






import os
from collections import deque

import torch
from torch.utils.data import Dataset

class 		lowerCAmelCase     ( lowerCamelCase__			):

					'''simple docstring'''

					def __init__(  self				:	Dict		,     __a				:	Dict=""		,     __a				:	Optional[int]="train"							)			->  str:


												"""simple docstring"""

												assert os.path.isdir(__lowerCamelCase							)

												__lowercase      :							Union[str, Any]				=     []
												__lowercase      :							List[str]				=     os.listdir(__lowerCamelCase							)
												for story_filename in story_filenames_list:
																			if "summary" in story_filename:
																										continue
																			__lowercase      :							int				=     os.path.join(__lowerCamelCase		,     __lowerCamelCase							)
																			if not os.path.isfile(__lowerCamelCase							):
																										continue
																			self.documents.append(__lowerCamelCase							)



					def __len__(  self				:	Tuple							)			->  int:


												"""simple docstring"""

												return len(self.documents							)



					def __getitem__(  self				:	str		,     __a				:	Dict							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.documents[idx]
												__lowercase      :							str				=     document_path.split("""/"""							)[-1]
												with open(__lowerCamelCase		,     encoding="""utf-8"""							) as source:
																			__lowercase      :							Dict				=     source.read()
																			__lowercase      :							Union[str, Any]				=     process_story(__lowerCamelCase							)
												return document_name, story_lines, summary_lines







def       snake_case_		(     lowerCAmelCase_       :						Optional[int]      ):
							__lowercase      :							Any				=     list(filter(lambda lowerCAmelCase_      : len(lowerCamelCase_      ) != 0       ,       [line.strip() for line in raw_story.split("""\n"""      )]      )      )

							# for some unknown reason some lines miss a period, add it
							__lowercase      :							Optional[int]				=     [_add_missing_period(lowerCamelCase_      ) for line in nonempty_lines]

							# gather article lines
							__lowercase      :							Tuple				=     []
							__lowercase      :							Tuple				=     deque(lowerCamelCase_      )
							while True:
														try:
																					__lowercase      :							Dict				=     lines.popleft()
																					if element.startswith("""@highlight"""      ):
																												break
																					story_lines.append(lowerCamelCase_      )
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							__lowercase      :							Any				=     list(filter(lambda lowerCAmelCase_      : not t.startswith("""@highlight"""      )       ,       lowerCamelCase_      )      )

							return story_lines, summary_lines
def       snake_case_		(     lowerCAmelCase_       :						int      ):
							__lowercase      :							Tuple				=     ['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
							if line.startswith("""@highlight"""      ):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."
def       snake_case_		(     lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Dict      ):

							if len(lowerCamelCase_      ) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCamelCase_      ))      )
														return sequence
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						Optional[Any]      ):
							__lowercase      :							List[Any]				=     torch.ones_like(lowerCamelCase_      )
							__lowercase      :							Any				=     sequence == pad_token_id
							__lowercase      :							List[str]				=     0
							return mask
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							Optional[Any]				=     [tokenizer.encode(lowerCamelCase_      ) for line in story_lines]
							__lowercase      :							Union[str, Any]				=     [token for sentence in story_lines_token_ids for token in sentence]
							__lowercase      :							Tuple				=     [tokenizer.encode(lowerCamelCase_      ) for line in summary_lines]
							__lowercase      :							List[Any]				=     [token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     []
							for sequence in batch:
														__lowercase      :							str				=     -1
														__lowercase      :							Tuple				=     []
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2      )
														batch_embeddings.append(lowerCamelCase_      )
							return torch.tensor(lowerCamelCase_      )







# 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 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												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
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps