



"""simple docstring"""
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_						(						a_     ,a_				):
							__lowerCAmelCase    					=					1






							@register_to_config
							def __init__(   self			,     a_ = 1_0_0_0			,     a_ = None      ):
									# set `betas`, `alphas`, `timesteps`
									self.set_timesteps(a_      )

									# standard deviation of the initial noise distribution
									a_     :      Dict 						=     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_     :      List[Any] 						=     4

									# running values
									a_     :      Optional[int] 						=     []






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      Any 						=     num_inference_steps
									a_     :      Tuple 						=     torch.linspace(1			,     0			,     num_inference_steps + 1      )[:-1]
									a_     :      Optional[Any] 						=     torch.cat([steps, torch.tensor([0.0]      )]      )

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

									a_     :      List[Any] 						=     (1.0 - self.betas**2) ** 0.5

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

									a_     :      Dict 						=     []






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_ = True			,     ):
									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_     :      Any 						=     (self.timesteps == timestep).nonzero().item()
									a_     :      List[Any] 						=     timestep_index + 1

									a_     :      Union[str, Any] 						=     sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(a_      )

									if len(self.ets      ) == 1:
											a_     :      int 						=     self.ets[-1]
									elif len(self.ets      ) == 2:
											a_     :      Optional[int] 						=     (3 * self.ets[-1] - self.ets[-2]) / 2
									elif len(self.ets      ) == 3:
											a_     :      int 						=     (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
									else:
											a_     :      List[Any] 						=     (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

									a_     :      Tuple 						=     self._get_prev_sample(a_			,     a_			,     a_			,     a_      )

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=a_      )






							def snake_case_  (   self			,     a_			,     *a_			,     **a_      ):
									return sample






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      Union[str, Any] 						=     self.alphas[timestep_index]
									a_     :      int 						=     self.betas[timestep_index]

									a_     :      Any 						=     self.alphas[prev_timestep_index]
									a_     :      Union[str, Any] 						=     self.betas[prev_timestep_index]

									a_     :      str 						=     (sample - sigma * ets) / max(a_			,     1e-8      )
									a_     :      Union[str, Any] 						=     next_alpha * pred + ets * next_sigma

									return prev_sample






							def __len__(   self      ):
									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_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		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_     :      Optional[int] 						=     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_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, 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=SCREAMING_SNAKE_CASE__, 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=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)