



"""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       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     1



			@register_to_config
			def __init__(			self							:				Optional[Any]   ,	_lowercase							:				int = 1000   ,	_lowercase							:				Optional[Union[np.ndarray, List[float]]] = None		)							->					Optional[Any]:
									# set `betas`, `alphas`, `timesteps`
									self.set_timesteps(_lowercase		)

									# standard deviation of the initial noise distribution
									snake_case   :			Union[str, Any]						    = 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.
									snake_case   :			int						    = 4

									# running values
									snake_case   :			int						    = []



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				int   ,	_lowercase							:				Union[str, torch.device] = None		)							->					List[Any]:
									snake_case   :			Optional[Any]						    = num_inference_steps
									snake_case   :			Dict						    = torch.linspace(1   ,	0   ,	num_inference_steps + 1		)[:-1]
									snake_case   :			List[Any]						    = torch.cat([steps, torch.tensor([0.0]		)]		)

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

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

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

									snake_case   :			Optional[Any]						    = []



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				torch.FloatTensor   ,	_lowercase							:				int   ,	_lowercase							:				torch.FloatTensor   ,	_lowercase							:				bool = True   ,	)							->					Union[SchedulerOutput, Tuple]:
									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"		)

									snake_case   :			List[str]						    = (self.timesteps == timestep).nonzero().item()
									snake_case   :			List[Any]						    = timestep_index + 1

									snake_case   :			Optional[Any]						    = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(_lowercase		)

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

									snake_case   :			Tuple						    = self._get_prev_sample(_lowercase   ,	_lowercase   ,	_lowercase   ,	_lowercase		)

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=_lowercase		)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				torch.FloatTensor   ,	*_lowercase							:				Optional[int]   ,	**_lowercase							:				Tuple		)							->					torch.FloatTensor:
									return sample



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				List[str]		)							->					Optional[Any]:
									snake_case   :			Union[str, Any]						    = self.alphas[timestep_index]
									snake_case   :			str						    = self.betas[timestep_index]

									snake_case   :			Optional[int]						    = self.alphas[prev_timestep_index]
									snake_case   :			int						    = self.betas[prev_timestep_index]

									snake_case   :			Any						    = (sample - sigma * ets) / max(_lowercase   ,	1E-8		)
									snake_case   :			Optional[Any]						    = next_alpha * pred + ets * next_sigma

									return prev_sample



			def __len__(			self							:				str		)							->					str:
									return self.config.num_train_timesteps








"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. 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 re

from ..utils import cached_file


# docstyle-ignore
A		=						'\nHuman: <<task>>\n\nAssistant: '


A		=						'huggingface-tools/default-prompts'
A		=						{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

						# prompt is considered a repo ID when it does not contain any kind of space
						if re.search("\\s"     ,		lowerCamelCase_ ) is not None:
												return prompt_or_repo_id

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



