



'''simple docstring'''





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :str						   =					(DDIMParallelScheduler,)
       lowerCAmelCase__  :List[Any]						   =					(("eta", 0.0), ("num_inference_steps", 50))



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           lowercase__		       =						{
               "num_train_timesteps": 1_000,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "clip_sample": True,
           }

           config.update(**UpperCAmelCase_      )
           return config



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(**UpperCAmelCase_      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						10, 0.0

           lowercase__		       =						self.dummy_model()
           lowercase__		       =						self.dummy_sample_deter

           scheduler.set_timesteps(UpperCAmelCase_      )

           for t in scheduler.timesteps:
               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						scheduler.step(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      ).prev_sample

           return sample



       def 							_a      (				self      )  ->							Tuple:
           for timesteps in [100, 500, 1_000]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=UpperCAmelCase_      )

           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(steps_offset=1      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )
           scheduler.set_timesteps(5      )
           assert torch.equal(scheduler.timesteps     ,torch.LongTensor([801, 601, 401, 201, 1]      )      )



       def 							_a      (				self      )  ->							Any:
           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,[0.0_02, 0.02, 0.2, 2]      ):
               self.check_over_configs(beta_start=UpperCAmelCase_     ,beta_end=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Dict:
           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           self.check_over_configs(thresholding=UpperCAmelCase_      )
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=UpperCAmelCase_     ,prediction_type=UpperCAmelCase_     ,sample_max_value=UpperCAmelCase_     ,)



       def 							_a      (				self      )  ->							List[str]:
           for t in [1, 10, 49]:
               self.check_over_forward(time_step=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           for t, num_inference_steps in zip([1, 10, 50]     ,[10, 50, 500]      ):
               self.check_over_forward(time_step=UpperCAmelCase_     ,num_inference_steps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           for t, eta in zip([1, 10, 49]     ,[0.0, 0.5, 1.0]      ):
               self.check_over_forward(time_step=UpperCAmelCase_     ,eta=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config()
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           assert torch.sum(torch.abs(scheduler._get_variance(0     ,0      ) - 0.0      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(420     ,400      ) - 0.1_47_71      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(980     ,960      ) - 0.3_24_60      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0     ,0      ) - 0.0      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487     ,486      ) - 0.0_09_79      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999     ,998      ) - 0.02      )      ) < 1E-5



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config()
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						10, 0.0
           scheduler.set_timesteps(UpperCAmelCase_      )

           lowercase__		       =						self.dummy_model()
           lowercase__		       =						self.dummy_sample_deter
           lowercase__		       =						self.dummy_sample_deter + 0.1
           lowercase__		       =						self.dummy_sample_deter - 0.1

           lowercase__		       =						samplea.shape[0]
           lowercase__		       =						torch.stack([samplea, samplea, samplea]     ,dim=0      )
           lowercase__		       =						torch.arange(UpperCAmelCase_      )[0:3, None].repeat(1     ,UpperCAmelCase_      )

           lowercase__		       =						model(samples.flatten(0     ,1      )     ,timesteps.flatten(0     ,1      )      )
           lowercase__		       =						scheduler.batch_step_no_noise(UpperCAmelCase_     ,timesteps.flatten(0     ,1      )     ,samples.flatten(0     ,1      )     ,UpperCAmelCase_      )

           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 11_47.79_04      ) < 1E-2
           assert abs(result_mean.item() - 0.49_82      ) < 1E-3



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.full_loop()

           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
           assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.full_loop(prediction_type="v_prediction"      )

           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 52.53_02      ) < 1E-2
           assert abs(result_mean.item() - 0.06_84      ) < 1E-3



       def 							_a      (				self      )  ->							Dict:
           # We specify different beta, so that the first alpha is 0.99
           lowercase__		       =						self.full_loop(set_alpha_to_one=UpperCAmelCase_     ,beta_start=0.01      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
           assert abs(result_mean.item() - 0.19_51      ) < 1E-3







       def 							_a      (				self      )  ->							int:
           # We specify different beta, so that the first alpha is 0.99
           lowercase__		       =						self.full_loop(set_alpha_to_one=UpperCAmelCase_     ,beta_start=0.01      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
           assert abs(result_mean.item() - 0.19_41      ) < 1E-3









'''simple docstring'''





# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def        lowerCamelCase  (				_snake_case  :						str=None       ):





    '''simple docstring'''


    if subparsers is not None:
        lowercase__		       =						subparsers.add_parser("env"       )
    else:
        lowercase__		       =						argparse.ArgumentParser("Accelerate env command"       )

    parser.add_argument(
        "--config_file"     ,default=_snake_case     ,help="The config file to use for the default values in the launching script."       )

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




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


    lowercase__		       =						torch.__version__
    lowercase__		       =						torch.cuda.is_available()
    lowercase__		       =						is_xpu_available()
    lowercase__		       =						is_npu_available()

    lowercase__		       =						"Not found"
    # Get the default from the config file.
    if args.config_file is not None or os.path.isfile(_snake_case       ):
        lowercase__		       =						load_config_from_file(args.config_file       ).to_dict()

    lowercase__		       =						{
        "`Accelerate` version": version,
        "Platform": platform.platform(),
        "Python version": platform.python_version(),
        "Numpy version": np.__version__,
        "PyTorch version (GPU?)": f'''{pt_version} ({pt_cuda_available})''',
        "PyTorch XPU available": str(_snake_case       ),
        "PyTorch NPU available": str(_snake_case       ),
        "System RAM": f'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
    }
    if pt_cuda_available:
        lowercase__		       =						torch.cuda.get_device_name()

    print("\nCopy-and-paste the text below in your GitHub issue\n"       )
    print("\n".join([f'''- {prop}: {val}''' for prop, val in info.items()]       )       )

    print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"       )
    lowercase__		       =						(
        "\n".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]       )
        if isinstance(_snake_case     ,_snake_case       )
        else f'''\t{accelerate_config}'''
    )
    print(_snake_case       )

    lowercase__		       =						accelerate_config

    return info




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						env_command_parser()
    lowercase__		       =						parser.parse_args()
    env_command(_snake_case       )
    return 0


if __name__ == "__main__":
 raise SystemExit(main())




