



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Tuple:

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			List[str])				->				Dict:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					1
    _snake_case    :  List[Any]					=					3
    _snake_case    :  Optional[Any]					=					(32, 32)

    _snake_case    :  str					=					floats_tensor((batch_size, num_channels) + sizes				,		rng=random.Random(0)).to(lowerCAmelCase)
    return image

   @property
   def      UpperCamelCase_  (					self							:			int)				->				Optional[Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  List[str]					=					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				,		)
    return model

   @property
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[str]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  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				,		)
    return model

   @property
   def      UpperCamelCase_  (					self							:			int)				->				Optional[int]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  Any					=					RobertaSeriesConfig(
        hidden_size=32				,		project_dim=32				,		intermediate_size=37				,		layer_norm_eps=1E-05				,		num_attention_heads=4				,		num_hidden_layers=5				,		pad_token_id=1				,		vocab_size=5006				,		)
    return RobertaSeriesModelWithTransformation(lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Tuple)				->				Optional[Any]:

    """simple docstring"""







    def extract(*lowerCAmelCase							:			Union[str, Any]				,		**lowerCAmelCase							:			Union[str, Any]):







     class 		snake_case						:



        '''simple docstring'''






        def __init__(					self							:			int)				->				Union[str, Any]:

         """simple docstring"""







         _snake_case    :  Optional[Any]					=					torch.ones([0])


        def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			int)				->				int:

         """simple docstring"""







         self.pixel_values.to(lowerCAmelCase)
         return self
     return Out()

    return extract

   def      UpperCamelCase_  (					self							:			Tuple)				->				int:

    """simple docstring"""







    _snake_case    :  Tuple					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    _snake_case    :  Any					=					self.dummy_cond_unet
    _snake_case    :  Optional[Any]					=					PNDMScheduler(skip_prk_steps=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					self.dummy_vae
    _snake_case    :  List[Any]					=					self.dummy_text_encoder
    _snake_case    :  Union[str, Any]					=					XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""")
    _snake_case    :  Union[str, Any]					=					77

    _snake_case    :  str					=					self.dummy_image.to(lowerCAmelCase)
    _snake_case    :  str					=					init_image / 2 + 0.5

    # make sure here that pndm scheduler skips prk
    _snake_case    :  int					=					AltDiffusionImgaImgPipeline(
        unet=lowerCAmelCase				,		scheduler=lowerCAmelCase				,		vae=lowerCAmelCase				,		text_encoder=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		safety_checker=lowerCAmelCase				,		feature_extractor=self.dummy_extractor				,		)
    _snake_case    :  int					=					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,		do_normalize=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					alt_pipe.to(lowerCAmelCase)
    alt_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Tuple					=					"""A painting of a squirrel eating a burger"""
    _snake_case    :  Optional[Any]					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  Dict					=					alt_pipe(
        [prompt]				,		generator=lowerCAmelCase				,		guidance_scale=6.0				,		num_inference_steps=2				,		output_type="""np"""				,		image=lowerCAmelCase				,		)

    _snake_case    :  Any					=					output.images

    _snake_case    :  Dict					=					torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case    :  str					=					alt_pipe(
        [prompt]				,		generator=lowerCAmelCase				,		guidance_scale=6.0				,		num_inference_steps=2				,		output_type="""np"""				,		image=lowerCAmelCase				,		return_dict=lowerCAmelCase				,		)[0]

    _snake_case    :  Union[str, Any]					=					image[0, -3:, -3:, -1]
    _snake_case    :  Any					=					image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case    :  Union[str, Any]					=					np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3

   @unittest.skipIf(torch_device != """cuda"""				,		"""This test requires a GPU""")
   def      UpperCamelCase_  (					self							:			Optional[int])				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					self.dummy_cond_unet
    _snake_case    :  Any					=					PNDMScheduler(skip_prk_steps=lowerCAmelCase)
    _snake_case    :  Dict					=					self.dummy_vae
    _snake_case    :  Tuple					=					self.dummy_text_encoder
    _snake_case    :  str					=					XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""")
    _snake_case    :  int					=					77

    _snake_case    :  List[str]					=					self.dummy_image.to(lowerCAmelCase)

    # put models in fp16
    _snake_case    :  str					=					unet.half()
    _snake_case    :  str					=					vae.half()
    _snake_case    :  Union[str, Any]					=					bert.half()

    # make sure here that pndm scheduler skips prk
    _snake_case    :  Union[str, Any]					=					AltDiffusionImgaImgPipeline(
        unet=lowerCAmelCase				,		scheduler=lowerCAmelCase				,		vae=lowerCAmelCase				,		text_encoder=lowerCAmelCase				,		tokenizer=lowerCAmelCase				,		safety_checker=lowerCAmelCase				,		feature_extractor=self.dummy_extractor				,		)
    _snake_case    :  Dict					=					VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,		do_normalize=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					alt_pipe.to(lowerCAmelCase)
    alt_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Dict					=					"""A painting of a squirrel eating a burger"""
    _snake_case    :  Dict					=					torch.manual_seed(0)
    _snake_case    :  Dict					=					alt_pipe(
        [prompt]				,		generator=lowerCAmelCase				,		num_inference_steps=2				,		output_type="""np"""				,		image=lowerCAmelCase				,		).images

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


   @unittest.skipIf(torch_device != """cuda"""				,		"""This test requires a GPU""")
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/img2img/sketch-mountains-input.jpg""")
    # resize to resolution that is divisible by 8 but not 16 or 32
    _snake_case    :  Dict					=					init_image.resize((760, 504))

    _snake_case    :  Optional[Any]					=					"""BAAI/AltDiffusion"""
    _snake_case    :  Union[str, Any]					=					AltDiffusionImgaImgPipeline.from_pretrained(
        lowerCAmelCase				,		safety_checker=lowerCAmelCase				,		)
    pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)
    pipe.enable_attention_slicing()

    _snake_case    :  List[Any]					=					"""A fantasy landscape, trending on artstation"""

    _snake_case    :  Optional[Any]					=					torch.manual_seed(0)
    _snake_case    :  Union[str, Any]					=					pipe(
        prompt=lowerCAmelCase				,		image=lowerCAmelCase				,		strength=0.75				,		guidance_scale=7.5				,		generator=lowerCAmelCase				,		output_type="""np"""				,		)
    _snake_case    :  Optional[int]					=					output.images[0]

    _snake_case    :  List[Any]					=					image[255:258, 383:386, -1]

    assert image.shape == (504, 760, 3)
    _snake_case    :  Tuple					=					np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000])

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







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



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Tuple)				->				List[Any]:

    """simple docstring"""







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


   def      UpperCamelCase_  (					self							:			List[Any])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/img2img/sketch-mountains-input.jpg""")
    _snake_case    :  Any					=					init_image.resize((768, 512))
    _snake_case    :  Optional[Any]					=					load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""")

    _snake_case    :  Tuple					=					"""BAAI/AltDiffusion"""
    _snake_case    :  Union[str, Any]					=					AltDiffusionImgaImgPipeline.from_pretrained(
        lowerCAmelCase				,		safety_checker=lowerCAmelCase				,		)
    pipe.to(lowerCAmelCase)
    pipe.set_progress_bar_config(disable=lowerCAmelCase)
    pipe.enable_attention_slicing()

    _snake_case    :  str					=					"""A fantasy landscape, trending on artstation"""

    _snake_case    :  List[str]					=					torch.manual_seed(0)
    _snake_case    :  List[Any]					=					pipe(
        prompt=lowerCAmelCase				,		image=lowerCAmelCase				,		strength=0.75				,		guidance_scale=7.5				,		generator=lowerCAmelCase				,		output_type="""np"""				,		)
    _snake_case    :  str					=					output.images[0]

    assert image.shape == (512, 768, 3)
    # img2img is flaky across GPUs even in fp32, so using MAE here
    assert np.abs(expected_image - image).max() < 1E-2





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
