


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        UpperCAmelCase__			(   A_  ,    A_  ,    A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						str    =    StableDiffusionModelEditingPipeline
    UpperCAmelCase__		:						Optional[Any]    =    TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase__		:						Union[str, Any]    =    TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase__		:						Optional[Any]    =    TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__		:						List[Any]    =    TEXT_TO_IMAGE_IMAGE_PARAMS






    def _a   (     self       )    ->			Optional[Any]:
         torch.manual_seed(0       )
         __UpperCamelCase       				=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   ,  )
         __UpperCamelCase       				=DDIMScheduler()
         torch.manual_seed(0       )
         __UpperCamelCase       				=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       )
         __UpperCamelCase       				=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=1000   ,  )
         __UpperCamelCase       				=CLIPTextModel(A_       )
         __UpperCamelCase       				=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

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






    def _a   (     self   ,  A_   ,  A_=0       )    ->			List[Any]:
         __UpperCamelCase       				=torch.manual_seed(A_       )
         __UpperCamelCase       				={
             '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 _a   (     self       )    ->			List[Any]:
         __UpperCamelCase       				='cpu'  # ensure determinism for the device-dependent torch.Generator
         __UpperCamelCase       				=self.get_dummy_components()
         __UpperCamelCase       				=StableDiffusionModelEditingPipeline(**A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=self.get_dummy_inputs(A_       )
         __UpperCamelCase       				=sd_pipe(**A_       ).images
         __UpperCamelCase       				=image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         __UpperCamelCase       				=np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]       )

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






    def _a   (     self       )    ->			Union[str, Any]:
         __UpperCamelCase       				='cpu'  # ensure determinism for the device-dependent torch.Generator
         __UpperCamelCase       				=self.get_dummy_components()
         __UpperCamelCase       				=StableDiffusionModelEditingPipeline(**A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=self.get_dummy_inputs(A_       )
         __UpperCamelCase       				='french fries'
         __UpperCamelCase       				=sd_pipe(**A_   ,  negative_prompt=A_       )
         __UpperCamelCase       				=output.images
         __UpperCamelCase       				=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         __UpperCamelCase       				=np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]       )

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






    def _a   (     self       )    ->			List[Any]:
         __UpperCamelCase       				='cpu'  # ensure determinism for the device-dependent torch.Generator
         __UpperCamelCase       				=self.get_dummy_components()
         __UpperCamelCase       				=EulerAncestralDiscreteScheduler(
             beta_start=0.0_0085   ,  beta_end=0.012   ,  beta_schedule='scaled_linear'       )
         __UpperCamelCase       				=StableDiffusionModelEditingPipeline(**A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=self.get_dummy_inputs(A_       )
         __UpperCamelCase       				=sd_pipe(**A_       ).images
         __UpperCamelCase       				=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         __UpperCamelCase       				=np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]       )

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






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				='cpu'  # ensure determinism for the device-dependent torch.Generator
         __UpperCamelCase       				=self.get_dummy_components()
         __UpperCamelCase       				=PNDMScheduler()
         __UpperCamelCase       				=StableDiffusionModelEditingPipeline(**A_       )
         __UpperCamelCase       				=sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=self.get_dummy_inputs(A_       )
         # the pipeline does not expect pndm so test if it raises error.
         with self.assertRaises(A_       ):
              __UpperCamelCase       				=sd_pipe(**A_       ).images






    def _a   (     self       )    ->			List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=5E-3       )






    def _a   (     self       )    ->			List[str]:
         super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )






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



    """simple docstring"""






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






    def _a   (     self   ,  A_=0       )    ->			Any:
         __UpperCamelCase       				=torch.manual_seed(A_       )
         __UpperCamelCase       				={
             'prompt': 'A field of roses',
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs






    def _a   (     self       )    ->			Union[str, Any]:
         __UpperCamelCase       				='CompVis/stable-diffusion-v1-4'
         __UpperCamelCase       				=StableDiffusionModelEditingPipeline.from_pretrained(A_   ,  safety_checker=A_       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         __UpperCamelCase       				=self.get_inputs()
         __UpperCamelCase       				=pipe(**A_       ).images
         __UpperCamelCase       				=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)

         __UpperCamelCase       				=np.array(
             [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]       )

         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       				=pipe(**A_       ).images
         __UpperCamelCase       				=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)

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






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

         __UpperCamelCase       				='CompVis/stable-diffusion-v1-4'
         __UpperCamelCase       				=DDIMScheduler.from_pretrained(A_   ,  subfolder='scheduler'       )
         __UpperCamelCase       				=StableDiffusionModelEditingPipeline.from_pretrained(
             A_   ,  scheduler=A_   ,  safety_checker=A_       )
         __UpperCamelCase       				=pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         __UpperCamelCase       				=self.get_inputs()
         __UpperCamelCase       				=pipe(**A_       )

         __UpperCamelCase       				=torch.cuda.max_memory_allocated()
         # make sure that less than 4.4 GB is allocated
         assert mem_bytes < 4.4 * 10**9









_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





