




'''simple docstring'''



from typing import Any






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						data
             SCREAMING_SNAKE_CASE   :      List[str]    					=						None






class 							_a				:





       '''simple docstring'''






       def __init__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						None







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.head
             while temp is not None:
                   print(temp.data,							end=' '  )
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						temp.next
             print()







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						Node(A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.head
             SCREAMING_SNAKE_CASE   :      Tuple    					=						new_node







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             if node_data_a == node_data_a:
                   return
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.head
                   while node_a is not None and node_a.data != node_data_a:
                         SCREAMING_SNAKE_CASE   :      Any    					=						node_a.next

                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.head
                   while node_a is not None and node_a.data != node_data_a:
                         SCREAMING_SNAKE_CASE   :      Dict    					=						node_a.next

                   if node_a is None or node_a is None:
                         return

                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						node_a.data, node_a.data


if __name__ == "__main__":
      UpperCamelCase_    	=				LinkedList()
      for i in range(5, 0, -1):
            ll.push(i)

      ll.print_list()

      ll.swap_nodes(1, 4)
      print("After swapping")
      ll.print_list()








'''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 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Union[str, Any]        =						StableDiffusionXLImgaImgPipeline
       A						:				Any        =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       A						:				str        =						PipelineTesterMixin.required_optional_params - {'''latents'''}
       A						:				List[str]        =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       A						:				Dict        =						IMAGE_TO_IMAGE_IMAGE_PARAMS
       A						:				int        =						IMAGE_TO_IMAGE_IMAGE_PARAMS







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      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=A,							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,							)
             SCREAMING_SNAKE_CASE   :      str    					=						EulerDiscreteScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							steps_offset=1,							beta_schedule='scaled_linear',							timestep_spacing='leading',							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      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  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						CLIPTextConfig(
                 bos_token_id=0,							eos_token_id=2,							hidden_size=32,							intermediate_size=37,							layer_norm_eps=1E-05,							num_attention_heads=4,							num_hidden_layers=5,							pad_token_id=1,							vocab_size=1_000,							hidden_act='gelu',							projection_dim=32,							)
             SCREAMING_SNAKE_CASE   :      int    					=						CLIPTextModel(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip',							local_files_only=A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						CLIPTextModelWithProjection(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip',							local_files_only=A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						{
                 '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,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						image / 2 + 0.5
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{
                 '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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						'cpu'  # ensure determinism for the device-dependent torch.Generator
             SCREAMING_SNAKE_CASE   :      str    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						StableDiffusionXLImgaImgPipeline(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						sd_pipe.to(A  )
             sd_pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						sd_pipe(**A  ).images
             SCREAMING_SNAKE_CASE   :      Dict    					=						image[0, -3:, -3:, -1]

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

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]  )

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						StableDiffusionXLImgaImgPipeline(**A  )
             SCREAMING_SNAKE_CASE   :      str    					=						sd_pipe.to(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						sd_pipe.to(A  )
             sd_pipe.set_progress_bar_config(disable=A  )

             # forward without prompt embeds
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						3 * ['this is a negative prompt']
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						negative_prompt
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						3 * [inputs['prompt']]

             SCREAMING_SNAKE_CASE   :      int    					=						sd_pipe(**A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						output.images[0, -3:, -3:, -1]

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

             (
                 (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    
             )   :      Optional[Any]    					=						sd_pipe.encode_prompt(A,							negative_prompt=A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						sd_pipe(
                 **A,							prompt_embeds=A,							negative_prompt_embeds=A,							pooled_prompt_embeds=A,							negative_pooled_prompt_embeds=A,							)
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A="cpu",							A=torch.floataa,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.random.RandomState(A  ).standard_normal((1, 4, 64, 64)  )
             SCREAMING_SNAKE_CASE   :      str    					=						torch.from_numpy(A  ).to(device=A,							dtype=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, 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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_inputs(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						pipe(**A  ).images
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]  )
             assert np.abs(image_slice - expected_slice  ).max() < 7E-3


