




"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class      UpperCAmelCase_  (						A_,       A_							):
    lowercase__						 =				1




    @register_to_config
    def __init__(   self						:	List[Any]     ,			snake_case_						:	Dict=2_000     ,			snake_case_						:	List[str]=0.1     ,			snake_case_						:	Tuple=20     ,			snake_case_						:	Any=1e-3				)				->							str:




     '''simple docstring'''

     A__	=    None
     A__	=    None
     A__	=    None




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	str     ,			snake_case_						:	Union[str, torch.device] = None				)				->							Any:




     '''simple docstring'''

     A__	=    torch.linspace(1     ,			self.config.sampling_eps     ,			snake_case_     ,			device=snake_case_				)




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict=None				)				->							List[str]:




     '''simple docstring'''

     if self.timesteps is None:
      raise ValueError(
          "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

     # TODO(Patrick) better comments + non-PyTorch
     # postprocess model score
     A__	=    (
         -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     A__	=    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
     A__	=    std.flatten()
     while len(std.shape				) < len(score.shape				):
      A__	=    std.unsqueeze(-1				)
     A__	=    -score / std

     # compute
     A__	=    -1.0 / len(self.timesteps				)

     A__	=    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
     A__	=    beta_t.flatten()
     while len(beta_t.shape				) < len(x.shape				):
      A__	=    beta_t.unsqueeze(-1				)
     A__	=    -0.5 * beta_t * x

     A__	=    torch.sqrt(snake_case_				)
     A__	=    drift - diffusion**2 * score
     A__	=    x + drift * dt

     # add noise
     A__	=    randn_tensor(x.shape     ,			layout=x.layout     ,			generator=snake_case_     ,			device=x.device     ,			dtype=x.dtype				)
     A__	=    x_mean + diffusion * math.sqrt(-dt				) * noise

     return x, x_mean




    def __len__(   self						:	Dict				)				->							Optional[int]:




     '''simple docstring'''

     return self.config.num_train_timesteps











"""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   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

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

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

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

 A__	=    {
     "`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(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    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:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


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





