



'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			@property
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					torch.manual_seed(0						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		UNetaDModel(
					    block_out_channels=(3_2, 6_4)							,     layers_per_block=2							,     sample_size=3_2							,     in_channels=3							,     out_channels=3							,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")							,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")							,     )
					return model



			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		self.dummy_uncond_unet
					__SCREAMING_SNAKE_CASE       :		List[str]           =		KarrasVeScheduler()

					__SCREAMING_SNAKE_CASE       :		str           =		KarrasVePipeline(unet=lowerCAmelCase__							,     scheduler=lowerCAmelCase__						)
					pipe.to(lowerCAmelCase__						)
					pipe.set_progress_bar_config(disable=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.manual_seed(0						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		pipe(num_inference_steps=2							,     generator=lowerCAmelCase__							,     output_type="""numpy"""						).images

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch.manual_seed(0						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		pipe(num_inference_steps=2							,     generator=lowerCAmelCase__							,     output_type="""numpy"""							,     return_dict=lowerCAmelCase__						)[0]

					__SCREAMING_SNAKE_CASE       :		Tuple           =		image[0, -3:, -3:, -1]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 3_2, 3_2, 3)
					__SCREAMING_SNAKE_CASE       :		str           =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

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






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


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""google/ncsnpp-celebahq-256"""
					__SCREAMING_SNAKE_CASE       :		Dict           =		UNetaDModel.from_pretrained(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		KarrasVeScheduler()

					__SCREAMING_SNAKE_CASE       :		Tuple           =		KarrasVePipeline(unet=lowerCAmelCase__							,     scheduler=lowerCAmelCase__						)
					pipe.to(lowerCAmelCase__						)
					pipe.set_progress_bar_config(disable=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch.manual_seed(0						)
					__SCREAMING_SNAKE_CASE       :		Any           =		pipe(num_inference_steps=2_0							,     generator=lowerCAmelCase__							,     output_type="""numpy"""						).images

					__SCREAMING_SNAKE_CASE       :		int           =		image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_5_6, 2_5_6, 3)
					__SCREAMING_SNAKE_CASE       :		int           =		np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]						)

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




'''simple docstring'''




from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__       :	Dict    = [
    '''python''',
    '''tqdm''',
    '''regex''',
    '''requests''',
    '''packaging''',
    '''filelock''',
    '''numpy''',
    '''tokenizers''',
    '''huggingface-hub''',
    '''safetensors''',
    '''accelerate''',
    '''pyyaml''',
]

for pkg in pkgs_to_check_at_runtime:
						if pkg in deps:
												if pkg == "tokenizers":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_tokenizers_available

																		if not is_tokenizers_available():
																								continue  # not required, check version only if installed
												elif pkg == "accelerate":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_accelerate_available

																		# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
																		# Transformers with PyTorch
																		if not is_accelerate_available():
																								continue  # not required, check version only if installed

												require_version_core(deps[pkg])
						else:
												raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   Optional[int]=None					):
		require_version(deps[pkg]       ,      _lowerCamelCase					)