


import gc
import unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	a__						(    lowerCAmelCase_    ,							lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  Tuple				     =  StableDiffusionXLPipeline
       lowerCamelCase__:  Dict				     =  TEXT_TO_IMAGE_PARAMS
       lowerCamelCase__:  str				     =  TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCamelCase__:  Tuple				     =  TEXT_TO_IMAGE_IMAGE_PARAMS
       lowerCamelCase__:  Optional[Any]				     =  TEXT_TO_IMAGE_IMAGE_PARAMS

       def     UpperCAmelCase( self   :  Any     ):
            torch.manual_seed(0     )
            a_     :  Tuple				      =     UNetaDConditionModel(
                block_out_channels=(3_2, 6_4)   ,				layers_per_block=2   ,				sample_size=3_2   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,				attention_head_dim=(2, 4)   ,				use_linear_projection=lowerCamelCase_   ,				addition_embed_type="""text_time"""   ,				addition_time_embed_dim=8   ,				transformer_layers_per_block=(1, 2)   ,				projection_class_embeddings_input_dim=8_0   ,				cross_attention_dim=6_4   ,				)
            a_     :  Optional[Any]				      =     EulerDiscreteScheduler(
                beta_start=0.0_0_0_8_5   ,				beta_end=0.0_1_2   ,				steps_offset=1   ,				beta_schedule="""scaled_linear"""   ,				timestep_spacing="""leading"""   ,				)
            torch.manual_seed(0     )
            a_     :  int				      =     AutoencoderKL(
                block_out_channels=[3_2, 6_4]   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,				latent_channels=4   ,				sample_size=1_2_8   ,				)
            torch.manual_seed(0     )
            a_     :  Optional[int]				      =     CLIPTextConfig(
                bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=3_2   ,				intermediate_size=3_7   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1_0_0_0   ,				hidden_act="""gelu"""   ,				projection_dim=3_2   ,				)
            a_     :  Dict				      =     CLIPTextModel(lowerCamelCase_     )
            a_     :  Optional[int]				      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,				local_files_only=lowerCamelCase_     )

            a_     :  Dict				      =     CLIPTextModelWithProjection(lowerCamelCase_     )
            a_     :  Dict				      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,				local_files_only=lowerCamelCase_     )

            a_     :  Dict				      =     {
                """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     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[Any]=0     ):
            if str(lowerCamelCase_     ).startswith("""mps"""     ):
                 a_     :  Dict				      =     torch.manual_seed(lowerCamelCase_     )
            else:
                 a_     :  int				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     {
                """prompt""": """A painting of a squirrel eating a burger""",
                """generator""": generator,
                """num_inference_steps""": 2,
                """guidance_scale""": 5.0,
                """output_type""": """numpy""",
            }
            return inputs

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  Optional[Any]				      =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
            a_     :  Union[str, Any]				      =     self.get_dummy_components()
            a_     :  Tuple				      =     StableDiffusionXLPipeline(**lowerCamelCase_     )
            a_     :  List[Any]				      =     sd_pipe.to(lowerCamelCase_     )
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  Tuple				      =     self.get_dummy_inputs(lowerCamelCase_     )
            a_     :  Dict				      =     sd_pipe(**lowerCamelCase_     ).images
            a_     :  Dict				      =     image[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)
            a_     :  List[Any]				      =     np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]     )

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

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  Optional[int]				      =     self.get_dummy_components()
            a_     :  Dict				      =     StableDiffusionXLPipeline(**lowerCamelCase_     )
            a_     :  int				      =     sd_pipe.to(lowerCamelCase_     )
            a_     :  Dict				      =     sd_pipe.to(lowerCamelCase_     )
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            # forward without prompt embeds
            a_     :  List[str]				      =     self.get_dummy_inputs(lowerCamelCase_     )
            a_     :  Any				      =     3 * ["""this is a negative prompt"""]
            a_     :  Any				      =     negative_prompt
            a_     :  Union[str, Any]				      =     3 * [inputs["""prompt"""]]

            a_     :  List[str]				      =     sd_pipe(**lowerCamelCase_     )
            a_     :  Tuple				      =     output.images[0, -3:, -3:, -1]

            # forward with prompt embeds
            a_     :  List[str]				      =     self.get_dummy_inputs(lowerCamelCase_     )
            a_     :  Any				      =     3 * ["""this is a negative prompt"""]
            a_     :  List[Any]				      =     3 * [inputs.pop("""prompt"""     )]

            (
                (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      
            )     :  int				      =     sd_pipe.encode_prompt(lowerCamelCase_   ,				negative_prompt=lowerCamelCase_     )

            a_     :  Any				      =     sd_pipe(
                **lowerCamelCase_   ,				prompt_embeds=lowerCamelCase_   ,				negative_prompt_embeds=lowerCamelCase_   ,				pooled_prompt_embeds=lowerCamelCase_   ,				negative_pooled_prompt_embeds=lowerCamelCase_   ,				)
            a_     :  Dict				      =     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

       def     UpperCAmelCase( self   :  str     ):
            super().test_attention_slicing_forward_pass(expected_max_diff=3E-3     )

       def     UpperCAmelCase( self   :  Tuple     ):
            super().test_inference_batch_single_identical(expected_max_diff=3E-3     )


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

       def     UpperCAmelCase( self   :  int     ):
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  List[str]="cpu"   ,				lowerCamelCase_   :  List[str]=torch.floataa   ,				lowerCamelCase_   :  int=0     ):
            a_     :  str				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
            a_     :  Dict				      =     np.random.RandomState(lowerCamelCase_     ).standard_normal((1, 4, 6_4, 6_4)     )
            a_     :  Dict				      =     torch.from_numpy(lowerCamelCase_     ).to(device=lowerCamelCase_   ,				dtype=lowerCamelCase_     )
            a_     :  int				      =     {
                """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     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  List[Any]				      =     DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""     )
            pipe.to(lowerCamelCase_     )
            pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  Dict				      =     self.get_inputs(lowerCamelCase_     )
            a_     :  Any				      =     pipe(**lowerCamelCase_     ).images
            a_     :  Any				      =     image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_     :  str				      =     np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]     )
            assert np.abs(image_slice - expected_slice     ).max() < 7E-3







__lowerCamelCase   	=  {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase   	=  {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Tuple				      =     True
     a_     :  Optional[Any]				      =     []

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               connected_verts += dfs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)

     return [vert, *connected_verts]



def 			_a      (    __UpperCamelCase			):
     a_     :  Dict				      =     len(__UpperCamelCase			)
     a_     :  List[str]				      =     graph_size * [False]
     a_     :  Dict				      =     []

     for i in range(__UpperCamelCase			):
          if not visited[i]:
               a_     :  str				      =     dfs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
               components_list.append(__UpperCamelCase			)

     return components_list


if __name__ == "__main__":
       import doctest

       doctest.testmod()



