
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  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@property
	def 						_lowercase  (       self  :			str       )       ->     Optional[Any]:
					torch.manual_seed(0       )
					lowercase_       =					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 						_lowercase  (       self  :			str       )       ->     List[Any]:
					lowercase_       =					self.dummy_uncond_unet
					lowercase_       =					KarrasVeScheduler()

					lowercase_       =					KarrasVePipeline(unet=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       )
					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(num_inference_steps=2       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''numpy'''       ).images

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(num_inference_steps=2       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''numpy'''       ,							return_dict=SCREAMING_SNAKE_CASE_       )[0]

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

					assert image.shape == (1, 3_2, 3_2, 3)
					lowercase_       =					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  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			int       )       ->     int:
					lowercase_       =					'''google/ncsnpp-celebahq-256'''
					lowercase_       =					UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					KarrasVeScheduler()

					lowercase_       =					KarrasVePipeline(unet=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       )
					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(num_inference_steps=2_0       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''numpy'''       ).images

					lowercase_       =					image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_5_6, 2_5_6, 3)
					lowercase_       =					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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




