

'''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 lowerCamelCase   (    unittest.TestCase				):

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					1
					snake_case							=					3
					snake_case							=					(32, 32)

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

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					torch.manual_seed(0 )
					snake_case							=					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 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					torch.manual_seed(0 )
					snake_case							=					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 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					'cpu'
					snake_case							=					self.dummy_uncond_unet
					snake_case							=					DDIMScheduler()
					snake_case							=					self.dummy_vq_model

					snake_case							=					LDMSuperResolutionPipeline(unet=lowercase_,						vqvae=lowercase_,						scheduler=lowercase_ )
					ldm.to(lowercase_ )
					ldm.set_progress_bar_config(disable=lowercase_ )

					snake_case							=					self.dummy_image.to(lowercase_ )

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

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

					assert image.shape == (1, 64, 64, 3)
					snake_case							=					np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150] )

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






				@unittest.skipIf(torch_device != 'cuda',						'This test requires a GPU' )
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.dummy_uncond_unet
					snake_case							=					DDIMScheduler()
					snake_case							=					self.dummy_vq_model

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

					snake_case							=					LDMSuperResolutionPipeline(unet=lowercase_,						vqvae=lowercase_,						scheduler=lowercase_ )
					ldm.to(lowercase_ )
					ldm.set_progress_bar_config(disable=lowercase_ )

					snake_case							=					self.dummy_image.to(lowercase_ )

					snake_case							=					ldm(lowercase_,						num_inference_steps=2,						output_type='numpy' ).images

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







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

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

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

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

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

					assert image.shape == (1, 256, 256, 3)
					snake_case							=					np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907] )

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













from collections import namedtuple

__UpperCAmelCase      =				namedtuple("from_to", "from_ to")

__UpperCAmelCase      =				{
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__UpperCAmelCase      =				{
    "mm": from_to(0.001, 1_0_0_0),
    "cm": from_to(0.01, 1_0_0),
    "m": from_to(1, 1),
    "km": from_to(1_0_0_0, 0.001),
    "in": from_to(0.0_254, 39.3_701),
    "ft": from_to(0.3_048, 3.28_084),
    "yd": from_to(0.9_144, 1.09_361),
    "mi": from_to(1_609.34, 0.000_621_371),
}





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->float:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		from_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		to_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 if new_from not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'from_type\' value: {from_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 if new_to not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'to_type\' value: {to_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()




