
"""simple docstring"""

import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						LDMTextToImagePipeline
      __SCREAMING_SNAKE_CASE :				str				   =						TEXT_TO_IMAGE_PARAMS - {
          "negative_prompt",
          "negative_prompt_embeds",
          "cross_attention_kwargs",
          "prompt_embeds",
      }
      __SCREAMING_SNAKE_CASE :				Any				   =						PipelineTesterMixin.required_optional_params - {
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						TEXT_TO_IMAGE_BATCH_PARAMS
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						False




      def      a_   (				self				)    ->       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') ,							cross_attention_dim=3_2 ,							)
          UpperCAmelCase          =       DDIMScheduler(
              beta_start=0.0_0_0_8_5 ,							beta_end=0.0_1_2 ,							beta_schedule='scaled_linear' ,							clip_sample=lowercase_ ,							set_alpha_to_one=lowercase_ ,							)
          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 ,							)
          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 ,							)
          UpperCAmelCase          =       CLIPTextModel(lowercase_				)
          UpperCAmelCase          =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

          UpperCAmelCase          =       {
              'unet': unet,
              'scheduler': scheduler,
              'vqvae': vae,
              'bert': text_encoder,
              'tokenizer': tokenizer,
          }
          return components




      def      a_   (				self ,							lowercase_ ,							lowercase_=0				)    ->       Any:
          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',
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       'cpu'  # ensure determinism for the device-dependent torch.Generator

          UpperCAmelCase          =       self.get_dummy_components()
          UpperCAmelCase          =       LDMTextToImagePipeline(**lowercase_				)
          pipe.to(lowercase_				)
          pipe.set_progress_bar_config(disable=lowercase_				)

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

          assert image.shape == (1, 1_6, 1_6, 3)
          UpperCAmelCase          =       np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]				)

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

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




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




      def      a_   (				self ,							lowercase_ ,							lowercase_=torch.floataa ,							lowercase_=0				)    ->       Dict:
          UpperCAmelCase          =       torch.manual_seed(lowercase_				)
          UpperCAmelCase          =       np.random.RandomState(lowercase_				).standard_normal((1, 4, 3_2, 3_2)				)
          UpperCAmelCase          =       torch.from_numpy(lowercase_				).to(device=lowercase_ ,							dtype=lowercase_				)
          UpperCAmelCase          =       {
              'prompt': 'A painting of a squirrel eating a burger',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'				).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, 2_5_6, 2_5_6, 3)
          UpperCAmelCase          =       np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]				)
          UpperCAmelCase          =       np.abs(expected_slice - image_slice				).max()
          assert max_diff < 1E-3

@nightly
@require_torch_gpu
class   _UpperCAmelCase				(		unittest.TestCase				):




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




      def      a_   (				self ,							lowercase_ ,							lowercase_=torch.floataa ,							lowercase_=0				)    ->       Tuple:
          UpperCAmelCase          =       torch.manual_seed(lowercase_				)
          UpperCAmelCase          =       np.random.RandomState(lowercase_				).standard_normal((1, 4, 3_2, 3_2)				)
          UpperCAmelCase          =       torch.from_numpy(lowercase_				).to(device=lowercase_ ,							dtype=lowercase_				)
          UpperCAmelCase          =       {
              'prompt': 'A painting of a squirrel eating a burger',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 5_0,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'				).to(lowercase_				)
          pipe.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       self.get_inputs(lowercase_				)
          UpperCAmelCase          =       pipe(**lowercase_				).images[0]

          UpperCAmelCase          =       load_numpy(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'				)
          UpperCAmelCase          =       np.abs(expected_image - image				).max()
          assert max_diff < 1E-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()



