"""simple docstring"""







import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @property
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					1
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					(32, 32)

      __lowerCamelCase    		=					floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0	)	).to(_SCREAMING_SNAKE_CASE	)
      return image







   @property
   def    _A				(	self	):


      '''simple docstring'''

      torch.manual_seed(0	)
      __lowerCamelCase    		=					UNetaDModel(
          block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=6 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
      return model







   @property
   def    _A				(	self	):


      '''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







   def    _A				(	self	):


      '''simple docstring'''

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

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

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

      assert image.shape == (1, 64, 64, 3)
      __lowerCamelCase    		=					np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]	)

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







   @unittest.skipIf(torch_device != 'cuda' ,				'This test requires a GPU'	)
   def    _A				(	self	):


      '''simple docstring'''

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

      # put models in fp16
      __lowerCamelCase    		=					unet.half()
      __lowerCamelCase    		=					vqvae.half()

      __lowerCamelCase    		=					LDMSuperResolutionPipeline(unet=_SCREAMING_SNAKE_CASE ,				vqvae=_SCREAMING_SNAKE_CASE ,				scheduler=_SCREAMING_SNAKE_CASE	)
      ldm.to(_SCREAMING_SNAKE_CASE	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.dummy_image.to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					ldm(_SCREAMING_SNAKE_CASE ,				num_inference_steps=2 ,				output_type='numpy'	).images

      assert image.shape == (1, 64, 64, 3)






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



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/vq_diffusion/teddy_bear_pool.png'	)
      __lowerCamelCase    		=					init_image.resize((64, 64) ,				resample=PIL_INTERPOLATION['lanczos']	)

      __lowerCamelCase    		=					LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution' ,				device_map='auto'	)
      ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.manual_seed(0	)
      __lowerCamelCase    		=					ldm(image=_SCREAMING_SNAKE_CASE ,				generator=_SCREAMING_SNAKE_CASE ,				num_inference_steps=20 ,				output_type='numpy'	).images

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

      assert image.shape == (1, 256, 256, 3)
      __lowerCamelCase    		=					np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]	)

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












'''simple docstring'''







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


A					       =10_00_00_00_00_00
A					       =1_00_00

A       , A					       =os.path.split(__file__)
A					       =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





def 		snake_case_				(_a      :				int       ,  _a      :				int	):
    UpperCAmelCase     			=						pa.Table.from_pydict({'''col''': [0] * chunk_size}	)
    UpperCAmelCase     			=						pa.concat_tables([table] * (num_examples // chunk_size)	)
    return datasets.Dataset(_a       ,  fingerprint='''table_100B'''	)


@dataclass
class      _a    :
       __a							:	int
       __a							:	int
       __a							:	int
       __a							:	int
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						np.random.default_rng(self.seed							)
           UpperCAmelCase     			=						rng.integers(low=self.low  ,							high=self.high  ,							size=self.size							).tolist()
       def __iter__(      self		:      Tuple							):



           '''simple docstring'''

           return iter(self._sampled_values							)







       def __len__(      self		:      Dict							):



           '''simple docstring'''

           return self.size






@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[0]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[-1]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[range(len(_a	) // 2       ,  len(_a	) // 2 + 1_0_2_4	)]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[RandIter(0       ,  len(_a	)       ,  1_0_2_4       ,  seed=4_2	)]





def 		snake_case_				():
    UpperCAmelCase     			=						{'''num examples''': SPEED_TEST_N_EXAMPLES}
    UpperCAmelCase     			=						(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print('''generating dataset'''	)
    UpperCAmelCase     			=						generate_100B_dataset(num_examples=_a       ,  chunk_size=_a	)
    print('''Functions'''	)
    for func in functions:
        print(func.__name__	)
        UpperCAmelCase     			=						func(_a	)

    with open(_a       ,  '''wb'''	) as f:
        f.write(json.dumps(_a	).encode('''utf-8'''	)	)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_table_100B()




