
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class   __lowerCAmelCase		(		lowerCAmelCase_       ,       unittest.TestCase					):



      """simple docstring"""







      A__		:	Tuple         =			UnCLIPImageVariationPipeline
      A__		:	Any         =			IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
      A__		:	Optional[int]         =			IMAGE_VARIATION_BATCH_PARAMS

      A__		:	Tuple         =			[
          '''generator''',
          '''return_dict''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]
      A__		:	Optional[int]         =			False




      @property
      def 						snake_case_	(					self						:				Union[str, Any] ):
          return 32




      @property
      def 						snake_case_	(					self						:				Optional[Any] ):
          return 32




      @property
      def 						snake_case_	(					self						:				Any ):
          return self.time_input_dim




      @property
      def 						snake_case_	(					self						:				str ):
          return self.time_input_dim * 4




      @property
      def 						snake_case_	(					self						:				int ):
          return 100




      @property
      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	List[Any]					=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
          return tokenizer




      @property
      def 						snake_case_	(					self						:				int ):
          torch.manual_seed(0 )
          __lowercase						:	List[Any]					=				CLIPTextConfig(
              bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=self.text_embedder_hidden_size ,				projection_dim=self.text_embedder_hidden_size ,				intermediate_size=37 ,				layer_norm_eps=1E-0_5 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1000 ,				)
          return CLIPTextModelWithProjection(__snake_case )




      @property
      def 						snake_case_	(					self						:				str ):
          torch.manual_seed(0 )
          __lowercase						:	List[Any]					=				CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size ,				projection_dim=self.text_embedder_hidden_size ,				num_hidden_layers=5 ,				num_attention_heads=4 ,				image_size=32 ,				intermediate_size=37 ,				patch_size=1 ,				)
          return CLIPVisionModelWithProjection(__snake_case )




      @property
      def 						snake_case_	(					self						:				Tuple ):
          torch.manual_seed(0 )

          __lowercase						:	Optional[Any]					=				{
              '''clip_embeddings_dim''': self.text_embedder_hidden_size,
              '''time_embed_dim''': self.time_embed_dim,
              '''cross_attention_dim''': self.cross_attention_dim,
          }

          __lowercase						:	List[Any]					=				UnCLIPTextProjModel(**__snake_case )
          return model




      @property
      def 						snake_case_	(					self						:				Optional[int] ):
          torch.manual_seed(0 )

          __lowercase						:	Any					=				{
              '''sample_size''': 32,
              # RGB in channels
              '''in_channels''': 3,
              # Out channels is double in channels because predicts mean and variance
              '''out_channels''': 6,
              '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
              '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
              '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
              '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
              '''layers_per_block''': 1,
              '''cross_attention_dim''': self.cross_attention_dim,
              '''attention_head_dim''': 4,
              '''resnet_time_scale_shift''': '''scale_shift''',
              '''class_embed_type''': '''identity''',
          }

          __lowercase						:	Optional[int]					=				UNetaDConditionModel(**__snake_case )
          return model




      @property
      def 						snake_case_	(					self						:				Any ):
          return {
              "sample_size": 64,
              "layers_per_block": 1,
              "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
              "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "in_channels": 6,
              "out_channels": 3,
          }




      @property
      def 						snake_case_	(					self						:				int ):
          torch.manual_seed(0 )

          __lowercase						:	str					=				UNetaDModel(**self.dummy_super_res_kwargs )
          return model




      @property
      def 						snake_case_	(					self						:				Any ):
          torch.manual_seed(1 )

          __lowercase						:	Tuple					=				UNetaDModel(**self.dummy_super_res_kwargs )
          return model




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	int					=				self.dummy_decoder
          __lowercase						:	List[str]					=				self.dummy_text_proj
          __lowercase						:	str					=				self.dummy_text_encoder
          __lowercase						:	str					=				self.dummy_tokenizer
          __lowercase						:	Optional[int]					=				self.dummy_super_res_first
          __lowercase						:	Optional[Any]					=				self.dummy_super_res_last

          __lowercase						:	int					=				UnCLIPScheduler(
              variance_type='''learned_range''' ,				prediction_type='''epsilon''' ,				num_train_timesteps=1000 ,				)

          __lowercase						:	Optional[int]					=				UnCLIPScheduler(
              variance_type='''fixed_small_log''' ,				prediction_type='''epsilon''' ,				num_train_timesteps=1000 ,				)

          __lowercase						:	int					=				CLIPImageProcessor(crop_size=32 ,				size=32 )

          __lowercase						:	str					=				self.dummy_image_encoder

          return {
              "decoder": decoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_proj": text_proj,
              "feature_extractor": feature_extractor,
              "image_encoder": image_encoder,
              "super_res_first": super_res_first,
              "super_res_last": super_res_last,
              "decoder_scheduler": decoder_scheduler,
              "super_res_scheduler": super_res_scheduler,
          }




      def 						snake_case_	(					self						:				Dict ,				_snake_case						:				Optional[Any] ,				_snake_case						:				Any=0 ,				_snake_case						:				List[Any]=True ):
          __lowercase						:	Optional[Any]					=				floats_tensor((1, 3, 32, 32) ,				rng=random.Random(__snake_case ) ).to(__snake_case )
          if str(__snake_case ).startswith('''mps''' ):
              __lowercase						:	Any					=				torch.manual_seed(__snake_case )
          else:
              __lowercase						:	Union[str, Any]					=				torch.Generator(device=__snake_case ).manual_seed(__snake_case )

          if pil_image:
              __lowercase						:	Dict					=				input_image * 0.5 + 0.5
              __lowercase						:	Union[str, Any]					=				input_image.clamp(0 ,				1 )
              __lowercase						:	List[Any]					=				input_image.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
              __lowercase						:	Any					=				DiffusionPipeline.numpy_to_pil(__snake_case )[0]

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }




      def 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	Union[str, Any]					=				'''cpu'''

          __lowercase						:	List[Any]					=				self.get_dummy_components()

          __lowercase						:	int					=				self.pipeline_class(**__snake_case )
          __lowercase						:	Union[str, Any]					=				pipe.to(__snake_case )

          pipe.set_progress_bar_config(disable=__snake_case )

          __lowercase						:	Dict					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )

          __lowercase						:	Any					=				pipe(**__snake_case )
          __lowercase						:	Optional[Any]					=				output.images

          __lowercase						:	Optional[int]					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )

          __lowercase						:	Optional[int]					=				pipe(
              **__snake_case ,				return_dict=__snake_case ,				)[0]

          __lowercase						:	Optional[int]					=				image[0, -3:, -3:, -1]
          __lowercase						:	str					=				image_from_tuple[0, -3:, -3:, -1]

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

          __lowercase						:	Union[str, Any]					=				np.array(
              [
                  0.99_97,
                  0.00_02,
                  0.99_97,
                  0.99_97,
                  0.99_69,
                  0.00_23,
                  0.99_97,
                  0.99_69,
                  0.99_70,
              ] )

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




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	int					=				'''cpu'''

          __lowercase						:	Optional[int]					=				self.get_dummy_components()

          __lowercase						:	Optional[int]					=				self.pipeline_class(**__snake_case )
          __lowercase						:	List[Any]					=				pipe.to(__snake_case )

          pipe.set_progress_bar_config(disable=__snake_case )

          __lowercase						:	Any					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )

          __lowercase						:	Tuple					=				pipe(**__snake_case )
          __lowercase						:	int					=				output.images

          __lowercase						:	str					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )

          __lowercase						:	List[Any]					=				pipe(
              **__snake_case ,				return_dict=__snake_case ,				)[0]

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

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

          __lowercase						:	int					=				np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71] )

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




      def 						snake_case_	(					self						:				Union[str, Any] ):
          __lowercase						:	Dict					=				'''cpu'''

          __lowercase						:	Dict					=				self.get_dummy_components()

          __lowercase						:	Dict					=				self.pipeline_class(**__snake_case )
          __lowercase						:	List[Any]					=				pipe.to(__snake_case )

          pipe.set_progress_bar_config(disable=__snake_case )

          __lowercase						:	List[str]					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )
          __lowercase						:	Any					=				[
              pipeline_inputs['''image'''],
              pipeline_inputs['''image'''],
          ]

          __lowercase						:	List[Any]					=				pipe(**__snake_case )
          __lowercase						:	Dict					=				output.images

          __lowercase						:	Tuple					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )
          __lowercase						:	List[str]					=				[
              tuple_pipeline_inputs['''image'''],
              tuple_pipeline_inputs['''image'''],
          ]

          __lowercase						:	Any					=				pipe(
              **__snake_case ,				return_dict=__snake_case ,				)[0]

          __lowercase						:	Optional[Any]					=				image[0, -3:, -3:, -1]
          __lowercase						:	int					=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (2, 64, 64, 3)

          __lowercase						:	Union[str, Any]					=				np.array(
              [
                  0.99_97,
                  0.99_89,
                  0.00_08,
                  0.00_21,
                  0.99_60,
                  0.00_18,
                  0.00_14,
                  0.00_02,
                  0.99_33,
              ] )

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




      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	str					=				torch.device('''cpu''' )


          class   __lowerCAmelCase		:



                """simple docstring"""







                A__		:	Tuple         =			1






          __lowercase						:	Any					=				self.get_dummy_components()

          __lowercase						:	List[Any]					=				self.pipeline_class(**__snake_case )
          __lowercase						:	Union[str, Any]					=				pipe.to(__snake_case )

          pipe.set_progress_bar_config(disable=__snake_case )

          __lowercase						:	Tuple					=				torch.Generator(device=__snake_case ).manual_seed(0 )
          __lowercase						:	List[str]					=				pipe.decoder.dtype
          __lowercase						:	Dict					=				1

          __lowercase						:	Any					=				(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          __lowercase						:	Optional[int]					=				pipe.prepare_latents(
              __snake_case ,				dtype=__snake_case ,				device=__snake_case ,				generator=__snake_case ,				latents=__snake_case ,				scheduler=DummyScheduler() )

          __lowercase						:	Optional[Any]					=				(
              batch_size,
              pipe.super_res_first.config.in_channels // 2,
              pipe.super_res_first.config.sample_size,
              pipe.super_res_first.config.sample_size,
          )
          __lowercase						:	List[Any]					=				pipe.prepare_latents(
              __snake_case ,				dtype=__snake_case ,				device=__snake_case ,				generator=__snake_case ,				latents=__snake_case ,				scheduler=DummyScheduler() )

          __lowercase						:	List[str]					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )

          __lowercase						:	Tuple					=				pipe(
              **__snake_case ,				decoder_latents=__snake_case ,				super_res_latents=__snake_case ).images

          __lowercase						:	Tuple					=				self.get_dummy_inputs(__snake_case ,				pil_image=__snake_case )
          # Don't pass image, instead pass embedding
          __lowercase						:	Any					=				pipeline_inputs.pop('''image''' )
          __lowercase						:	Optional[int]					=				pipe.image_encoder(__snake_case ).image_embeds

          __lowercase						:	Any					=				pipe(
              **__snake_case ,				decoder_latents=__snake_case ,				super_res_latents=__snake_case ,				image_embeddings=__snake_case ,				).images

          # make sure passing text embeddings manually is identical
          assert np.abs(img_out_a - img_out_a ).max() < 1E-4




      @skip_mps
      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	Optional[int]					=				torch_device == '''cpu'''

          # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
          __lowercase						:	Optional[int]					=				1E-2

          self._test_attention_slicing_forward_pass(
              test_max_difference=__snake_case ,				expected_max_diff=__snake_case )




      @skip_mps
      def 						snake_case_	(					self						:				str ):
          __lowercase						:	List[str]					=				torch_device == '''cpu'''
          __lowercase						:	Dict					=				True
          __lowercase						:	str					=				[
              '''decoder_num_inference_steps''',
              '''super_res_num_inference_steps''',
          ]

          self._test_inference_batch_single_identical(
              test_max_difference=__snake_case ,				relax_max_difference=__snake_case ,				additional_params_copy_to_batched_inputs=__snake_case ,				)




      def 						snake_case_	(					self						:				int ):
          __lowercase						:	Any					=				[
              '''decoder_num_inference_steps''',
              '''super_res_num_inference_steps''',
          ]

          if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              __lowercase						:	Optional[int]					=				[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=__snake_case ,				additional_params_copy_to_batched_inputs=__snake_case ,				)
          else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=__snake_case )




      @skip_mps
      def 						snake_case_	(					self						:				Optional[int] ):
          return super().test_dict_tuple_outputs_equivalent()




      @skip_mps
      def 						snake_case_	(					self						:				Optional[int] ):
          return super().test_save_load_local()




      @skip_mps
      def 						snake_case_	(					self						:				Any ):
          return super().test_save_load_optional_components()






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



      """simple docstring"""







      def 						snake_case_	(					self						:				Union[str, Any] ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	Union[str, Any]					=				load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''' )
          __lowercase						:	Union[str, Any]					=				load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''' )

          __lowercase						:	List[Any]					=				UnCLIPImageVariationPipeline.from_pretrained(
              '''kakaobrain/karlo-v1-alpha-image-variations''' ,				torch_dtype=torch.floataa )
          __lowercase						:	List[Any]					=				pipeline.to(__snake_case )
          pipeline.set_progress_bar_config(disable=__snake_case )

          __lowercase						:	Optional[Any]					=				torch.Generator(device='''cpu''' ).manual_seed(0 )
          __lowercase						:	Optional[Any]					=				pipeline(
              __snake_case ,				generator=__snake_case ,				output_type='''np''' ,				)

          __lowercase						:	List[Any]					=				output.images[0]

          assert image.shape == (256, 256, 3)

          assert_mean_pixel_difference(__snake_case ,				__snake_case ,				15 )





# 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        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

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

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

			return True


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


print(check_bipartite_dfs(graph))






