


'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase=""       ,			__lowercase="train"		):
										assert os.path.isdir(__lowercase		)

										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				os.listdir(__lowercase		)
										for story_filename in story_filenames_list:
																if "summary" in story_filename:
																						continue
																__lowerCAmelCase							       =				os.path.join(__lowercase       ,			__lowercase		)
																if not os.path.isfile(__lowercase		):
																						continue
																self.documents.append(__lowercase		)




				def __len__(self		):
										return len(self.documents		)




				def __getitem__(self       ,			__lowercase		):
										__lowerCAmelCase							       =				self.documents[idx]
										__lowerCAmelCase							       =				document_path.split('''/'''		)[-1]
										with open(__lowercase       ,			encoding='''utf-8'''		) as source:
																__lowerCAmelCase							       =				source.read()
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				process_story(__lowercase		)
										return document_name, story_lines, summary_lines

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				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
						__lowerCAmelCase							       =				[_add_missing_period(lowerCamelCase) for line in nonempty_lines]

						# gather article lines
						__lowerCAmelCase							       =				[]
						__lowerCAmelCase							       =				deque(lowerCamelCase)
						while True:
												try:
																		__lowerCAmelCase							       =				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
						__lowerCAmelCase							       =				list(filter(lambda lowerCamelCase: not t.startswith('''@highlight'''),				lowerCamelCase))

						return story_lines, summary_lines

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
						if line.startswith('''@highlight'''):
												return line
						if line[-1] in END_TOKENS:
												return line
						return line + "."

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						if len(lowerCamelCase) > block_size:
												return sequence[:block_size]
						else:
												sequence.extend([pad_token_id] * (block_size - len(lowerCamelCase)))
												return sequence

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				torch.ones_like(lowerCamelCase)
						__lowerCAmelCase							       =				sequence == pad_token_id
						__lowerCAmelCase							       =				0
						return mask

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				[tokenizer.encode(lowerCamelCase) for line in story_lines]
						__lowerCAmelCase							       =				[token for sentence in story_lines_token_ids for token in sentence]
						__lowerCAmelCase							       =				[tokenizer.encode(lowerCamelCase) for line in summary_lines]
						__lowerCAmelCase							       =				[token for sentence in summary_lines_token_ids for token in sentence]

						return story_token_ids, summary_token_ids




def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				[]
						for sequence in batch:
												__lowerCAmelCase							       =				-1
												__lowerCAmelCase							       =				[]
												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)







'''simple docstring'''

import numpy as np

from transformers import Pipeline

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				np.max(lowerCamelCase,				axis=-1,				keepdims=lowerCamelCase)
						__lowerCAmelCase							       =				np.exp(outputs - maxes)
						return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=lowerCamelCase)




class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{}
										if "second_text" in kwargs:
																__lowerCAmelCase							       =				kwargs['''second_text''']
										return preprocess_kwargs, {}, {}




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										return self.tokenizer(__lowercase       ,			text_pair=__lowercase       ,			return_tensors=self.framework		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.model(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				model_outputs.logits[0].numpy()
										__lowerCAmelCase							       =				softmax(__lowercase		)

										__lowerCAmelCase							       =				np.argmax(__lowercase		)
										__lowerCAmelCase							       =				self.model.config.idalabel[best_class]
										__lowerCAmelCase							       =				probabilities[best_class].item()
										__lowerCAmelCase							       =				logits.tolist()
										return {"label": label, "score": score, "logits": logits}

