



"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class        __snake_case       (	_lowercase):
  snake_case__				:   Optional[Any]  			=						(CMStochasticIterativeScheduler,)
  snake_case__				:   Tuple  			=						1_0




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= {
             '''num_train_timesteps''': 2_0_1,
             '''sigma_min''': 0.0_02,
             '''sigma_max''': 80.0,
         }

         config.update(**__lowerCAmelCase		)
         return config




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= 1_0

         _lowerCamelCase						:				List[str]    			= self.get_scheduler_config()
         _lowerCamelCase						:				Union[str, Any]    			= self.scheduler_classes[0](**__lowerCAmelCase		)

         scheduler.set_timesteps(__lowerCAmelCase		)

         _lowerCamelCase						:				Optional[Any]    			= scheduler.timesteps[0]
         _lowerCamelCase						:				str    			= scheduler.timesteps[1]

         _lowerCamelCase						:				List[Any]    			= self.dummy_sample
         _lowerCamelCase						:				str    			= 0.1 * sample

         _lowerCamelCase						:				str    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		).prev_sample
         _lowerCamelCase						:				Tuple    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		).prev_sample

         self.assertEqual(output_a.shape						,					sample.shape		)
         self.assertEqual(output_a.shape						,					output_a.shape		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         for clip_denoised in [True, False]:
                self.check_over_configs(clip_denoised=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Optional[Any]    			= self.get_scheduler_config()
         _lowerCamelCase						:				int    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				str    			= 1
         scheduler.set_timesteps(__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= scheduler.timesteps

         _lowerCamelCase						:				Tuple    			= torch.manual_seed(0		)

         _lowerCamelCase						:				int    			= self.dummy_model()
         _lowerCamelCase						:				Dict    			= self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(__lowerCAmelCase		):
                # 1. scale model input
                _lowerCamelCase						:				Dict    			= scheduler.scale_model_input(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 2. predict noise residual
                _lowerCamelCase						:				Optional[int]    			= model(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 3. predict previous sample x_t-1
                _lowerCamelCase						:				Any    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:				List[str]    			= pred_prev_sample

         _lowerCamelCase						:				int    			= torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				str    			= torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 1_92.76_14		) < 1E-2
         assert abs(result_mean.item() - 0.25_10		) < 1E-3




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				List[str]    			= self.get_scheduler_config()
         _lowerCamelCase						:				str    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				int    			= [1_0_6, 0]
         scheduler.set_timesteps(timesteps=__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= scheduler.timesteps

         _lowerCamelCase						:				str    			= torch.manual_seed(0		)

         _lowerCamelCase						:				Optional[int]    			= self.dummy_model()
         _lowerCamelCase						:				List[str]    			= self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
                # 1. scale model input
                _lowerCamelCase						:				Optional[Any]    			= scheduler.scale_model_input(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 2. predict noise residual
                _lowerCamelCase						:				Optional[Any]    			= model(__lowerCAmelCase						,					__lowerCAmelCase		)

                # 3. predict previous sample x_t-1
                _lowerCamelCase						:				Union[str, Any]    			= scheduler.step(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:				List[str]    			= pred_prev_sample

         _lowerCamelCase						:				Optional[int]    			= torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				Tuple    			= torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 3_47.63_57		) < 1E-2
         assert abs(result_mean.item() - 0.45_27		) < 1E-3




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				str    			= self.get_scheduler_config()
         _lowerCamelCase						:				List[Any]    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Union[str, Any]    			= [3_9, 3_0, 1_2, 1_5, 0]

         with self.assertRaises(__lowerCAmelCase						,					msg='''`timesteps` must be in descending order.'''		):
                scheduler.set_timesteps(timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Any    			= self.get_scheduler_config()
         _lowerCamelCase						:				List[Any]    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				List[Any]    			= [3_9, 3_0, 1_2, 1, 0]
         _lowerCamelCase						:				Optional[Any]    			= len(__lowerCAmelCase		)

         with self.assertRaises(__lowerCAmelCase						,					msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''		):
                scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase						,					timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.scheduler_classes[0]
         _lowerCamelCase						:				Dict    			= self.get_scheduler_config()
         _lowerCamelCase						:				Dict    			= scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:				Dict    			= [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __lowerCAmelCase						,					msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''						,					):
                scheduler.set_timesteps(timesteps=__lowerCAmelCase		)



# 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       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    ArgumentParser("Accelerate CLI tool"   , usage="accelerate <command> [<args>]"   , allow_abbrev=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    parser.add_subparsers(help="accelerate command helpers"	)

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

		# Let's go
		SCREAMING_SNAKE_CASE_  :		Dict				      =    parser.parse_args()

		if not hasattr(lowerCAmelCase   , "func"	):
				parser.print_help()
				exit(1	)

		# Run
		args.func(lowerCAmelCase	)


if __name__ == "__main__":
		main()
