import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class    a__				(       unittest.TestCase			):






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         torch.manual_seed(0   )
         __lowerCamelCase          =  UNetaDModel(
             block_out_channels=(32, 64)  ,    layers_per_block=2  ,    sample_size=32  ,    in_channels=3  ,    out_channels=3  ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,    )
         return model






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         torch.manual_seed(0   )
         __lowerCamelCase          =  VQModel(
             block_out_channels=[32, 64]  ,    in_channels=3  ,    out_channels=3  ,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,    latent_channels=3  ,    )
         return model






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         torch.manual_seed(0   )
         __lowerCamelCase          =  CLIPTextConfig(
             bos_token_id=0  ,    eos_token_id=2  ,    hidden_size=32  ,    intermediate_size=37  ,    layer_norm_eps=1e-0_5  ,    num_attention_heads=4  ,    num_hidden_layers=5  ,    pad_token_id=1  ,    vocab_size=10_00  ,    )
         return CLIPTextModel(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  self.dummy_uncond_unet
         __lowerCamelCase          =  DDIMScheduler()
         __lowerCamelCase          =  self.dummy_vq_model

         __lowerCamelCase          =  LDMPipeline(unet=a  ,    vqvae=a  ,    scheduler=a   )
         ldm.to(a   )
         ldm.set_progress_bar_config(disable=a   )

         __lowerCamelCase          =  torch.manual_seed(0   )
         __lowerCamelCase          =  ldm(generator=a  ,    num_inference_steps=2  ,    output_type='''numpy'''   ).images

         __lowerCamelCase          =  torch.manual_seed(0   )
         __lowerCamelCase          =  ldm(generator=a  ,    num_inference_steps=2  ,    output_type='''numpy'''  ,    return_dict=a   )[0]

         __lowerCamelCase          =  image[0, -3:, -3:, -1]
         __lowerCamelCase          =  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __lowerCamelCase          =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]   )
         __lowerCamelCase          =  1e-2 if torch_device != '''mps''' else 3e-2

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < tolerance
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < tolerance




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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''   )
         ldm.to(a   )
         ldm.set_progress_bar_config(disable=a   )

         __lowerCamelCase          =  torch.manual_seed(0   )
         __lowerCamelCase          =  ldm(generator=a  ,    num_inference_steps=5  ,    output_type='''numpy'''   ).images

         __lowerCamelCase          =  image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         __lowerCamelCase          =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]   )
         __lowerCamelCase          =  1e-2 if torch_device != '''mps''' else 3e-2

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < tolerance

'''simple docstring'''




from bisect import bisect
from itertools import accumulate






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  sorted(zip(UpperCamelCase__    ,  UpperCamelCase__    )    ,  key=lambda UpperCamelCase__    : x[0] / x[1]    ,  reverse=UpperCamelCase__    )
    __lowerCamelCase     ,  __lowerCamelCase          =  [i[0] for i in r], [i[1] for i in r]
    __lowerCamelCase          =  list(accumulate(UpperCamelCase__    )    )
    __lowerCamelCase          =  bisect(UpperCamelCase__    ,  UpperCamelCase__    )
    return (
        0
        if k == 0
        else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
        if k != n
        else sum(vl[:k]    )
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
