"""simple docstring"""



import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		):
							# Get the sagemaker specific mp parameters from smp_options variable.
							A__       :     Union[str, Any]								=			os.getenv("""SM_HP_MP_PARAMETERS"""			,  """{}"""							)
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														A__       :     Any								=			json.loads(lowerCAmelCase							)
														if "partitions" not in smp_options:
																					return False
							except json.JSONDecodeError:
														return False

							# Get the sagemaker specific framework parameters from mpi_options variable.
							A__       :     int								=			os.getenv("""SM_FRAMEWORK_PARAMS"""			,  """{}"""							)
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														A__       :     Union[str, Any]								=			json.loads(lowerCAmelCase							)
														if not mpi_options.get("""sagemaker_mpi_enabled"""			,  lowerCAmelCase							):
																					return False
							except json.JSONDecodeError:
														return False
							# Lastly, check if the `smdistributed` module is present.
							return importlib.util.find_spec("""smdistributed"""							) is not None


if is_sagemaker_model_parallel_available():
				import smdistributed.modelparallel.torch as smp

				smp.init()



@dataclass
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				field(
			    default=''							,   metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}							,   )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().__post_init__()
										warnings.warn(
										    """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
										    """`TrainingArguments` instead."""   ,      snake_case_   ,      )





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										logger.info("""PyTorch: setting up devices"""							)
										if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
																	logger.warning(
																	    """torch.distributed process group is initialized, but local_rank == -1. """
																	    """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""							)
										if self.no_cuda:
																	A__       :     Tuple								=			torch.device("""cpu"""							)
																	A__       :     Dict								=			0
										elif is_sagemaker_model_parallel_available():
																	A__       :     Any								=			smp.local_rank()
																	A__       :     Optional[Any]								=			torch.device("""cuda"""   ,      snake_case_							)
																	A__       :     Optional[int]								=			1
										elif is_sagemaker_dp_enabled():
																	import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

																	torch.distributed.init_process_group(backend="""smddp"""   ,      timeout=self.ddp_timeout_delta							)
																	A__       :     Any								=			int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""							)							)
																	A__       :     Union[str, Any]								=			torch.device("""cuda"""   ,      self.local_rank							)
																	A__       :     Optional[Any]								=			1
										elif self.local_rank == -1:
																	# if n_gpu is > 1 we'll use nn.DataParallel.
																	# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																	# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
																	# trigger an error that a device index is missing. Index 0 takes into account the
																	# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
																	# will use the first GPU in that env, i.e. GPU#1
																	A__       :     List[Any]								=			torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""							)
																	# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
																	# the default value.
																	A__       :     Optional[Any]								=			torch.cuda.device_count()
										else:
																	# Here, we'll use torch.distributed.
																	# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
																	if not torch.distributed.is_initialized():
																								torch.distributed.init_process_group(backend="""nccl"""   ,      timeout=self.ddp_timeout_delta							)
																	A__       :     Optional[Any]								=			torch.device("""cuda"""   ,      self.local_rank							)
																	A__       :     Any								=			1

										if device.type == "cuda":
																	torch.cuda.set_device(snake_case_							)

										return device





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										if is_sagemaker_model_parallel_available():
																	return smp.dp_size()

										return super().world_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return not is_sagemaker_model_parallel_available()





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return False






"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


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





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			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
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			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
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		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  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														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							)





