




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class        __lowerCAmelCase					(			__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			DDIMPipeline
			snake_case_  						=			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			snake_case_  						=			PipelineTesterMixin.required_optional_params - {
			    '''num_images_per_prompt''',
			    '''latents''',
			    '''callback''',
			    '''callback_steps''',
			}
			snake_case_  						=			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			snake_case_  						=			False


			def  lowercase_       ( self				)       -> Union[str, 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')     ,		)
							__lowerCamelCase									=       DDIMScheduler()
							__lowerCamelCase									=       {'unet': unet, 'scheduler': scheduler}
							return components


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__=0				)       -> List[Any]:



							'''simple docstring'''
							if str(lowerCamelCase__				).startswith('mps'				):
											__lowerCamelCase									=       torch.manual_seed(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
							__lowerCamelCase									=       {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       'cpu'

							__lowerCamelCase									=       self.get_dummy_components()
							__lowerCamelCase									=       self.pipeline_class(**lowerCamelCase__				)
							pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							__lowerCamelCase									=       self.get_dummy_inputs(lowerCamelCase__				)
							__lowerCamelCase									=       pipe(**lowerCamelCase__				).images
							__lowerCamelCase									=       image[0, -3:, -3:, -1]

							self.assertEqual(image.shape     ,		(1, 32, 32, 3)				)
							__lowerCamelCase									=       np.array(
							    [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]				)
							__lowerCamelCase									=       np.abs(image_slice.flatten() - expected_slice				).max()
							self.assertLessEqual(lowerCamelCase__     ,		1e-3				)


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3				)


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							super().test_save_load_local(expected_max_difference=3e-3				)


			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							super().test_save_load_optional_components(expected_max_difference=3e-3				)






			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







@slow
@require_torch_gpu
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       'google/ddpm-cifar10-32'

							__lowerCamelCase									=       UNetaDModel.from_pretrained(lowerCamelCase__				)
							__lowerCamelCase									=       DDIMScheduler()

							__lowerCamelCase									=       DDIMPipeline(unet=lowerCamelCase__     ,		scheduler=lowerCamelCase__				)
							ddim.to(lowerCamelCase__				)
							ddim.set_progress_bar_config(disable=lowerCamelCase__				)

							__lowerCamelCase									=       torch.manual_seed(0				)
							__lowerCamelCase									=       ddim(generator=lowerCamelCase__     ,		eta=0.0     ,		output_type='numpy'				).images

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

							assert image.shape == (1, 32, 32, 3)
							__lowerCamelCase									=       np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]				)

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






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       'google/ddpm-ema-bedroom-256'

							__lowerCamelCase									=       UNetaDModel.from_pretrained(lowerCamelCase__				)
							__lowerCamelCase									=       DDIMScheduler.from_pretrained(lowerCamelCase__				)

							__lowerCamelCase									=       DDIMPipeline(unet=lowerCamelCase__     ,		scheduler=lowerCamelCase__				)
							ddpm.to(lowerCamelCase__				)
							ddpm.set_progress_bar_config(disable=lowerCamelCase__				)

							__lowerCamelCase									=       torch.manual_seed(0				)
							__lowerCamelCase									=       ddpm(generator=lowerCamelCase__     ,		output_type='numpy'				).images

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

							assert image.shape == (1, 256, 256, 3)
							__lowerCamelCase									=       np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]				)

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








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser







def      lowerCamelCase_						(			)	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       ArgumentParser('Accelerate CLI tool'					,	usage='accelerate <command> [<args>]'					,	allow_abbrev=UpperCamelCase__ )
				__lowerCamelCase									=       parser.add_subparsers(help='accelerate command helpers' )

				# Register commands
				get_config_parser(subparsers=UpperCamelCase__ )
				env_command_parser(subparsers=UpperCamelCase__ )
				launch_command_parser(subparsers=UpperCamelCase__ )
				tpu_command_parser(subparsers=UpperCamelCase__ )
				test_command_parser(subparsers=UpperCamelCase__ )

				# Let's go
				__lowerCamelCase									=       parser.parse_args()

				if not hasattr(UpperCamelCase__					,	'func' ):
								parser.print_help()
								exit(1 )

				# Run
				args.func(UpperCamelCase__ )


if __name__ == "__main__":
					main()


