
"""simple docstring"""


# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class 	lowerCamelCase__      (      UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_				,			unittest.TestCase       ):
			lowerCamelCase						      =	StableDiffusionControlNetInpaintPipeline
			lowerCamelCase						      =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase						      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase						      =	frozenset({'''control_image'''}       )  # skip `image` and `mask` for now, only test for control_image
			lowerCamelCase						      =	TEXT_TO_IMAGE_IMAGE_PARAMS



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				torch.manual_seed(0						)
				_lowerCAmelCase			=UNetaDConditionModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  sample_size=32  ,  in_channels=9  ,  out_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,  cross_attention_dim=32  ,  )
				torch.manual_seed(0						)
				_lowerCAmelCase			=ControlNetModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  in_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  cross_attention_dim=32  ,  conditioning_embedding_out_channels=(16, 32)  ,  )
				torch.manual_seed(0						)
				_lowerCAmelCase			=DDIMScheduler(
				    beta_start=0.0_0_0_8_5  ,  beta_end=0.0_1_2  ,  beta_schedule="""scaled_linear"""  ,  clip_sample=__SCREAMING_SNAKE_CASE  ,  set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,  )
				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  ,  )
				torch.manual_seed(0						)
				_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			={
				    """unet""": unet,
				    """controlnet""": controlnet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=0						)				->  Dict:
				if str(__SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
					_lowerCAmelCase			=torch.manual_seed(__SCREAMING_SNAKE_CASE						)
				else:
					_lowerCAmelCase			=torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)

				_lowerCAmelCase			=2
				_lowerCAmelCase			=randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,  generator=__SCREAMING_SNAKE_CASE  ,  device=torch.device(__SCREAMING_SNAKE_CASE						)  ,  )
				_lowerCAmelCase			=floats_tensor((1, 3, 32, 32)  ,  rng=random.Random(__SCREAMING_SNAKE_CASE						)						).to(__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=init_image.cpu().permute(0  ,  2  ,  3  ,  1						)[0]

				_lowerCAmelCase			=Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE						)						).convert("""RGB"""						).resize((64, 64)						)
				_lowerCAmelCase			=Image.fromarray(np.uinta(init_image + 4						)						).convert("""RGB"""						).resize((64, 64)						)

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

				return inputs



			def       _lowerCAmelCase	(							self						)				->  int:
				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
			def       _lowerCAmelCase	(							self						)				->  Dict:
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)







class 	lowerCamelCase__      (      UpperCamelCase_       ):
			lowerCamelCase						      =	StableDiffusionControlNetInpaintPipeline
			lowerCamelCase						      =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase						      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase						      =	frozenset([]       )



			def       _lowerCAmelCase	(							self						)				->  Dict:
				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  ,  )
				torch.manual_seed(0						)
				_lowerCAmelCase			=ControlNetModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  in_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  cross_attention_dim=32  ,  conditioning_embedding_out_channels=(16, 32)  ,  )
				torch.manual_seed(0						)
				_lowerCAmelCase			=DDIMScheduler(
				    beta_start=0.0_0_0_8_5  ,  beta_end=0.0_1_2  ,  beta_schedule="""scaled_linear"""  ,  clip_sample=__SCREAMING_SNAKE_CASE  ,  set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,  )
				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  ,  )
				torch.manual_seed(0						)
				_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			={
				    """unet""": unet,
				    """controlnet""": controlnet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components







class 	lowerCamelCase__      (      UpperCamelCase_				,			UpperCamelCase_				,			unittest.TestCase       ):
			lowerCamelCase						      =	StableDiffusionControlNetInpaintPipeline
			lowerCamelCase						      =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase						      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				torch.manual_seed(0						)
				_lowerCAmelCase			=UNetaDConditionModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  sample_size=32  ,  in_channels=9  ,  out_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,  cross_attention_dim=32  ,  )
				torch.manual_seed(0						)

				def init_weights(__UpperCAmelCase						):
					if isinstance(__SCREAMING_SNAKE_CASE  ,  torch.nn.Convad						):
						torch.nn.init.normal(m.weight						)
						m.bias.data.fill_(1.0						)

				_lowerCAmelCase			=ControlNetModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  in_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  cross_attention_dim=32  ,  conditioning_embedding_out_channels=(16, 32)  ,  )
				controlneta.controlnet_down_blocks.apply(__SCREAMING_SNAKE_CASE						)

				torch.manual_seed(0						)
				_lowerCAmelCase			=ControlNetModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  in_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  cross_attention_dim=32  ,  conditioning_embedding_out_channels=(16, 32)  ,  )
				controlneta.controlnet_down_blocks.apply(__SCREAMING_SNAKE_CASE						)

				torch.manual_seed(0						)
				_lowerCAmelCase			=DDIMScheduler(
				    beta_start=0.0_0_0_8_5  ,  beta_end=0.0_1_2  ,  beta_schedule="""scaled_linear"""  ,  clip_sample=__SCREAMING_SNAKE_CASE  ,  set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,  )
				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  ,  )
				torch.manual_seed(0						)
				_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			=MultiControlNetModel([controlneta, controlneta]						)

				_lowerCAmelCase			={
				    """unet""": unet,
				    """controlnet""": controlnet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=0						)				->  List[Any]:
				if str(__SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
					_lowerCAmelCase			=torch.manual_seed(__SCREAMING_SNAKE_CASE						)
				else:
					_lowerCAmelCase			=torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)

				_lowerCAmelCase			=2

				_lowerCAmelCase			=[
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,  generator=__SCREAMING_SNAKE_CASE  ,  device=torch.device(__SCREAMING_SNAKE_CASE						)  ,  ),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,  generator=__SCREAMING_SNAKE_CASE  ,  device=torch.device(__SCREAMING_SNAKE_CASE						)  ,  ),
				]
				_lowerCAmelCase			=floats_tensor((1, 3, 32, 32)  ,  rng=random.Random(__SCREAMING_SNAKE_CASE						)						).to(__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=init_image.cpu().permute(0  ,  2  ,  3  ,  1						)[0]

				_lowerCAmelCase			=Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE						)						).convert("""RGB"""						).resize((64, 64)						)
				_lowerCAmelCase			=Image.fromarray(np.uinta(init_image + 4						)						).convert("""RGB"""						).resize((64, 64)						)

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

				return inputs



			def       _lowerCAmelCase	(							self						)				->  Dict:
				_lowerCAmelCase			=self.get_dummy_components()
				_lowerCAmelCase			=self.pipeline_class(**__SCREAMING_SNAKE_CASE						)
				pipe.to(__SCREAMING_SNAKE_CASE						)

				_lowerCAmelCase			=10.0
				_lowerCAmelCase			=4

				_lowerCAmelCase			=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=steps
				_lowerCAmelCase			=scale
				_lowerCAmelCase			=pipe(**__SCREAMING_SNAKE_CASE						)[0]

				_lowerCAmelCase			=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=steps
				_lowerCAmelCase			=scale
				_lowerCAmelCase			=pipe(**__SCREAMING_SNAKE_CASE  ,  control_guidance_start=0.1  ,  control_guidance_end=0.2						)[0]

				_lowerCAmelCase			=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=steps
				_lowerCAmelCase			=scale
				_lowerCAmelCase			=pipe(**__SCREAMING_SNAKE_CASE  ,  control_guidance_start=[0.1, 0.3]  ,  control_guidance_end=[0.2, 0.7]						)[0]

				_lowerCAmelCase			=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=steps
				_lowerCAmelCase			=scale
				_lowerCAmelCase			=pipe(**__SCREAMING_SNAKE_CASE  ,  control_guidance_start=0.4  ,  control_guidance_end=[0.5, 0.8]						)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3



			def       _lowerCAmelCase	(							self						)				->  str:
				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
			def       _lowerCAmelCase	(							self						)				->  Any:
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)



			def       _lowerCAmelCase	(							self						)				->  Any:
				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				_lowerCAmelCase			=self.get_dummy_components()
				_lowerCAmelCase			=self.pipeline_class(**__SCREAMING_SNAKE_CASE						)
				pipe.to(__SCREAMING_SNAKE_CASE						)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)
				with tempfile.TemporaryDirectory() as tmpdir:
					try:
						# save_pretrained is not implemented for Multi-ControlNet
						pipe.save_pretrained(__SCREAMING_SNAKE_CASE						)
					except NotImplementedError:
						pass







@slow
@require_torch_gpu
class 	lowerCamelCase__      (      unittest.TestCase       ):



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



			def       _lowerCAmelCase	(							self						)				->  List[Any]:
				_lowerCAmelCase			=ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""						)

				_lowerCAmelCase			=StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    """runwayml/stable-diffusion-inpainting"""  ,  safety_checker=__SCREAMING_SNAKE_CASE  ,  controlnet=__SCREAMING_SNAKE_CASE						)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

				_lowerCAmelCase			=torch.Generator(device="""cpu"""						).manual_seed(0						)
				_lowerCAmelCase			=load_image(
				    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""						).resize((5_12, 5_12)						)

				_lowerCAmelCase			=load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
				    """/stable_diffusion_inpaint/input_bench_mask.png"""						).resize((5_12, 5_12)						)

				_lowerCAmelCase			="""pitch black hole"""

				_lowerCAmelCase			=load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""						).resize((5_12, 5_12)						)

				_lowerCAmelCase			=pipe(
				    __SCREAMING_SNAKE_CASE  ,  image=__SCREAMING_SNAKE_CASE  ,  mask_image=__SCREAMING_SNAKE_CASE  ,  control_image=__SCREAMING_SNAKE_CASE  ,  generator=__SCREAMING_SNAKE_CASE  ,  output_type="""np"""  ,  num_inference_steps=3  ,  )

				_lowerCAmelCase			=output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				_lowerCAmelCase			=load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""						)

				assert np.abs(expected_image - image						).max() < 9e-2



			def       _lowerCAmelCase	(							self						)				->  Dict:
				_lowerCAmelCase			=ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""						)

				_lowerCAmelCase			=StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    """runwayml/stable-diffusion-v1-5"""  ,  safety_checker=__SCREAMING_SNAKE_CASE  ,  controlnet=__SCREAMING_SNAKE_CASE						)
				_lowerCAmelCase			=DDIMScheduler.from_config(pipe.scheduler.config						)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

				_lowerCAmelCase			=torch.Generator(device="""cpu"""						).manual_seed(33						)

				_lowerCAmelCase			=load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""						)
				_lowerCAmelCase			=init_image.resize((5_12, 5_12)						)

				_lowerCAmelCase			=load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""						)
				_lowerCAmelCase			=mask_image.resize((5_12, 5_12)						)

				_lowerCAmelCase			="""a handsome man with ray-ban sunglasses"""

				def make_inpaint_condition(__UpperCAmelCase  ,  __UpperCAmelCase						):
					_lowerCAmelCase			=np.array(image.convert("""RGB"""						)						).astype(np.floataa						) / 2_55.0
					_lowerCAmelCase			=np.array(image_mask.convert("""L"""						)						).astype(np.floataa						) / 2_55.0

					assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
					_lowerCAmelCase			=-1.0  # set as masked pixel
					_lowerCAmelCase			=np.expand_dims(__SCREAMING_SNAKE_CASE  ,  0						).transpose(0  ,  3  ,  1  ,  2						)
					_lowerCAmelCase			=torch.from_numpy(__SCREAMING_SNAKE_CASE						)
					return image

				_lowerCAmelCase			=make_inpaint_condition(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE						)

				_lowerCAmelCase			=pipe(
				    __SCREAMING_SNAKE_CASE  ,  image=__SCREAMING_SNAKE_CASE  ,  mask_image=__SCREAMING_SNAKE_CASE  ,  control_image=__SCREAMING_SNAKE_CASE  ,  guidance_scale=9.0  ,  eta=1.0  ,  generator=__SCREAMING_SNAKE_CASE  ,  num_inference_steps=20  ,  output_type="""np"""  ,  )
				_lowerCAmelCase			=output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				_lowerCAmelCase			=load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""						)

				assert np.abs(expected_image - image						).max() < 9e-2





"""simple docstring"""


import os
import sys

from . import rsa_key_generator as rkg

__A 		= 128
__A 		= 256



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE )       ->				list[int]:
	_lowerCAmelCase			=message.encode("""ascii""" )

	_lowerCAmelCase			=[]
	for block_start in range(0					,				len(__UpperCamelCase )					,				__UpperCamelCase ):
		_lowerCAmelCase			=0
		for i in range(__UpperCamelCase					,				min(block_start + block_size					,				len(__UpperCamelCase ) ) ):
			block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
		block_ints.append(__UpperCamelCase )
	return block_ints



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE )       ->				str:
	_lowerCAmelCase			=[]
	for block_int in block_ints:
		_lowerCAmelCase			=[]
		for i in range(block_size - 1					,				-1					,				-1 ):
			if len(__UpperCamelCase ) + i < message_length:
				_lowerCAmelCase			=block_int // (BYTE_SIZE**i)
				_lowerCAmelCase			=block_int % (BYTE_SIZE**i)
				block_message.insert(0					,				chr(__UpperCamelCase ) )
		message.extend(__UpperCamelCase )
	return "".join(__UpperCamelCase )



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE )       ->				list[int]:
	_lowerCAmelCase			=[]
	_lowerCAmelCase		,							_lowerCAmelCase			=key

	for block in get_blocks_from_text(__UpperCamelCase					,				__UpperCamelCase ):
		encrypted_blocks.append(pow(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ) )
	return encrypted_blocks



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE					,				)       ->				str:
	_lowerCAmelCase			=[]
	_lowerCAmelCase		,							_lowerCAmelCase			=key
	for block in encrypted_blocks:
		decrypted_blocks.append(pow(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ) )
	return get_text_from_blocks(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )



def 					_lowerCamelCase(__UpperCamelCase )       ->				tuple[int, int, int]:
	with open(__UpperCamelCase ) as fo:
		_lowerCAmelCase			=fo.read()
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=content.split(""",""" )
	return (int(__UpperCamelCase ), int(__UpperCamelCase ), int(__UpperCamelCase ))



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE					,				)       ->				str:
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=read_key_file(__UpperCamelCase )
	if key_size < block_size * 8:
		sys.exit(
		    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
		    """requires the block size to be equal to or greater than the key size. """
		    """Either decrease the block size or use different keys.""".format(
		        block_size * 8					,				__UpperCamelCase ) )

	_lowerCAmelCase			=[str(__UpperCamelCase ) for i in encrypt_message(__UpperCamelCase					,				(n, e)					,				__UpperCamelCase )]

	_lowerCAmelCase			=""",""".join(__UpperCamelCase )
	_lowerCAmelCase			=F'''{len(__UpperCamelCase )}_{block_size}_{encrypted_content}'''
	with open(__UpperCamelCase					,				"""w""" ) as fo:
		fo.write(__UpperCamelCase )
	return encrypted_content



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				str:
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=read_key_file(__UpperCamelCase )
	with open(__UpperCamelCase ) as fo:
		_lowerCAmelCase			=fo.read()
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=content.split("""_""" )
	_lowerCAmelCase			=int(__UpperCamelCase )
	_lowerCAmelCase			=int(__UpperCamelCase )

	if key_size < block_size * 8:
		sys.exit(
		    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
		    """requires the block size to be equal to or greater than the key size. """
		    """Did you specify the correct key file and encrypted file?""".format(
		        block_size * 8					,				__UpperCamelCase ) )

	_lowerCAmelCase			=[]
	for block in encrypted_message.split(""",""" ):
		encrypted_blocks.append(int(__UpperCamelCase ) )

	return decrypt_message(__UpperCamelCase					,				__UpperCamelCase					,				(n, d)					,				__UpperCamelCase )



def 					_lowerCamelCase()       ->				None:
	_lowerCAmelCase			="""encrypted_file.txt"""
	_lowerCAmelCase			=input(R"""Encrypt\Decrypt [e\d]: """ )

	if response.lower().startswith("""e""" ):
		_lowerCAmelCase			="""encrypt"""
	elif response.lower().startswith("""d""" ):
		_lowerCAmelCase			="""decrypt"""

	if mode == "encrypt":
		if not os.path.exists("""rsa_pubkey.txt""" ):
			rkg.make_key_files("""rsa"""					,				1024 )

		_lowerCAmelCase			=input("""\nEnter message: """ )
		_lowerCAmelCase			="""rsa_pubkey.txt"""
		print(F'''Encrypting and writing to {filename}...''' )
		_lowerCAmelCase			=encrypt_and_write_to_file(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )

		print("""\nEncrypted text:""" )
		print(__UpperCamelCase )

	elif mode == "decrypt":
		_lowerCAmelCase			="""rsa_privkey.txt"""
		print(F'''Reading from {filename} and decrypting...''' )
		_lowerCAmelCase			=read_from_file_and_decrypt(__UpperCamelCase					,				__UpperCamelCase )
		print("""writing decryption to rsa_decryption.txt...""" )
		with open("""rsa_decryption.txt"""					,				"""w""" ) as dec:
			dec.write(__UpperCamelCase )

		print("""\nDecryption:""" )
		print(__UpperCamelCase )


if __name__ == "__main__":
			main()



