






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        _lowerCAmelCase( UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase      ):





       """simple docstring"""







       a       :	str								=StableDiffusionXLPipeline
       a       :	List[str]								=TEXT_TO_IMAGE_PARAMS
       a       :	Any								=TEXT_TO_IMAGE_BATCH_PARAMS
       a       :	Any								=TEXT_TO_IMAGE_IMAGE_PARAMS
       a       :	Tuple								=TEXT_TO_IMAGE_IMAGE_PARAMS





       def     _a					(      self							):
         torch.manual_seed(0							)
         UpperCamelCase_:      Union[str, Any]					   =       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						,    )
         UpperCamelCase_:      List[str]					   =       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_:      Tuple					   =       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_:      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						,    )
         UpperCamelCase_:      Optional[int]					   =       CLIPTextModel(_lowerCamelCase							)
         UpperCamelCase_:      Tuple					   =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,    local_files_only=_lowerCamelCase							)

         UpperCamelCase_:      Union[str, Any]					   =       CLIPTextModelWithProjection(_lowerCamelCase							)
         UpperCamelCase_:      Any					   =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						,    local_files_only=_lowerCamelCase							)

         UpperCamelCase_:      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     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase=0							):
         if str(_lowerCamelCase							).startswith('mps'							):
           UpperCamelCase_:      Any					   =       torch.manual_seed(_lowerCamelCase							)
         else:
           UpperCamelCase_:      Tuple					   =       torch.Generator(device=_lowerCamelCase							).manual_seed(_lowerCamelCase							)
         UpperCamelCase_:      Tuple					   =       {
             '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     _a					(      self							):
         UpperCamelCase_:      Dict					   =       'cpu'  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase_:      Union[str, Any]					   =       self.get_dummy_components()
         UpperCamelCase_:      Optional[int]					   =       StableDiffusionXLPipeline(**_lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       sd_pipe.to(_lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_:      Dict					   =       self.get_dummy_inputs(_lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       sd_pipe(**_lowerCamelCase							).images
         UpperCamelCase_:      Union[str, Any]					   =       image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         UpperCamelCase_:      Tuple					   =       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     _a					(      self							):
         UpperCamelCase_:      List[Any]					   =       self.get_dummy_components()
         UpperCamelCase_:      Union[str, Any]					   =       StableDiffusionXLPipeline(**_lowerCamelCase							)
         UpperCamelCase_:      str					   =       sd_pipe.to(_lowerCamelCase							)
         UpperCamelCase_:      Union[str, Any]					   =       sd_pipe.to(_lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         # forward without prompt embeds
         UpperCamelCase_:      List[Any]					   =       self.get_dummy_inputs(_lowerCamelCase							)
         UpperCamelCase_:      Dict					   =       3 * ['this is a negative prompt']
         UpperCamelCase_:      Optional[Any]					   =       negative_prompt
         UpperCamelCase_:      Any					   =       3 * [inputs['prompt']]

         UpperCamelCase_:      Optional[int]					   =       sd_pipe(**_lowerCamelCase							)
         UpperCamelCase_:      Optional[int]					   =       output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         UpperCamelCase_:      Optional[Any]					   =       self.get_dummy_inputs(_lowerCamelCase							)
         UpperCamelCase_:      Dict					   =       3 * ['this is a negative prompt']
         UpperCamelCase_:      Optional[Any]					   =       3 * [inputs.pop('prompt'							)]

         (
             UpperCamelCase_
         ):      Optional[Any]					   =       sd_pipe.encode_prompt(_lowerCamelCase						,    negative_prompt=_lowerCamelCase							)

         UpperCamelCase_:      int					   =       sd_pipe(
             **_lowerCamelCase						,    prompt_embeds=_lowerCamelCase						,    negative_prompt_embeds=_lowerCamelCase						,    pooled_prompt_embeds=_lowerCamelCase						,    negative_pooled_prompt_embeds=_lowerCamelCase						,    )
         UpperCamelCase_:      List[Any]					   =       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     _a					(      self							):
         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)





       def     _a					(      self							):
         super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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





       """simple docstring"""







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





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

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCamelCase_:      Optional[int]					   =       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







from math import factorial

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Dict:
  return factorial(UpperCAmelCase__						) / (factorial(UpperCAmelCase__						) * factorial(n - r						))

def 						snake_case      () ->       Optional[Any]:
  UpperCamelCase_:      int					   =       0

  for i in range(1			,					1_0_1						):
    for j in range(1			,					i + 1						):
      if combinations(UpperCAmelCase__			,					UpperCAmelCase__						) > 1E6:
        total += 1
  return total


if __name__ == "__main__":
   print(solution())