





'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class  SCREAMING_SNAKE_CASE    (a__					,		a__					,		unittest.TestCase							):
     lowerCAmelCase					=       StableDiffusionXLImgaImgPipeline
     lowerCAmelCase					=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     lowerCAmelCase					=       PipelineTesterMixin.required_optional_params - {'''latents'''}
     lowerCAmelCase					=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase					=       IMAGE_TO_IMAGE_IMAGE_PARAMS
     lowerCAmelCase					=       IMAGE_TO_IMAGE_IMAGE_PARAMS


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						: 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')						,	attention_head_dim=(2, 4)						,	use_linear_projection=_UpperCAmelCase						,	addition_embed_type='text_time'						,	addition_time_embed_dim=8						,	transformer_layers_per_block=(1, 2)						,	projection_class_embeddings_input_dim=80						,	cross_attention_dim=64						,	)
          __A						: Dict      =       EulerDiscreteScheduler(
              beta_start=0.00085						,	beta_end=0.012						,	steps_offset=1						,	beta_schedule='scaled_linear'						,	timestep_spacing='leading'						,	)
          torch.manual_seed(0)
          __A						: List[Any]      =       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)
          __A						: str      =       CLIPTextConfig(
              bos_token_id=0						,	eos_token_id=2						,	hidden_size=32						,	intermediate_size=37						,	layer_norm_eps=1e-0_5						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=1000						,	hidden_act='gelu'						,	projection_dim=32						,	)
          __A						: List[str]      =       CLIPTextModel(_UpperCAmelCase)
          __A						: Tuple      =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,	local_files_only=_UpperCAmelCase)

          __A						: List[Any]      =       CLIPTextModelWithProjection(_UpperCAmelCase)
          __A						: Tuple      =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,	local_files_only=_UpperCAmelCase)

          __A						: Optional[Any]      =       {
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'text_encoder_2': text_encoder_a,
              'tokenizer_2': tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase=0):

          '''simple docstring'''






          __A						: Union[str, Any]      =       floats_tensor((1, 3, 32, 32)						,	rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
          __A						: int      =       image / 2 + 0.5
          if str(_UpperCAmelCase).startswith('mps'):
               __A						: int      =       torch.manual_seed(_UpperCAmelCase)
          else:
               __A						: str      =       torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						: List[str]      =       {
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 5.0,
              'output_type': 'numpy',
              'strength': 0.75,
          }
          return inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A						: str      =       self.get_dummy_components()
          __A						: Tuple      =       StableDiffusionXLImgaImgPipeline(**_UpperCAmelCase)
          __A						: Dict      =       sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						: List[str]      =       self.get_dummy_inputs(_UpperCAmelCase)
          __A						: Optional[Any]      =       sd_pipe(**_UpperCAmelCase).images
          __A						: Union[str, Any]      =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          __A						: Dict      =       np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165])

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().test_inference_batch_single_identical(expected_max_diff=3e-3)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.get_dummy_components()
          __A						: Union[str, Any]      =       StableDiffusionXLImgaImgPipeline(**_UpperCAmelCase)
          __A						: str      =       sd_pipe.to(_UpperCAmelCase)
          __A						: Optional[int]      =       sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          # forward without prompt embeds
          __A						: List[Any]      =       self.get_dummy_inputs(_UpperCAmelCase)
          __A						: Dict      =       3 * ['this is a negative prompt']
          __A						: List[str]      =       negative_prompt
          __A						: Dict      =       3 * [inputs['prompt']]

          __A						: Optional[int]      =       sd_pipe(**_UpperCAmelCase)
          __A						: Tuple      =       output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __A						: Union[str, Any]      =       self.get_dummy_inputs(_UpperCAmelCase)
          __A						: int      =       3 * ['this is a negative prompt']
          __A						: List[Any]      =       3 * [inputs.pop('prompt')]

          (
              (
              __A
          ) ,(
              __A
          ) ,(
              __A
          ) ,(
              __A
          ) ,
          )						: str      =       sd_pipe.encode_prompt(_UpperCAmelCase						,	negative_prompt=_UpperCAmelCase)

          __A						: Dict      =       sd_pipe(
              **_UpperCAmelCase						,	prompt_embeds=_UpperCAmelCase						,	negative_prompt_embeds=_UpperCAmelCase						,	pooled_prompt_embeds=_UpperCAmelCase						,	negative_pooled_prompt_embeds=_UpperCAmelCase						,	)
          __A						: str      =       output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4




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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase="cpu"						,	_UpperCAmelCase=torch.floataa						,	_UpperCAmelCase=0):

          '''simple docstring'''






          __A						: Tuple      =       torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						: Any      =       np.random.RandomState(_UpperCAmelCase).standard_normal((1, 4, 64, 64))
          __A						: Union[str, Any]      =       torch.from_numpy(_UpperCAmelCase).to(device=_UpperCAmelCase						,	dtype=_UpperCAmelCase)
          __A						: Optional[Any]      =       {
              'prompt': 'a photograph of an astronaut riding a horse',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

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

          assert image.shape == (1, 512, 512, 3)
          __A						: List[Any]      =       np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
          assert np.abs(image_slice - expected_slice).max() < 7e-3






'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()