






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Any   			=					1


	@register_to_config
	def __init__(      self  ,					__a = 1000  ,					__a = None						):


					"""simple docstring"""

					self.set_timesteps(__a						)

					# standard deviation of the initial noise distribution
					A__							 =       1.0

					# For now we only support F-PNDM, i.e. the runge-kutta method
					# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
					# mainly at formula (9), (12), (13) and the Algorithm 2.
					A__							 =       4

					# running values
					A__							 =       []


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       num_inference_steps
					A__							 =       torch.linspace(1  ,					0  ,					num_inference_steps + 1						)[:-1]
					A__							 =       torch.cat([steps, torch.tensor([0.0]						)]						)

					if self.config.trained_betas is not None:
									A__							 =       torch.tensor(self.config.trained_betas  ,					dtype=torch.floataa						)
					else:
									A__							 =       torch.sin(steps * math.pi / 2						) ** 2

					A__							 =       (1.0 - self.betas**2) ** 0.5

					A__							 =       (torch.atana(self.betas  ,					self.alphas						) / math.pi * 2)[:-1]
					A__							 =       timesteps.to(__a						)

					A__							 =       []


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a = True  ,					):


					"""simple docstring"""

					if self.num_inference_steps is None:
									raise ValueError(
									    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

					A__							 =       (self.timesteps == timestep).nonzero().item()
					A__							 =       timestep_index + 1

					A__							 =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
					self.ets.append(__a						)

					if len(self.ets						) == 1:
									A__							 =       self.ets[-1]
					elif len(self.ets						) == 2:
									A__							 =       (3 * self.ets[-1] - self.ets[-2]) / 2
					elif len(self.ets						) == 3:
									A__							 =       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
					else:
									A__							 =       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

					A__							 =       self._get_prev_sample(__a  ,					__a  ,					__a  ,					__a						)

					if not return_dict:
									return (prev_sample,)

					return SchedulerOutput(prev_sample=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					*__a  ,					**__a						):


					"""simple docstring"""

					return sample


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       self.alphas[timestep_index]
					A__							 =       self.betas[timestep_index]

					A__							 =       self.alphas[prev_timestep_index]
					A__							 =       self.betas[prev_timestep_index]

					A__							 =       (sample - sigma * ets) / max(__a  ,					1E-8						)
					A__							 =       next_alpha * pred + ets * next_sigma

					return prev_sample






	def __len__(      self						):


					"""simple docstring"""

					return self.config.num_train_timesteps










"""simple docstring"""






# 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
								return False
				A__							 =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
				A__							 =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								A__							 =       torch.cuda.device_count()
								A__							 =       num_gpus
								A__							 =       False
								if num_gpus > 1:
												A__							 =       'MULTI_GPU'
								else:
												A__							 =       'NO'
				elif is_xpu_available() and use_xpu:
								A__							 =       torch.xpu.device_count()
								A__							 =       num_xpus
								A__							 =       False
								if num_xpus > 1:
												A__							 =       'MULTI_XPU'
								else:
												A__							 =       'NO'
				elif is_npu_available():
								A__							 =       torch.npu.device_count()
								A__							 =       num_npus
								A__							 =       False
								if num_npus > 1:
												A__							 =       'MULTI_NPU'
								else:
												A__							 =       'NO'
				else:
								A__							 =       0
								A__							 =       True
								A__							 =       1
								A__							 =       'NO'
				A__							 =       ClusterConfig(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=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\'.'
				    )       ,dest='save_location'       ,)

				parser.add_argument(
				    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=lowerCAmelCase__       ,help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
				parser.set_defaults(func=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


