import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				(CMStochasticIterativeScheduler,)
  lowercase		:     Optional[int]            =				10







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	**SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Union[str, Any]:
         A						:     List[str]         ={
             'num_train_timesteps': 2_01,
             'sigma_min': 0.0_0_2,
             'sigma_max': 8_0.0,
         }

         config.update(**SCREAMING_SNAKE_CASE__     )
         return config







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[int]:
         A						:     str         =10

         A						:     List[str]         =self.get_scheduler_config()
         A						:     Any         =self.scheduler_classes[0](**SCREAMING_SNAKE_CASE__     )

         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )

         A						:     str         =scheduler.timesteps[0]
         A						:     Tuple         =scheduler.timesteps[1]

         A						:     Dict         =self.dummy_sample
         A						:     List[str]         =0.1 * sample

         A						:     Tuple         =scheduler.step(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ).prev_sample
         A						:     Dict         =scheduler.step(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ).prev_sample

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         for clip_denoised in [True, False]:
                self.check_over_configs(clip_denoised=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         A						:     Dict         =self.scheduler_classes[0]
         A						:     Union[str, Any]         =self.get_scheduler_config()
         A						:     Union[str, Any]         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =1
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =scheduler.timesteps

         A						:     Optional[Any]         =torch.manual_seed(0     )

         A						:     List[Any]         =self.dummy_model()
         A						:     Any         =self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(SCREAMING_SNAKE_CASE__     ):
                # 1. scale model input
                A						:     Optional[Any]         =scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                # 2. predict noise residual
                A						:     str         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                # 3. predict previous sample x_t-1
                A						:     Optional[Any]         =scheduler.step(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	generator=SCREAMING_SNAKE_CASE__     ).prev_sample

                A						:     Any         =pred_prev_sample

         A						:     Dict         =torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
         A						:     Dict         =torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

         assert abs(result_sum.item() - 1_9_2.7_6_1_4     ) < 1e-2
         assert abs(result_mean.item() - 0.2_5_1_0     ) < 1e-3







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				str:
         A						:     int         =self.scheduler_classes[0]
         A						:     str         =self.get_scheduler_config()
         A						:     Dict         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         A						:     int         =[1_06, 0]
         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =scheduler.timesteps

         A						:     Dict         =torch.manual_seed(0     )

         A						:     str         =self.dummy_model()
         A						:     str         =self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
                # 1. scale model input
                A						:     Union[str, Any]         =scheduler.scale_model_input(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                # 2. predict noise residual
                A						:     str         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                # 3. predict previous sample x_t-1
                A						:     int         =scheduler.step(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	generator=SCREAMING_SNAKE_CASE__     ).prev_sample

                A						:     Union[str, Any]         =pred_prev_sample

         A						:     int         =torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
         A						:     Dict         =torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

         assert abs(result_sum.item() - 3_4_7.6_3_5_7     ) < 1e-2
         assert abs(result_mean.item() - 0.4_5_2_7     ) < 1e-3







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Dict:
         A						:     Union[str, Any]         =self.scheduler_classes[0]
         A						:     List[str]         =self.get_scheduler_config()
         A						:     Tuple         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         A						:     List[Any]         =[39, 30, 12, 15, 0]

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         A						:     Optional[int]         =self.scheduler_classes[0]
         A						:     int         =self.get_scheduler_config()
         A						:     Any         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         A						:     Union[str, Any]         =[39, 30, 12, 1, 0]
         A						:     Dict         =len(SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[Any]:
         A						:     Optional[Any]         =self.scheduler_classes[0]
         A						:     Tuple         =self.get_scheduler_config()
         A						:     List[Any]         =scheduler_class(**SCREAMING_SNAKE_CASE__     )

         A						:     List[Any]         =[scheduler.config.num_train_timesteps]

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







# 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase							:      List[str]      							='''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''



def A__  (			)				->		List[Any]:
       A						:     Any         =_ask_options(
           'In which compute environment are you running?',		['This machine', 'AWS (Amazon SageMaker)'],		_convert_compute_environment,		)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A						:     Tuple         =get_sagemaker_input()
       else:
              A						:     str         =get_cluster_input()
       return config



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       parser.add_argument(
           '--config_file',		default=lowercase,		help=(
               'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
               'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
               'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
               'with \'huggingface\'.'
           ),		)

       if subparsers is not None:
              parser.set_defaults(func=lowercase					)
       return parser



def A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

       if config_file.endswith('.json'					):
              config.to_json_file(lowercase					)
       else:
              config.to_yaml_file(lowercase					)
       print(F'accelerate configuration saved at {config_file}'					)



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






