


# 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  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					str					   =    1
					@register_to_config
					def __init__(   self     :int    ,__snake_case     :Any=20_00    ,__snake_case     :str=0.1    ,__snake_case     :List[str]=20    ,__snake_case     :Dict=1E-3   )      ->					List[str]:
												a__    =  None
												a__    =  None
												a__    =  None
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[Any]    ,__snake_case     :Union[str, torch.device] = None   )      ->					Tuple:
												a__    =  torch.linspace(1    ,self.config.sampling_eps    ,__snake_case    ,device=__snake_case   )
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Union[str, Any]    ,__snake_case     :Optional[Any]=None   )      ->					Union[str, Any]:
												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     :Union[str, Any]   )      ->					Optional[int]:
												return self.config.num_train_timesteps








# 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
snake_case							: Dict      =   '''
Human: <<task>>

Assistant: '''


snake_case							: Optional[int]      =   '''huggingface-tools/default-prompts'''
snake_case							: Tuple      =   {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       Any="run"     ):
							if prompt_or_repo_id is None:
														a__    =  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

							a__    =  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()




