
"""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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Any				   =						StableDiffusionXLImgaImgPipeline
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						PipelineTesterMixin.required_optional_params - {"latents"}
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      __SCREAMING_SNAKE_CASE :				Any				   =						IMAGE_TO_IMAGE_IMAGE_PARAMS
      __SCREAMING_SNAKE_CASE :				int				   =						IMAGE_TO_IMAGE_IMAGE_PARAMS




      def      a_   (				self				)    ->       Optional[int]:
          torch.manual_seed(0				)
          UpperCAmelCase          =       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=lowercase_ ,							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 ,							)
          UpperCAmelCase          =       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				)
          UpperCAmelCase          =       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				)
          UpperCAmelCase          =       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 ,							)
          UpperCAmelCase          =       CLIPTextModel(lowercase_				)
          UpperCAmelCase          =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ,							local_files_only=lowercase_				)

          UpperCAmelCase          =       CLIPTextModelWithProjection(lowercase_				)
          UpperCAmelCase          =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ,							local_files_only=lowercase_				)

          UpperCAmelCase          =       {
              '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      a_   (				self ,							lowercase_ ,							lowercase_=0				)    ->       Tuple:
          UpperCAmelCase          =       floats_tensor((1, 3, 3_2, 3_2) ,							rng=random.Random(lowercase_				)				).to(lowercase_				)
          UpperCAmelCase          =       image / 2 + 0.5
          if str(lowercase_				).startswith('mps'				):
              UpperCAmelCase          =       torch.manual_seed(lowercase_				)
          else:
              UpperCAmelCase          =       torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
          UpperCAmelCase          =       {
              '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': 0.7_5,
          }
          return inputs




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       'cpu'  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase          =       self.get_dummy_components()
          UpperCAmelCase          =       StableDiffusionXLImgaImgPipeline(**lowercase_				)
          UpperCAmelCase          =       sd_pipe.to(lowercase_				)
          sd_pipe.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       self.get_dummy_inputs(lowercase_				)
          UpperCAmelCase          =       sd_pipe(**lowercase_				).images
          UpperCAmelCase          =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)

          UpperCAmelCase          =       np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]				)

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




      def      a_   (				self				)    ->       Optional[Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)




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




      def      a_   (				self				)    ->       Optional[int]:
          pass




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.get_dummy_components()
          UpperCAmelCase          =       StableDiffusionXLImgaImgPipeline(**lowercase_				)
          UpperCAmelCase          =       sd_pipe.to(lowercase_				)
          UpperCAmelCase          =       sd_pipe.to(lowercase_				)
          sd_pipe.set_progress_bar_config(disable=lowercase_				)

          # forward without prompt embeds
          UpperCAmelCase          =       self.get_dummy_inputs(lowercase_				)
          UpperCAmelCase          =       3 * ['this is a negative prompt']
          UpperCAmelCase          =       negative_prompt
          UpperCAmelCase          =       3 * [inputs['prompt']]

          UpperCAmelCase          =       sd_pipe(**lowercase_				)
          UpperCAmelCase          =       output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          UpperCAmelCase          =       self.get_dummy_inputs(lowercase_				)
          UpperCAmelCase          =       3 * ['this is a negative prompt']
          UpperCAmelCase          =       3 * [inputs.pop('prompt'				)]

          (
              (
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				
          )          =       sd_pipe.encode_prompt(lowercase_ ,							negative_prompt=lowercase_				)

          UpperCAmelCase          =       sd_pipe(
              **lowercase_ ,							prompt_embeds=lowercase_ ,							negative_prompt_embeds=lowercase_ ,							pooled_prompt_embeds=lowercase_ ,							negative_pooled_prompt_embeds=lowercase_ ,							)
          UpperCAmelCase          =       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   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       Dict:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




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

          UpperCAmelCase          =       self.get_inputs(lowercase_				)
          UpperCAmelCase          =       pipe(**lowercase_				).images
          UpperCAmelCase          =       image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          UpperCAmelCase          =       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





"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



