
"""simple docstring"""



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,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Union[str, Any]           =     StableDiffusionDiffEditPipeline
      UpperCamelCase							:   Any           =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
      UpperCamelCase							:   List[str]           =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
      UpperCamelCase							:   Any           =     frozenset(
          []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCamelCase							:   List[Any]           =     frozenset([]      )



      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					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     ,	attention_head_dim=(2, 4)     ,	use_linear_projection=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(
              beta_start=0.00085     ,	beta_end=0.012     ,	beta_schedule='scaled_linear'     ,	clip_sample=UpperCamelCase_     ,	set_alpha_to_one=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      List[Any]     							=					DDIMInverseScheduler(
              beta_start=0.00085     ,	beta_end=0.012     ,	beta_schedule='scaled_linear'     ,	clip_sample=UpperCamelCase_     ,	set_alpha_to_zero=UpperCamelCase_     ,	)
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[int]     							=					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     ,	sample_size=128     ,	)
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					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=1_000     ,	hidden_act='gelu'     ,	projection_dim=512     ,	)
          UpperCAmelCase__	:      str     							=					CLIPTextModel(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

          UpperCAmelCase__	:      Optional[int]     							=					{
              'unet': unet,
              'scheduler': scheduler,
              'inverse_scheduler': inverse_scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      Tuple     							=					floats_tensor((1, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor((1, 2, 4, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					{
              'prompt': 'a dog and a newt',
              'mask_image': mask,
              'image_latents': latents,
              'generator': generator,
              'num_inference_steps': 2,
              'inpaint_strength': 1.0,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }

          return inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      str     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      List[Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      )
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[Any]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					{
              'image': image,
              'source_prompt': 'a cat and a frog',
              'target_prompt': 'a dog and a newt',
              'generator': generator,
              'num_inference_steps': 2,
              'num_maps_per_mask': 2,
              'mask_encode_strength': 1.0,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }

          return inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Optional[int]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      )
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Tuple     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Tuple     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					{
              'image': image,
              'prompt': 'a cat and a frog',
              'generator': generator,
              'num_inference_steps': 2,
              'inpaint_strength': 1.0,
              'guidance_scale': 6.0,
              'decode_latents': True,
              'output_type': 'numpy',
          }
          return inputs



      def  __snake_case							(   self      ):
          if not hasattr(self.pipeline_class     ,	'_optional_components'      ):
              return

          UpperCAmelCase__	:      int     							=					self.get_dummy_components()
          UpperCAmelCase__	:      List[Any]     							=					self.pipeline_class(**UpperCamelCase_      )
          pipe.to(UpperCamelCase_      )
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          # set all optional components to None and update pipeline config accordingly
          for optional_component in pipe._optional_components:
              setattr(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
          pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}      )

          UpperCAmelCase__	:      Dict     							=					self.get_dummy_inputs(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipe(**UpperCamelCase_      )[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      str     							=					self.pipeline_class.from_pretrained(UpperCamelCase_      )
              pipe_loaded.to(UpperCamelCase_      )
              pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_      )

          for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(UpperCamelCase_     ,	UpperCamelCase_      ) is None     ,	F'''`{optional_component}` did not stay set to None after loading.'''     ,	)

          UpperCAmelCase__	:      Optional[int]     							=					self.get_dummy_inputs(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipe_loaded(**UpperCamelCase_      )[0]

          UpperCAmelCase__	:      Tuple     							=					np.abs(output - output_loaded      ).max()
          self.assertLess(UpperCamelCase_     ,	1E-4      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					'cpu'

          UpperCAmelCase__	:      Optional[Any]     							=					self.get_dummy_components()
          UpperCAmelCase__	:      List[str]     							=					self.pipeline_class(**UpperCamelCase_      )
          pipe.to(UpperCamelCase_      )
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					self.get_dummy_mask_inputs(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					pipe.generate_mask(**UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					mask[0, -3:, -3:]

          self.assertEqual(mask.shape     ,	(1, 16, 16)      )
          UpperCAmelCase__	:      str     							=					np.array([0] * 9      )
          UpperCAmelCase__	:      Dict     							=					np.abs(mask_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(UpperCamelCase_     ,	1E-3      )
          self.assertEqual(mask[0, -3, -4]     ,	0      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					'cpu'

          UpperCAmelCase__	:      List[str]     							=					self.get_dummy_components()
          UpperCAmelCase__	:      Optional[int]     							=					self.pipeline_class(**UpperCamelCase_      )
          pipe.to(UpperCamelCase_      )
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					self.get_dummy_inversion_inputs(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					pipe.invert(**UpperCamelCase_      ).images
          UpperCAmelCase__	:      List[str]     							=					image[0, -1, -3:, -3:]

          self.assertEqual(image.shape     ,	(2, 32, 32, 3)      )
          UpperCAmelCase__	:      Optional[int]     							=					np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]     ,	)
          UpperCAmelCase__	:      Any     							=					np.abs(image_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(UpperCamelCase_     ,	1E-3      )



      def  __snake_case							(   self      ):
          super().test_inference_batch_single_identical(expected_max_diff=5E-3      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					'cpu'

          UpperCAmelCase__	:      int     							=					self.get_dummy_components()

          UpperCAmelCase__	:      List[Any]     							=					{'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
          UpperCAmelCase__	:      List[str]     							=					DPMSolverMultistepScheduler(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					DPMSolverMultistepInverseScheduler(**UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					self.pipeline_class(**UpperCamelCase_      )
          pipe.to(UpperCamelCase_      )
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					self.get_dummy_inversion_inputs(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.invert(**UpperCamelCase_      ).images
          UpperCAmelCase__	:      Optional[Any]     							=					image[0, -1, -3:, -3:]

          self.assertEqual(image.shape     ,	(2, 32, 32, 3)      )
          UpperCAmelCase__	:      Dict     							=					np.array(
              [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]     ,	)
          UpperCAmelCase__	:      str     							=					np.abs(image_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(UpperCamelCase_     ,	1E-3      )





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



      def  __snake_case							(   self      ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      @classmethod
      def  __snake_case							(   cls      ):
          UpperCAmelCase__	:      Dict     							=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'      )

          UpperCAmelCase__	:      List[Any]     							=					raw_image.convert('RGB'      ).resize((768, 768)      )

          UpperCAmelCase__	:      List[Any]     							=					raw_image



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      List[str]     							=					StableDiffusionDiffEditPipeline.from_pretrained(
              'stabilityai/stable-diffusion-2-1'     ,	safety_checker=UpperCamelCase_     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Tuple     							=					DDIMScheduler.from_config(pipe.scheduler.config      )
          UpperCAmelCase__	:      Tuple     							=					DDIMInverseScheduler.from_config(pipe.scheduler.config      )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[str]     							=					'a bowl of fruit'
          UpperCAmelCase__	:      Optional[Any]     							=					'a bowl of pears'

          UpperCAmelCase__	:      Tuple     							=					pipe.generate_mask(
              image=self.raw_image     ,	source_prompt=UpperCamelCase_     ,	target_prompt=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	)

          UpperCAmelCase__	:      str     							=					pipe.invert(
              prompt=UpperCamelCase_     ,	image=self.raw_image     ,	inpaint_strength=0.7     ,	generator=UpperCamelCase_      ).latents

          UpperCAmelCase__	:      Any     							=					pipe(
              prompt=UpperCamelCase_     ,	mask_image=UpperCamelCase_     ,	image_latents=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	negative_prompt=UpperCamelCase_     ,	inpaint_strength=0.7     ,	output_type='numpy'     ,	).images[0]

          UpperCAmelCase__	:      List[str]     							=					(
              np.array(
                  load_image(
                      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                      '/diffedit/pears.png'      ).resize((768, 768)      )      )
              / 255
          )
          assert np.abs((expected_image - image).max()      ) < 5E-1



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      Any     							=					StableDiffusionDiffEditPipeline.from_pretrained(
              'stabilityai/stable-diffusion-2-1'     ,	safety_checker=UpperCamelCase_     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      List[Any]     							=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
          UpperCAmelCase__	:      List[Any]     							=					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config      )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[str]     							=					'a bowl of fruit'
          UpperCAmelCase__	:      Tuple     							=					'a bowl of pears'

          UpperCAmelCase__	:      List[Any]     							=					pipe.generate_mask(
              image=self.raw_image     ,	source_prompt=UpperCamelCase_     ,	target_prompt=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Tuple     							=					pipe.invert(
              prompt=UpperCamelCase_     ,	image=self.raw_image     ,	inpaint_strength=0.7     ,	generator=UpperCamelCase_     ,	num_inference_steps=25     ,	).latents

          UpperCAmelCase__	:      Any     							=					pipe(
              prompt=UpperCamelCase_     ,	mask_image=UpperCamelCase_     ,	image_latents=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	negative_prompt=UpperCamelCase_     ,	inpaint_strength=0.7     ,	num_inference_steps=25     ,	output_type='numpy'     ,	).images[0]

          UpperCAmelCase__	:      Union[str, Any]     							=					(
              np.array(
                  load_image(
                      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                      '/diffedit/pears.png'      ).resize((768, 768)      )      )
              / 255
          )
          assert np.abs((expected_image - image).max()      ) < 5E-1



'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

    # in case there's no path between the 2 nodes
							return []







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Tuple):
							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							UpperCamelCase   			=     [start]
							UpperCamelCase   			=     set(A)
							# Keep tab on distances from `start` node.
							UpperCamelCase   			=     {start: 0, target: -1}
							while queue:
														UpperCamelCase   			=     queue.pop(0)
														if node == target:
																					UpperCamelCase   			=     (
																					    dist[node] if dist[target] == -1 else min(dist[target]		,     dist[node])
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(A)
																												queue.append(A)
																												UpperCamelCase   			=     dist[node] + 1
							return dist[target]


if __name__ == "__main__":
						print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
						print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4




