import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class  __lowerCAmelCase	(							a_,						a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Dict					     =			StableDiffusionModelEditingPipeline
 _lowerCamelCase    :						List[Any]					     =			TEXT_TO_IMAGE_PARAMS
 _lowerCamelCase    :						Optional[Any]					     =			TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase    :						Union[str, Any]					     =			TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowerCamelCase    :						int					     =			TEXT_TO_IMAGE_IMAGE_PARAMS

 def        __snake_case       (      self							:		Optional[int]	)			->				List[str]:
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[int]	    =	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=4							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=3_2							,      )
        _UpperCamelCase  :							Dict	    =	DDIMScheduler()
        torch.manual_seed(0	)
        _UpperCamelCase  :							List[str]	    =	AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,      in_channels=3							,      out_channels=3							,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,      latent_channels=4							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[Any]	    =	CLIPTextConfig(
            bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
        _UpperCamelCase  :							str	    =	CLIPTextModel(lowerCamelCase_	)
        _UpperCamelCase  :							Dict	    =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

        _UpperCamelCase  :							Any	    =	{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		List[str]=0	)			->				str:
        _UpperCamelCase  :							Any	    =	torch.manual_seed(lowerCamelCase_	)
        _UpperCamelCase  :							Optional[Any]	    =	{
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs

 def        __snake_case       (      self							:		str	)			->				List[str]:
        _UpperCamelCase  :							Tuple	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							str	    =	self.get_dummy_components()
        _UpperCamelCase  :							Tuple	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
        _UpperCamelCase  :							Optional[int]	    =	sd_pipe.to(lowerCamelCase_	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_inputs(lowerCamelCase_	)
        _UpperCamelCase  :							Optional[Any]	    =	sd_pipe(**lowerCamelCase_	).images
        _UpperCamelCase  :							str	    =	image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							Union[str, Any]	    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

 def        __snake_case       (      self							:		Optional[int]	)			->				List[Any]:
        _UpperCamelCase  :							str	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							int	    =	self.get_dummy_components()
        _UpperCamelCase  :							List[str]	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
        _UpperCamelCase  :							Optional[int]	    =	sd_pipe.to(lowerCamelCase_	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_inputs(lowerCamelCase_	)
        _UpperCamelCase  :							List[Any]	    =	'french fries'
        _UpperCamelCase  :							List[Any]	    =	sd_pipe(**lowerCamelCase_							,      negative_prompt=lowerCamelCase_	)
        _UpperCamelCase  :							Optional[Any]	    =	output.images
        _UpperCamelCase  :							int	    =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							int	    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

 def        __snake_case       (      self							:		Optional[Any]	)			->				str:
        _UpperCamelCase  :							int	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Union[str, Any]	    =	self.get_dummy_components()
        _UpperCamelCase  :							str	    =	EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='scaled_linear'	)
        _UpperCamelCase  :							int	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
        _UpperCamelCase  :							Any	    =	sd_pipe.to(lowerCamelCase_	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

        _UpperCamelCase  :							int	    =	self.get_dummy_inputs(lowerCamelCase_	)
        _UpperCamelCase  :							Any	    =	sd_pipe(**lowerCamelCase_	).images
        _UpperCamelCase  :							Optional[Any]	    =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							Any	    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

 def        __snake_case       (      self							:		List[Any]	)			->				str:
        _UpperCamelCase  :							List[Any]	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							List[Any]	    =	self.get_dummy_components()
        _UpperCamelCase  :							List[str]	    =	PNDMScheduler()
        _UpperCamelCase  :							List[Any]	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
        _UpperCamelCase  :							Optional[Any]	    =	sd_pipe.to(lowerCamelCase_	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

        _UpperCamelCase  :							Tuple	    =	self.get_dummy_inputs(lowerCamelCase_	)
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowerCamelCase_	):
               _UpperCamelCase  :							Optional[Any]	    =	sd_pipe(**lowerCamelCase_	).images

 def        __snake_case       (      self							:		Dict	)			->				str:
        super().test_inference_batch_single_identical(expected_max_diff=5e-3	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[Any]:
        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3	)







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



 '''simple docstring'''

 def        __snake_case       (      self							:		Any	)			->				int:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str=0	)			->				Union[str, Any]:
        _UpperCamelCase  :							Tuple	    =	torch.manual_seed(lowerCamelCase_	)
        _UpperCamelCase  :							Optional[int]	    =	{
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        _UpperCamelCase  :							Dict	    =	'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase  :							Tuple	    =	StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_							,      safety_checker=lowerCamelCase_	)
        pipe.to(lowerCamelCase_	)
        pipe.set_progress_bar_config(disable=lowerCamelCase_	)
        pipe.enable_attention_slicing()

        _UpperCamelCase  :							Tuple	    =	self.get_inputs()
        _UpperCamelCase  :							str	    =	pipe(**lowerCamelCase_	).images
        _UpperCamelCase  :							Union[str, Any]	    =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        _UpperCamelCase  :							List[str]	    =	np.array(
            [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-2

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'							,      'A pack of blue roses'	)

        _UpperCamelCase  :							Optional[Any]	    =	pipe(**lowerCamelCase_	).images
        _UpperCamelCase  :							Dict	    =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

 def        __snake_case       (      self							:		List[str]	)			->				Dict:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCamelCase  :							Optional[Any]	    =	'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase  :							Optional[Any]	    =	DDIMScheduler.from_pretrained(lowerCamelCase_							,      subfolder='scheduler'	)
        _UpperCamelCase  :							Optional[int]	    =	StableDiffusionModelEditingPipeline.from_pretrained(
            lowerCamelCase_							,      scheduler=lowerCamelCase_							,      safety_checker=lowerCamelCase_	)
        _UpperCamelCase  :							Tuple	    =	pipe.to(lowerCamelCase_	)
        pipe.set_progress_bar_config(disable=lowerCamelCase_	)
        pipe.enable_attention_slicing(1	)
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase  :							Any	    =	self.get_inputs()
        _UpperCamelCase  :							int	    =	pipe(**lowerCamelCase_	)

        _UpperCamelCase  :							Dict	    =	torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 1_0**9








_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
