batch_size
							snake_case_		:			Union[str, Any]											=	num_channels
							snake_case_		:			List[Any]											=	image_size
							snake_case_		:			Tuple											=	min_resolution
							snake_case_		:			Optional[Any]											=	max_resolution
							snake_case_		:			Any											=	do_resize
							snake_case_		:			List[Any]											=	size
							snake_case_		:			Tuple											=	do_center_crop
							snake_case_		:			Union[str, Any]											=	crop_size
							snake_case_		:			Optional[Any]											=	do_normalize
							snake_case_		:			List[Any]											=	image_mean
							snake_case_		:			List[Any]											=	image_std
							snake_case_		:			Tuple											=	do_convert_rgb






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=False		,   _A      :      Dict=False		,   _A      :      List[Any]=False	)							->    Optional[Any]:







							"""simple docstring"""


							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
								snake_case_		:			Dict											=	[]
								for i in range(self.batch_size	):
									image_inputs.append(
									    np.random.randint(
									        255		,   size=(self.num_channels, self.max_resolution, self.max_resolution)		,   dtype=np.uinta	)	)
							else:
								snake_case_		:			List[str]											=	[]
								for i in range(self.batch_size	):
									snake_case_						,snake_case_		:			str											=	np.random.choice(np.arange(self.min_resolution		,   self.max_resolution	)		,   2	)
									image_inputs.append(np.random.randint(255		,   size=(self.num_channels, width, height)		,   dtype=np.uinta	)	)

							if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								snake_case_		:			Dict											=	[Image.fromarray(np.moveaxis(__UpperCamelCase		,   0		,   -1	)	) for x in image_inputs]

							if torchify:
								snake_case_		:			Optional[Any]											=	[torch.from_numpy(__UpperCamelCase	) for x in image_inputs]

							return image_inputs
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			List[Any]       =       ChineseCLIPImageProcessor if is_vision_available() else None






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	ChineseCLIPImageProcessingTester(self		,   do_center_crop=__UpperCamelCase	)






						@property
						def UpperCAmelCase_				(      self      :      int	)							->    Dict:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_resize'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'size'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_center_crop'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'center_crop'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_normalize'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'image_mean'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'image_std'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_convert_rgb'	)	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size		,   {'height': 224, 'width': 224}	)
							self.assertEqual(image_processor.crop_size		,   {'height': 18, 'width': 18}	)

							snake_case_		:			Any											=	self.image_processing_class.from_dict(self.image_processor_dict		,   size=42		,   crop_size=84	)
							self.assertEqual(image_processor.size		,   {'shortest_edge': 42}	)
							self.assertEqual(image_processor.crop_size		,   {'height': 84, 'width': 84}	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Tuple:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							snake_case_		:			Any											=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase		,   Image.Image	)

							# Test not batched input
							snake_case_		:			int											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )

							# Test batched
							snake_case_		:			Union[str, Any]											=	image_processing(__UpperCamelCase		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )






						def UpperCAmelCase_				(      self      :      str	)							->    str:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							snake_case_		:			Dict											=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase		,   numpify=__UpperCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase		,   np.ndarray	)

							# Test not batched input
							snake_case_		:			List[Any]											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )

							# Test batched
							snake_case_		:			Tuple											=	image_processing(__UpperCamelCase		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							snake_case_		:			Tuple											=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase		,   torchify=__UpperCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase		,   torch.Tensor	)

							# Test not batched input
							snake_case_		:			Optional[int]											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )

							# Test batched
							snake_case_		:			Any											=	image_processing(__UpperCamelCase		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			int       =       ChineseCLIPImageProcessor if is_vision_available() else None






						def UpperCAmelCase_				(      self      :      List[str]	)							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	ChineseCLIPImageProcessingTester(self		,   num_channels=4		,   do_center_crop=__UpperCamelCase	)
							snake_case_		:			Dict											=	3






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_				(      self      :      Dict	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_resize'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'size'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_center_crop'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'center_crop'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_normalize'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'image_mean'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'image_std'	)	)
							self.assertTrue(hasattr(__UpperCamelCase		,   'do_convert_rgb'	)	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    int:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Any											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							snake_case_		:			List[Any]											=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase	)
							for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase		,   Image.Image	)

							# Test not batched input
							snake_case_		:			Optional[Any]											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )

							# Test batched
							snake_case_		:			str											=	image_processing(__UpperCamelCase		,   return_tensors='pt'	).pixel_values
							self.assertEqual(
							    encoded_images.shape		,   (
							        self.image_processor_tester.batch_size,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )











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

import cva
import numpy as np

# Parrameters
_SCREAMING_SNAKE_CASE      	=		(7_20, 12_80)  # Height, Width
_SCREAMING_SNAKE_CASE      	=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE      	=		1 / 1_00
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		""""""
_SCREAMING_SNAKE_CASE      	=		2_50


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_						,snake_case_		:			Any											=	get_dataset(__a   , __a   )
	for index in range(__a   ):
		snake_case_		:			Dict											=	random.sample(range(len(__a   )   )   , 4   )
		snake_case_						,snake_case_						,snake_case_		:			Optional[int]											=	update_image_and_anno(
		    __a   , __a   , __a   , __a   , __a   , filter_scale=__a   , )

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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			List[Any]											=	[]
	snake_case_		:			Optional[int]											=	[]
	for label_file in glob.glob(os.path.join(__a   , '*.txt'   )   ):
		snake_case_		:			List[str]											=	label_file.split(os.sep   )[-1].rsplit('.'   , 1   )[0]
		with open(__a   ) as in_file:
			snake_case_		:			Dict											=	in_file.readlines()
		snake_case_		:			int											=	os.path.join(__a   , f"""{label_name}.jpg"""   )

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

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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a = 0.0   , ):
	snake_case_		:			str											=	np.zeros([output_size[0], output_size[1], 3]   , dtype=np.uinta   )
	snake_case_		:			Tuple											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			int											=	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case_		:			Optional[Any]											=	int(scale_x * output_size[1]   )
	snake_case_		:			int											=	int(scale_y * output_size[0]   )

	snake_case_		:			str											=	[]
	snake_case_		:			Dict											=	[]
	for i, index in enumerate(__a   ):
		snake_case_		:			Any											=	all_img_list[index]
		path_list.append(__a   )
		snake_case_		:			List[str]											=	all_annos[index]
		snake_case_		:			Any											=	cva.imread(__a   )
		if i == 0:  # top-left
			snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, divid_point_y)   )
			snake_case_		:			Dict											=	img
			for bbox in img_annos:
				snake_case_		:			Dict											=	bbox[1] * scale_x
				snake_case_		:			Tuple											=	bbox[2] * scale_y
				snake_case_		:			Optional[Any]											=	bbox[3] * scale_x
				snake_case_		:			str											=	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		elif i == 1:  # top-right
			snake_case_		:			Tuple											=	cva.resize(__a   , (output_size[1] - divid_point_x, divid_point_y)   )
			snake_case_		:			Optional[Any]											=	img
			for bbox in img_annos:
				snake_case_		:			int											=	scale_x + bbox[1] * (1 - scale_x)
				snake_case_		:			Dict											=	bbox[2] * scale_y
				snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
				snake_case_		:			Union[str, Any]											=	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		elif i == 2:  # bottom-left
			snake_case_		:			Optional[Any]											=	cva.resize(__a   , (divid_point_x, output_size[0] - divid_point_y)   )
			snake_case_		:			int											=	img
			for bbox in img_annos:
				snake_case_		:			int											=	bbox[1] * scale_x
				snake_case_		:			List[str]											=	scale_y + bbox[2] * (1 - scale_y)
				snake_case_		:			List[str]											=	bbox[3] * scale_x
				snake_case_		:			Union[str, Any]											=	scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
		else:  # bottom-right
			snake_case_		:			str											=	cva.resize(
			    __a   , (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
			snake_case_		:			Dict											=	img
			for bbox in img_annos:
				snake_case_		:			Dict											=	scale_x + bbox[1] * (1 - scale_x)
				snake_case_		:			Tuple											=	scale_y + bbox[2] * (1 - scale_y)
				snake_case_		:			Union[str, Any]											=	scale_x + bbox[3] * (1 - scale_x)
				snake_case_		:			List[Any]											=	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:
		snake_case_		:			Dict											=	[
		    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__						(    __a   ):
	assert number_char > 1, "The number of character should greater than 1"
	snake_case_		:			Tuple											=	ascii_lowercase + digits
	return "".join(random.choice(__a   ) for _ in range(__a   )   )


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

