
'''simple docstring'''






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

import cva
import numpy as np

# Parrameters
A			      =      (720, 1280)  # Height, Width
A			      =      (0.4, 0.6)  # if height or width lower than this scale, drop it.
A			      =      1 / 100
A			      =      ''
A			      =      ''
A			      =      ''
A			      =      250





def    UpperCAmelCase							(  ):
				lowerCamelCase			, lowerCamelCase					:			str									= get_dataset(a__       ,							a__)
				for index in range(a__):
								lowerCamelCase					:			Union[str, Any]									= random.sample(range(len(a__))       ,							4)
								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			List[Any]									= update_image_and_anno(
								    a__       ,							a__       ,							a__       ,							a__       ,							a__       ,							filter_scale=a__       ,							)

								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								lowerCamelCase					:			List[str]									= random_chars(32)
								lowerCamelCase					:			List[Any]									= path.split(os.sep)[-1].rsplit('.'       ,							1)[0]
								lowerCamelCase					:			Optional[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}''')
								lowerCamelCase					:			Optional[Any]									= []
								for anno in new_annos:
												lowerCamelCase					:			Union[str, Any]									= anno[3] - anno[1]
												lowerCamelCase					:			int									= anno[4] - anno[2]
												lowerCamelCase					:			Tuple									= anno[1] + width / 2
												lowerCamelCase					:			Optional[Any]									= anno[2] + height / 2
												lowerCamelCase					:			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    UpperCAmelCase							(  UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Tuple):
				lowerCamelCase					:			Optional[int]									= []
				lowerCamelCase					:			Any									= []
				for label_file in glob.glob(os.path.join(a__       ,							'*.txt')):
								lowerCamelCase					:			List[str]									= label_file.split(os.sep)[-1].rsplit('.'       ,							1)[0]
								with open(a__) as in_file:
												lowerCamelCase					:			List[Any]									= in_file.readlines()
								lowerCamelCase					:			Tuple									= os.path.join(a__       ,							F'''{label_name}.jpg''')

								lowerCamelCase					:			Dict									= []
								for obj_list in obj_lists:
												lowerCamelCase					:			Optional[Any]									= obj_list.rstrip('\n').split(' ')
												lowerCamelCase					:			str									= float(obj[1]) - float(obj[3]) / 2
												lowerCamelCase					:			Union[str, Any]									= float(obj[2]) - float(obj[4]) / 2
												lowerCamelCase					:			Union[str, Any]									= float(obj[1]) + float(obj[3]) / 2
												lowerCamelCase					:			Tuple									= 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    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Any = 0.0       ,							):
				lowerCamelCase					:			List[Any]									= np.zeros([output_size[0], output_size[1], 3]       ,							dtype=np.uinta)
				lowerCamelCase					:			Union[str, Any]									= scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				lowerCamelCase					:			str									= scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				lowerCamelCase					:			List[str]									= int(scale_x * output_size[1])
				lowerCamelCase					:			Tuple									= int(scale_y * output_size[0])

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


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


'''simple docstring'''






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort




class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
					def     UpperCAmelCase_							(     self,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= floats_tensor((1, 3, 128, 128),    rng=random.Random(A      )      )
									lowerCamelCase					:			Tuple									= np.random.RandomState(A      )
									lowerCamelCase					:			str									= {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 3,
									    'strength': 0.75,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= self.get_dummy_inputs()
									lowerCamelCase					:			List[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Union[str, Any]									= image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Union[str, Any]									= np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]      )
									assert np.abs(image_slice - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Any									= PNDMScheduler.from_config(pipe.scheduler.config,    skip_prk_steps=A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Dict									= self.get_dummy_inputs()
									lowerCamelCase					:			Dict									= pipe(**A      ).images
									lowerCamelCase					:			Optional[Any]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Union[str, Any]									= np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Optional[int]									= LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									# warmup pass to apply optimizations
									lowerCamelCase					:			Union[str, Any]									= pipe(**self.get_dummy_inputs()      )

									lowerCamelCase					:			Any									= self.get_dummy_inputs()
									lowerCamelCase					:			Optional[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Dict									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Dict									= np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			int									= EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			str									= self.get_dummy_inputs()
									lowerCamelCase					:			List[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Tuple									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			List[str]									= np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Union[str, Any]									= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= self.get_dummy_inputs()
									lowerCamelCase					:			List[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Optional[Any]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Optional[int]									= np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			str									= self.get_dummy_inputs()
									lowerCamelCase					:			Tuple									= pipe(**A      ).images
									lowerCamelCase					:			Union[str, Any]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			List[Any]									= np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1





@nightly
@require_onnxruntime
@require_torch_gpu
class     __snake_case		(   unittest.TestCase):
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= ort.SessionOptions()
									lowerCamelCase					:			Union[str, Any]									= False
									return options
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/img2img/sketch-mountains-input.jpg'      )
									lowerCamelCase					:			int									= init_image.resize((768, 512)      )
									# using the PNDM scheduler by default
									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    'CompVis/stable-diffusion-v1-4',    revision='onnx',    safety_checker=A,    feature_extractor=A,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Optional[Any]									= 'A fantasy landscape, trending on artstation'

									lowerCamelCase					:			Dict									= np.random.RandomState(0      )
									lowerCamelCase					:			Any									= pipe(
									    prompt=A,    image=A,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=10,    generator=A,    output_type='np',    )
									lowerCamelCase					:			Union[str, Any]									= output.images
									lowerCamelCase					:			Union[str, Any]									= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowerCamelCase					:			List[Any]									= np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]      )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/img2img/sketch-mountains-input.jpg'      )
									lowerCamelCase					:			Any									= init_image.resize((768, 512)      )
									lowerCamelCase					:			List[Any]									= LMSDiscreteScheduler.from_pretrained(
									    'runwayml/stable-diffusion-v1-5',    subfolder='scheduler',    revision='onnx'      )
									lowerCamelCase					:			Dict									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5',    revision='onnx',    scheduler=A,    safety_checker=A,    feature_extractor=A,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= 'A fantasy landscape, trending on artstation'

									lowerCamelCase					:			List[Any]									= np.random.RandomState(0      )
									lowerCamelCase					:			Tuple									= pipe(
									    prompt=A,    image=A,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=20,    generator=A,    output_type='np',    )
									lowerCamelCase					:			List[Any]									= output.images
									lowerCamelCase					:			Union[str, Any]									= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowerCamelCase					:			int									= np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]      )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2e-2
