





'''simple docstring'''







import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device





def      _lowerCAmelCase		(		lowercase			)      ->      int:
			__lowerCAmelCase      		=       {}
			for name in unet.attn_processors.keys():
						__lowerCAmelCase      		=       None if name.endswith("""attn1.processor"""			) else unet.config.cross_attention_dim
						if name.startswith("""mid_block"""			):
									__lowerCAmelCase      		=       unet.config.block_out_channels[-1]
						elif name.startswith("""up_blocks"""			):
									__lowerCAmelCase      		=       int(name[len("""up_blocks."""			)]			)
									__lowerCAmelCase      		=       list(reversed(unet.config.block_out_channels			)			)[block_id]
						elif name.startswith("""down_blocks"""			):
									__lowerCAmelCase      		=       int(name[len("""down_blocks."""			)]			)
									__lowerCAmelCase      		=       unet.config.block_out_channels[block_id]
						__lowerCAmelCase      		=       (
						    LoRAAttnProcessor2_0 if hasattr(lowercase      ,			"""scaled_dot_product_attention"""			) else LoRAAttnProcessor
						)
						__lowerCAmelCase      		=       lora_attn_processor_class(
						    hidden_size=lowercase      ,			cross_attention_dim=lowercase			)
			__lowerCAmelCase      		=       AttnProcsLayers(lowercase			)
			return lora_attn_procs, unet_lora_layers





def      _lowerCAmelCase		(		lowercase			)      ->      Optional[Any]:
			__lowerCAmelCase      		=       {}
			__lowerCAmelCase      		=       (
			    LoRAAttnProcessor2_0 if hasattr(lowercase      ,			"""scaled_dot_product_attention"""			) else LoRAAttnProcessor
			)
			for name, module in text_encoder.named_modules():
						if name.endswith(lowercase			):
									__lowerCAmelCase      		=       lora_attn_processor_class(
									    hidden_size=module.out_proj.out_features      ,			cross_attention_dim=lowercase			)
			return text_lora_attn_procs





def      _lowerCAmelCase		(		lowercase			)      ->      Union[str, Any]:
			__lowerCAmelCase      		=       create_text_encoder_lora_attn_procs(lowercase			)
			__lowerCAmelCase      		=       AttnProcsLayers(lowercase			)
			return text_encoder_lora_layers





def      _lowerCAmelCase		(		lowercase      ,			lowercase=False			)      ->      Dict:







			for _, attn_proc in text_lora_attn_procs.items():
						# set up.weights
						for layer_name, layer_module in attn_proc.named_modules():
									if layer_name.endswith("""_lora"""			):
												__lowerCAmelCase      		=       (
												    torch.randn_like(layer_module.up.weight			)
												    if randn_weight
												    else torch.zeros_like(layer_module.up.weight			)
												)
												__lowerCAmelCase      		=       torch.nn.Parameter(lowercase			)







class 					_UpperCAmelCase							(		unittest.TestCase     ):
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					torch.manual_seed(0    )
					__lowerCAmelCase      		=       UNetaDConditionModel(
					    block_out_channels=(32, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),cross_attention_dim=32,)
					__lowerCAmelCase      		=       DDIMScheduler(
					    beta_start=0.0_0085,beta_end=0.012,beta_schedule="""scaled_linear""",clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,steps_offset=1,)
					torch.manual_seed(0    )
					__lowerCAmelCase      		=       AutoencoderKL(
					    block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],latent_channels=4,)
					__lowerCAmelCase      		=       CLIPTextConfig(
					    bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=10_00,)
					__lowerCAmelCase      		=       CLIPTextModel(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

					__lowerCAmelCase		, __lowerCAmelCase      		=       create_unet_lora_layers(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       create_text_encoder_lora_layers(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       {
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """safety_checker""": None,
					    """feature_extractor""": None,
					}
					__lowerCAmelCase      		=       {
					    """unet_lora_layers""": unet_lora_layers,
					    """text_encoder_lora_layers""": text_encoder_lora_layers,
					    """unet_lora_attn_procs""": unet_lora_attn_procs,
					}
					return pipeline_components, lora_components
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       1
					__lowerCAmelCase      		=       10
					__lowerCAmelCase      		=       4
					__lowerCAmelCase      		=       (32, 32)

					__lowerCAmelCase      		=       torch.manual_seed(0    )
					__lowerCAmelCase      		=       floats_tensor((batch_size, num_channels) + sizes    )
					__lowerCAmelCase      		=       torch.randint(1,__SCREAMING_SNAKE_CASE,size=(batch_size, sequence_length),generator=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       {
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 6.0,
					    """output_type""": """numpy""",
					}

					return noise, input_ids, pipeline_inputs

					# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       77

					__lowerCAmelCase      		=       torch.randint(2,56,size=(1, max_seq_length),generator=torch.manual_seed(0    )    )

					__lowerCAmelCase      		=       {}
					__lowerCAmelCase      		=       inputs
					return prepared_inputs
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					LoraLoaderMixin.save_lora_weights(
					    save_directory=__SCREAMING_SNAKE_CASE,unet_lora_layers=lora_components["""unet_lora_layers"""],text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""],)
					self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""    )    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_inputs()

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_SNAKE_CASE,unet_lora_layers=lora_components["""unet_lora_layers"""],text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""],)
								self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""    )    )    )
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE    ),torch.from_numpy(__SCREAMING_SNAKE_CASE    )    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_inputs()

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_SNAKE_CASE,unet_lora_layers=lora_components["""unet_lora_layers"""],text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""],safe_serialization=__SCREAMING_SNAKE_CASE,)
								self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.safetensors"""    )    )    )
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE    ),torch.from_numpy(__SCREAMING_SNAKE_CASE    )    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       lora_components["""unet_lora_attn_procs"""]
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_inputs()

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
								__lowerCAmelCase      		=       sd_pipe.unet
								unet.set_attn_processor(__SCREAMING_SNAKE_CASE    )
								unet.save_attn_procs(__SCREAMING_SNAKE_CASE    )
								self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""    )    )    )
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE    ),torch.from_numpy(__SCREAMING_SNAKE_CASE    )    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       self.get_dummy_tokens()

					# inference without lora
					__lowerCAmelCase      		=       pipe.text_encoder(**__SCREAMING_SNAKE_CASE    )[0]
					assert outputs_without_lora.shape == (1, 77, 32)

					# create lora_attn_procs with zeroed out up.weights
					__lowerCAmelCase      		=       create_text_encoder_lora_attn_procs(pipe.text_encoder    )
					set_lora_up_weights(__SCREAMING_SNAKE_CASE,randn_weight=__SCREAMING_SNAKE_CASE    )

					# monkey patch
					pipe._modify_text_encoder(__SCREAMING_SNAKE_CASE    )

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					__lowerCAmelCase      		=       pipe.text_encoder(**__SCREAMING_SNAKE_CASE    )[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

					# create lora_attn_procs with randn up.weights
					__lowerCAmelCase      		=       create_text_encoder_lora_attn_procs(pipe.text_encoder    )
					set_lora_up_weights(__SCREAMING_SNAKE_CASE,randn_weight=__SCREAMING_SNAKE_CASE    )

					# monkey patch
					pipe._modify_text_encoder(__SCREAMING_SNAKE_CASE    )

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					__lowerCAmelCase      		=       pipe.text_encoder(**__SCREAMING_SNAKE_CASE    )[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       self.get_dummy_tokens()

					# inference without lora
					__lowerCAmelCase      		=       pipe.text_encoder(**__SCREAMING_SNAKE_CASE    )[0]
					assert outputs_without_lora.shape == (1, 77, 32)

					# create lora_attn_procs with randn up.weights
					__lowerCAmelCase      		=       create_text_encoder_lora_attn_procs(pipe.text_encoder    )
					set_lora_up_weights(__SCREAMING_SNAKE_CASE,randn_weight=__SCREAMING_SNAKE_CASE    )

					# monkey patch
					pipe._modify_text_encoder(__SCREAMING_SNAKE_CASE    )

					# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
					del text_attn_procs
					gc.collect()

					# inference with lora
					__lowerCAmelCase      		=       pipe.text_encoder(**__SCREAMING_SNAKE_CASE    )[0]
					assert outputs_with_lora.shape == (1, 77, 32)

					assert not torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ), "lora outputs should be different to without lora outputs"

					# remove monkey patch
					pipe._remove_text_encoder_monkey_patch()

					# inference with removed lora
					__lowerCAmelCase      		=       pipe.text_encoder(**__SCREAMING_SNAKE_CASE    )[0]
					assert outputs_without_lora_removed.shape == (1, 77, 32)

					assert torch.allclose(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ), "remove lora monkey patch should restore the original outputs"
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_inputs()

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_SNAKE_CASE,unet_lora_layers=lora_components["""unet_lora_layers"""],text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""],)
								self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""    )    )    )
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       lora_images[0, -3:, -3:, -1]

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE,cross_attention_kwargs={"""scale""": 0.5}    ).images
					__lowerCAmelCase      		=       lora_images_with_scale[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(
					    torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE    ),torch.from_numpy(__SCREAMING_SNAKE_CASE    )    )    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					with tempfile.TemporaryDirectory() as tmpdirname:
								self.create_lora_weight_file(__SCREAMING_SNAKE_CASE    )

								__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
								__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								# check if vanilla attention processors are used
								for _, module in sd_pipe.unet.named_modules():
											if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
														self.assertIsInstance(module.processor,(AttnProcessor, AttnProcessor2_0)    )

            # load LoRA weight file
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

								# check if lora attention processors are used
								for _, module in sd_pipe.unet.named_modules():
											if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
														__lowerCAmelCase      		=       (
														    LoRAAttnProcessor2_0 if hasattr(__SCREAMING_SNAKE_CASE,"""scaled_dot_product_attention"""    ) else LoRAAttnProcessor
														)
														self.assertIsInstance(module.processor,__SCREAMING_SNAKE_CASE    )
		@unittest.skipIf(torch_device != """cuda""","""This test is supposed to run on GPU"""    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					with tempfile.TemporaryDirectory() as tmpdirname:
								self.create_lora_weight_file(__SCREAMING_SNAKE_CASE    )

								__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
								__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

								# check if xFormers attention processors are used
								for _, module in sd_pipe.unet.named_modules():
											if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
														self.assertIsInstance(module.processor,__SCREAMING_SNAKE_CASE    )

            # load LoRA weight file
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

								# check if lora attention processors are used
								for _, module in sd_pipe.unet.named_modules():
											if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
														self.assertIsInstance(module.processor,__SCREAMING_SNAKE_CASE    )







		@unittest.skipIf(torch_device != """cuda""","""This test is supposed to run on GPU"""    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_components()
					__lowerCAmelCase      		=       StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       sd_pipe.to(__SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       self.get_dummy_inputs()

					# enable XFormers
					sd_pipe.enable_xformers_memory_efficient_attention()

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       original_images[0, -3:, -3:, -1]

					with tempfile.TemporaryDirectory() as tmpdirname:
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_SNAKE_CASE,unet_lora_layers=lora_components["""unet_lora_layers"""],text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""],)
								self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,"""pytorch_lora_weights.bin"""    )    )    )
								sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       sd_pipe(**__SCREAMING_SNAKE_CASE    ).images
					__lowerCAmelCase      		=       lora_images[0, -3:, -3:, -1]

					# Outputs shouldn't match.
					self.assertFalse(torch.allclose(torch.from_numpy(__SCREAMING_SNAKE_CASE    ),torch.from_numpy(__SCREAMING_SNAKE_CASE    )    )    )






'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]

			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue

			def normalize(lowercase			):
						return min(lowercase      ,			255			)

			for i in range(lowercase			):
						for j in range(lowercase			):
									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
									__lowerCAmelCase      		=       [
									    normalize(lowercase			),
									    normalize(greyscale + factor			),
									    normalize(greyscale + 2 * factor			),
									]

			return img


if __name__ == "__main__":
	# read original image
	_a      :				Any											=					{
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(f'Original image with sepia (factor: {percentage})', img)

	waitKey(0)
	destroyAllWindows()

