


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()




class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  List[str]      =StableUnCLIPImgaImgPipeline
      UpperCamelCase__							:  Optional[Any]      =TEXT_GUIDED_IMAGE_VARIATION_PARAMS
      UpperCamelCase__							:  Optional[Any]      =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase__							:  Any      =frozenset(
          []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCamelCase__							:  Optional[int]      =frozenset([]     )






      def        lowerCamelCase       (					self						:     str )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		int		=32
             _lowerCamelCase       :		Optional[int]		=embedder_hidden_size

             # image encoding components

             _lowerCamelCase       :		Any		=CLIPImageProcessor(crop_size=32			,   size=32 )

             torch.manual_seed(0 )
             _lowerCamelCase       :		int		=CLIPVisionModelWithProjection(
                 CLIPVisionConfig(
                     hidden_size=lowercase_			,   projection_dim=lowercase_			,   num_hidden_layers=5			,   num_attention_heads=4			,   image_size=32			,   intermediate_size=37			,   patch_size=1			,   ) )

             # regular denoising components

             torch.manual_seed(0 )
             _lowerCamelCase       :		List[Any]		=StableUnCLIPImageNormalizer(embedding_dim=lowercase_ )
             _lowerCamelCase       :		Tuple		=DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

             torch.manual_seed(0 )
             _lowerCamelCase       :		str		=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

             torch.manual_seed(0 )
             _lowerCamelCase       :		Optional[Any]		=CLIPTextModel(
                 CLIPTextConfig(
                     bos_token_id=0			,   eos_token_id=2			,   hidden_size=lowercase_			,   projection_dim=32			,   intermediate_size=37			,   layer_norm_eps=1E-05			,   num_attention_heads=4			,   num_hidden_layers=5			,   pad_token_id=1			,   vocab_size=1000			,   ) )

             torch.manual_seed(0 )
             _lowerCamelCase       :		List[str]		=UNetaDConditionModel(
                 sample_size=32			,   in_channels=4			,   out_channels=4			,   down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')			,   up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')			,   block_out_channels=(32, 64)			,   attention_head_dim=(2, 4)			,   class_embed_type='projection'			,   projection_class_embeddings_input_dim=embedder_projection_dim * 2			,   cross_attention_dim=lowercase_			,   layers_per_block=1			,   upcast_attention=lowercase_			,   use_linear_projection=lowercase_			,   )

             torch.manual_seed(0 )
             _lowerCamelCase       :		Union[str, Any]		=DDIMScheduler(
                 beta_schedule='scaled_linear'			,   beta_start=0.00085			,   beta_end=0.012			,   prediction_type='v_prediction'			,   set_alpha_to_one=lowercase_			,   steps_offset=1			,   )

             torch.manual_seed(0 )
             _lowerCamelCase       :		Optional[Any]		=AutoencoderKL()

             _lowerCamelCase       :		Optional[int]		={
                 # image encoding components
                 'feature_extractor': feature_extractor,
                 'image_encoder': image_encoder.eval(),
                 # image noising components
                 'image_normalizer': image_normalizer.eval(),
                 'image_noising_scheduler': image_noising_scheduler,
                 # regular denoising components
                 'tokenizer': tokenizer,
                 'text_encoder': text_encoder.eval(),
                 'unet': unet.eval(),
                 'scheduler': scheduler,
                 'vae': vae.eval(),
             }

             return components






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     Optional[int]			,   lowercase_						:     Dict=0			,   lowercase_						:     Optional[int]=True )					->						List[str]:
             """simple docstring"""




             if str(lowercase_ ).startswith('mps' ):
                    _lowerCamelCase       :		List[str]		=torch.manual_seed(lowercase_ )
             else:
                    _lowerCamelCase       :		List[str]		=torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(lowercase_ ) ).to(lowercase_ )

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

             return {
                 "prompt": "An anime racoon running a marathon",
                 "image": input_image,
                 "generator": generator,
                 "num_inference_steps": 2,
                 "output_type": "np",
             }






      @skip_mps
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		str		='cpu'  # ensure determinism for the device-dependent torch.Generator
             _lowerCamelCase       :		Tuple		=self.get_dummy_components()
             _lowerCamelCase       :		str		=StableUnCLIPImgaImgPipeline(**lowercase_ )
             _lowerCamelCase       :		int		=sd_pipe.to(lowercase_ )
             sd_pipe.set_progress_bar_config(disable=lowercase_ )

             _lowerCamelCase       :		List[str]		=self.get_dummy_inputs(lowercase_ )
             inputs.update({'image_embeds': None} )
             _lowerCamelCase       :		Optional[Any]		=sd_pipe(**lowercase_ ).images
             _lowerCamelCase       :		int		=image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             _lowerCamelCase       :		Dict		=np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )

             assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=torch_device in ['cpu', 'mps']

             self._test_attention_slicing_forward_pass(test_max_difference=lowercase_ )






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=torch_device in ['cpu', 'mps']

             self._test_inference_batch_single_identical(test_max_difference=lowercase_ )






      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()			,   reason='XFormers attention is only available with CUDA and `xformers` installed'			,   )
      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase_ )




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






      def        lowerCamelCase       (					self						:     str )					->						int:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png' )

             _lowerCamelCase       :		List[Any]		=load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy' )

             _lowerCamelCase       :		List[Any]		=StableUnCLIPImgaImgPipeline.from_pretrained(
                 'fusing/stable-unclip-2-1-l-img2img'			,   torch_dtype=torch.floataa )
             pipe.to(lowercase_ )
             pipe.set_progress_bar_config(disable=lowercase_ )
             # stable unclip will oom when integration tests are run on a V100,
             # so turn on memory savings
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()

             _lowerCamelCase       :		Dict		=torch.Generator(device='cpu' ).manual_seed(0 )
             _lowerCamelCase       :		List[Any]		=pipe(lowercase_			,   'anime turle'			,   generator=lowercase_			,   output_type='np' )

             _lowerCamelCase       :		Dict		=output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png' )

             _lowerCamelCase       :		Dict		=load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy' )

             _lowerCamelCase       :		int		=StableUnCLIPImgaImgPipeline.from_pretrained(
                 'fusing/stable-unclip-2-1-h-img2img'			,   torch_dtype=torch.floataa )
             pipe.to(lowercase_ )
             pipe.set_progress_bar_config(disable=lowercase_ )
             # stable unclip will oom when integration tests are run on a V100,
             # so turn on memory savings
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()

             _lowerCamelCase       :		Optional[Any]		=torch.Generator(device='cpu' ).manual_seed(0 )
             _lowerCamelCase       :		List[Any]		=pipe(lowercase_			,   'anime turle'			,   generator=lowercase_			,   output_type='np' )

             _lowerCamelCase       :		Optional[int]		=output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     str )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png' )

             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             _lowerCamelCase       :		List[Any]		=StableUnCLIPImgaImgPipeline.from_pretrained(
                 'fusing/stable-unclip-2-1-h-img2img'			,   torch_dtype=torch.floataa )
             _lowerCamelCase       :		str		=pipe.to(lowercase_ )
             pipe.set_progress_bar_config(disable=lowercase_ )
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()

             _lowerCamelCase       :		Optional[Any]		=pipe(
                 lowercase_			,   'anime turtle'			,   num_inference_steps=2			,   output_type='np'			,   )

             _lowerCamelCase       :		Tuple		=torch.cuda.max_memory_allocated()
             # make sure that less than 7 GB is allocated
             assert mem_bytes < 7 * 10**9






# using dfs for finding eulerian path traversal




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


