


'''simple docstring'''






import os
from collections import deque

import torch
from torch.utils.data import Dataset




class 						a__							(				a__    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=""   ,       lowerCamelCase_="train"  )			->				Optional[int]:
														assert os.path.isdir(lowerCamelCase_  )

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







							def __len__(						self  )			->				List[Any]:
														return len(self.documents  )







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


def    _snake_case  (	A			)		->       Optional[int]:
							lowerCAmelCase__			  =      list(filter(lambda A			: len(A			) != 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(A			) for line in nonempty_lines]

							# gather article lines
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      deque(A			)
							while True:
														try:
																					lowerCAmelCase__			  =      lines.popleft()
																					if element.startswith('''@highlight'''			):
																												break
																					story_lines.append(A			)
														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 A			: not t.startswith('''@highlight'''			)					,       A			)			)

							return story_lines, summary_lines


def    _snake_case  (	A			)		->       Dict:
							lowerCAmelCase__			  =      ['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
							if line.startswith('''@highlight'''			):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."


def    _snake_case  (	A					,       A					,       A			)		->       str:
							if len(A			) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(A			))			)
														return sequence


def    _snake_case  (	A					,       A			)		->       str:
							lowerCAmelCase__			  =      torch.ones_like(A			)
							lowerCAmelCase__			  =      sequence == pad_token_id
							lowerCAmelCase__			  =      0
							return mask


def    _snake_case  (	A					,       A					,       A			)		->       Dict:
							lowerCAmelCase__			  =      [tokenizer.encode(A			) for line in story_lines]
							lowerCAmelCase__			  =      [token for sentence in story_lines_token_ids for token in sentence]
							lowerCAmelCase__			  =      [tokenizer.encode(A			) 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    _snake_case  (	A					,       A			)		->       List[Any]:
							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(A			)
							return torch.tensor(A			)



'''simple docstring'''






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def    _snake_case  (	)		->       Union[str, Any]:
							raise RuntimeError('''CUDA out of memory.'''			)




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							def __init__(						self  )			->				int:
														super().__init__()
														lowerCAmelCase__			  =      nn.Linear(3   ,       4  )
														lowerCAmelCase__			  =      nn.BatchNormad(4  )
														lowerCAmelCase__			  =      nn.Linear(4   ,       5  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_  )  )  )




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()

														mock_training_loop_function()
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()
																					return batch_size, arga

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      mock_training_loop_function('''hello'''  )
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )
														self.assertListEqual([bs, arga]   ,       [8, '''hello''']  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														@find_executable_batch_size(starting_batch_size=0  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					if batch_size > 0:
																												raise_fake_out_of_memory()
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					if batch_size != 8:
																												raise raise_fake_out_of_memory()

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function(1_28   ,       '''hello'''   ,       '''world'''  )
																					self.assertIn('''Batch size was passed into `f`'''   ,       cm.exception.args[0]  )
																					self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					raise ValueError('''Oops, we had an error!'''  )

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''Oops, we had an error!'''   ,       cm.exception.args[0]  )







							@require_cuda
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      torch.cuda.memory_allocated()
														lowerCAmelCase__			  =      ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      release_memory(lowerCamelCase_  )
														self.assertEqual(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )