






'''simple docstring'''

from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE__      =   [
    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
    ''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
    ''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
    '''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
    ''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
    ''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
    ''' body.''',
    '''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
    ''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
    ''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
    ''' punishment.''',
]

SCREAMING_SNAKE_CASE__      =   [
    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
    ''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
    ''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
    '''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
    ''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
    ''' Israelis .''',
    '''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
    ''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
    ''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
    ''' sentences up by 28% .''',
]







def     lowercase__ (      )-> Any:
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						bootstrap_aggregation=lowerCAmelCase_		,						rouge_keys=["""rouge2""", """rougeL"""]    )
      assert isinstance(lowerCAmelCase_		,						lowerCAmelCase_    )
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						bootstrap_aggregation=lowerCAmelCase_		,						rouge_keys=["""rouge2"""]    )
      assert (
          pd.DataFrame(no_aggregation["""rouge2"""]    ).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]    ).fmeasure.mean()
      )







def     lowercase__ (      )-> str:
      UpperCamelCase				  =					"""rougeLsum"""
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						newline_sep=lowerCAmelCase_		,						rouge_keys=[k]    )[k]
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						newline_sep=lowerCAmelCase_		,						rouge_keys=[k]    )[k]
      assert score > score_no_sep







def     lowercase__ (      )-> Dict:
      UpperCamelCase				  =					["""rouge1""", """rouge2""", """rougeL"""]
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						newline_sep=lowerCAmelCase_		,						rouge_keys=lowerCAmelCase_    )
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						newline_sep=lowerCAmelCase_		,						rouge_keys=lowerCAmelCase_    )
      assert score_sep == score_no_sep







def     lowercase__ (      )-> Dict:
      UpperCamelCase				  =					[
          """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
          """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
      ]
      UpperCamelCase				  =					[
          """Margot Frank, died in 1945, a month earlier than previously thought.""",
          """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
          """ the final seconds on board Flight 9525.""",
      ]
      assert calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						newline_sep=lowerCAmelCase_    ) == calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						newline_sep=lowerCAmelCase_    )







def     lowercase__ (      )-> Union[str, Any]:
      UpperCamelCase				  =					[
          """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
      ]
      UpperCamelCase				  =					[
          """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
      ]

      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						rouge_keys=["""rougeLsum"""]		,						newline_sep=lowerCAmelCase_    )["""rougeLsum"""]
      UpperCamelCase				  =					calculate_rouge(lowerCAmelCase_		,						lowerCAmelCase_		,						rouge_keys=["""rougeLsum"""]    )["""rougeLsum"""]
      assert new_score > prev_score







def     lowercase__ (      )-> Optional[Any]:
      UpperCamelCase				  =					Path("""examples/seq2seq/test_data/wmt_en_ro"""    )
      UpperCamelCase				  =					calculate_rouge_path(data_dir.joinpath("""test.source"""    )		,						data_dir.joinpath("""test.target"""    )    )
      assert isinstance(lowerCAmelCase_		,						lowerCAmelCase_    )
      UpperCamelCase				  =					calculate_rouge_path(
          data_dir.joinpath("""test.source"""    )		,						data_dir.joinpath("""test.target"""    )		,						bootstrap_aggregation=lowerCAmelCase_    )
      assert isinstance(lowerCAmelCase_		,						lowerCAmelCase_    )




"""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


a__				:							Union[str, Any]						=     logging.get_logger(__name__)




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				os.getenv("SM_HP_MP_PARAMETERS"  ,						"{}"   )
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										__SCREAMING_SNAKE_CASE						   =				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.
					__SCREAMING_SNAKE_CASE						   =				os.getenv("SM_FRAMEWORK_PARAMS"  ,						"{}"   )
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__SCREAMING_SNAKE_CASE						   =				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_	( UpperCamelCase):

	"""simple docstring"""





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




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Any:
						super().__post_init__()
						warnings.warn(
						    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
						    "`TrainingArguments` instead."			,			UpperCAmelCase__			,			)




	@cached_property
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	"torch.device":
						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:
											__SCREAMING_SNAKE_CASE						   =				torch.device("cpu"   )
											__SCREAMING_SNAKE_CASE						   =				0
						elif is_sagemaker_model_parallel_available():
											__SCREAMING_SNAKE_CASE						   =				smp.local_rank()
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				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   )
											__SCREAMING_SNAKE_CASE						   =				int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"   )   )
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			self.local_rank   )
											__SCREAMING_SNAKE_CASE						   =				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
											__SCREAMING_SNAKE_CASE						   =				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.
											__SCREAMING_SNAKE_CASE						   =				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   )
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			self.local_rank   )
											__SCREAMING_SNAKE_CASE						   =				1

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

						return device




	@property
	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Any:
						if is_sagemaker_model_parallel_available():
											return smp.dp_size()

						return super().world_size




	@property
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	List[Any]:
						return not is_sagemaker_model_parallel_available()




	@property
	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	int:
						return False
