





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       UNetaDModel
  a_							:       ScoreSdeVeScheduler







  def __init__(      self      :					Any      ,				UpperCamelCase      :					UNetaDModel      ,				UpperCamelCase      :					ScoreSdeVeScheduler							):


    '''simple docstring'''





    super().__init__()
    self.register_modules(unet=UpperCamelCase      ,				scheduler=UpperCamelCase							)







  @torch.no_grad()
  def __call__(      self      :					List[str]      ,				UpperCamelCase      :					int = 1      ,				UpperCamelCase      :					int = 20_00      ,				UpperCamelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,				UpperCamelCase      :					Optional[str] = "pil"      ,				UpperCamelCase      :					bool = True      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	self.unet.config.sample_size
    _snake_case   :				List[Any]														=	(batch_size, 3, img_size, img_size)

    _snake_case   :				List[str]														=	self.unet

    _snake_case   :				Tuple														=	randn_tensor(UpperCamelCase      ,				generator=UpperCamelCase							) * self.scheduler.init_noise_sigma
    _snake_case   :				List[str]														=	sample.to(self.device							)

    self.scheduler.set_timesteps(UpperCamelCase							)
    self.scheduler.set_sigmas(UpperCamelCase							)

    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
      _snake_case   :				Union[str, Any]														=	self.scheduler.sigmas[i] * torch.ones(shape[0]      ,				device=self.device							)

      # correction step
      for _ in range(self.scheduler.config.correct_steps							):
        _snake_case   :				Tuple														=	self.unet(UpperCamelCase      ,				UpperCamelCase							).sample
        _snake_case   :				List[str]														=	self.scheduler.step_correct(UpperCamelCase      ,				UpperCamelCase      ,				generator=UpperCamelCase							).prev_sample

      # prediction step
      _snake_case   :				Dict														=	model(UpperCamelCase      ,				UpperCamelCase							).sample
      _snake_case   :				int														=	self.scheduler.step_pred(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				generator=UpperCamelCase							)

      _snake_case							,					_snake_case   :				Any														=	output.prev_sample, output.prev_sample_mean

    _snake_case   :				List[str]														=	sample_mean.clamp(0      ,				1							)
    _snake_case   :				Any														=	sample.cpu().permute(0      ,				2      ,				3      ,				1							).numpy()
    if output_type == "pil":
      _snake_case   :				List[Any]														=	self.numpy_to_pil(UpperCamelCase							)

    if not return_dict:
      return (sample,)

    return ImagePipelineOutput(images=UpperCamelCase							)











# 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


lowerCAmelCase_						   =    """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     lowerCamelCase_	(      )->		Tuple:
  _snake_case   :				int														=	_ask_options(
      'In which compute environment are you running?'    ,							['This machine', 'AWS (Amazon SageMaker)']    ,							_convert_compute_environment    ,							)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    _snake_case   :				int														=	get_sagemaker_input()
  else:
    _snake_case   :				Any														=	get_cluster_input()
  return config


def     lowerCamelCase_	(      lowerCAmelCase:	str=None				)->		Any:
  if subparsers is not None:
    _snake_case   :				List[Any]														=	subparsers.add_parser('config'    ,							description=lowerCAmelCase				)
  else:
    _snake_case   :				Dict														=	argparse.ArgumentParser('Accelerate config command'    ,							description=lowerCAmelCase				)

  parser.add_argument(
      '--config_file'    ,							default=lowerCAmelCase    ,							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=lowerCAmelCase				)
  return parser


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Any:
  _snake_case   :				Dict														=	get_user_input()
  if args.config_file is not None:
    _snake_case   :				List[str]														=	args.config_file
  else:
    if not os.path.isdir(lowerCAmelCase				):
      os.makedirs(lowerCAmelCase				)
    _snake_case   :				Union[str, Any]														=	default_yaml_config_file

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


def     lowerCamelCase_	(      )->		Dict:
  _snake_case   :				List[str]														=	config_command_parser()
  _snake_case   :				str														=	parser.parse_args()
  config_command(lowerCAmelCase				)


if __name__ == "__main__":
      main()




