




import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
 import torch

a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	)							->						str:

    """simple docstring"""


    return importlib.util.find_spec('''deepspeed'''   ) is not None


if is_accelerate_available() and is_deepspeed_available():
 from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
 # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
 # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
 from builtins import object as DeepSpeedConfig

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			Optional[int]			,							snake_case       :			Optional[Any]					):
          # set global weakref object
          set_hf_deepspeed_config(self					)
          dep_version_check('''accelerate'''					)
          dep_version_check('''deepspeed'''					)
          super().__init__(snake_case					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			List[Any]			,							snake_case       :			Dict					):
          super().__init__(snake_case					)
          __UpperCamelCase			=    None
          __UpperCamelCase			=    []







      def    snake_case						(  self       :			Dict					):
          if self._dtype is None:
              raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''					)
          return self._dtype







      def    snake_case						(  self       :			str			,							snake_case       :			int					):
          __UpperCamelCase			=    self.get_value(snake_case					)
          if val is None:
              return False
          else:
              return val == "auto"







      def    snake_case						(  self       :			str			,							snake_case       :			str			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[int]=None			,							snake_case       :			Any=True					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.find_config_node(snake_case					)
          if config is None:
              return

          if config.get(snake_case					) == "auto":
              __UpperCamelCase			=    hf_val
              return

          if not must_match:
              return

          __UpperCamelCase			=    config.get(snake_case					)
          if ds_val is not None and ds_val != hf_val:
              self.mismatches.append(F"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"					)

      lowerCAmelCase__      :			List[str]     =       partialmethod(_SCREAMING_SNAKE_CASE				,      must_match=_SCREAMING_SNAKE_CASE )







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			Optional[Any]					):
          __UpperCamelCase			=    args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
          self.fill_match(
              '''train_micro_batch_size_per_gpu'''			,							args.per_device_train_batch_size			,							'''per_device_train_batch_size'''					)
          self.fill_match('''gradient_accumulation_steps'''			,							args.gradient_accumulation_steps			,							'''gradient_accumulation_steps'''					)
          self.fill_match('''train_batch_size'''			,							snake_case			,							'''train_batch_size (calculated)'''					)
          self.fill_match('''gradient_clipping'''			,							args.max_grad_norm			,							'''max_grad_norm'''					)

          self.fill_match('''optimizer.params.lr'''			,							args.learning_rate			,							'''learning_rate'''					)
          self.fill_match('''optimizer.params.betas'''			,							[args.adam_betaa, args.adam_betaa]			,							'''adam_beta1+adam_beta2'''					)
          self.fill_match('''optimizer.params.eps'''			,							args.adam_epsilon			,							'''adam_epsilon'''					)
          self.fill_match('''optimizer.params.weight_decay'''			,							args.weight_decay			,							'''weight_decay'''					)

          self.fill_only('''scheduler.params.warmup_min_lr'''			,							0					)  # not a trainer arg
          self.fill_match('''scheduler.params.warmup_max_lr'''			,							args.learning_rate			,							'''learning_rate'''					)
          # total_num_steps - will get set in trainer_config_finalize

          # fp16
          if args.fpaa or args.fpaa_full_eval:
              __UpperCamelCase			=    '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
          else:
              __UpperCamelCase			=    None

          if args.save_on_each_node:
              # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
              __UpperCamelCase			=    self.config.get('''checkpoint'''			,							{}					)
              __UpperCamelCase			=    args.save_on_each_node

          # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
          # any here unless the user did the work
          self.fill_match(
              '''fp16.enabled'''			,							((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')			,							'''fp16|fp16_full_eval+fp16_backend(amp)'''			,							)

          # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
          # ZeRO features
          self.fill_match('''amp.enabled'''			,							fpaa_backend == '''apex'''			,							'''fp16+fp16_backend(apex)'''					)
          self.fill_match('''amp.opt_level'''			,							args.fpaa_opt_level			,							'''fp16_opt_level'''					)

          self.fill_match('''bf16.enabled'''			,							(args.bfaa or args.bfaa_full_eval)			,							'''bf16|bf16_full_eval'''					)

          # deepspeed's default mode is fp16 unless there is a config that says differently
          if self.is_true('''bf16.enabled'''					):
              __UpperCamelCase			=    torch.bfloataa
          elif self.is_false('''fp16.enabled'''					):
              __UpperCamelCase			=    torch.floataa
          else:
              __UpperCamelCase			=    torch.floataa







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[Any]			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[int]					):
          __UpperCamelCase			=    [
              '''zero_optimization.reduce_bucket_size''',
              '''zero_optimization.stage3_prefetch_bucket_size''',
              '''zero_optimization.stage3_param_persistence_threshold''',
          ]
          __UpperCamelCase			=    [x for x in hidden_size_based_keys if self.is_auto(snake_case					)]

          if len(snake_case					) > 0:
              if hasattr(model.config			,							'''hidden_size'''					):
                  __UpperCamelCase			=    model.config.hidden_size
              elif hasattr(model.config			,							'''hidden_sizes'''					):
                  # if there are many hidden sizes pick the largest one
                  __UpperCamelCase			=    max(model.config.hidden_sizes					)
              else:
                  raise ValueError(
                      '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                      '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                      F"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
                      '''`auto` values for these keys with an integer value of your choice.'''					)

              self.fill_only('''zero_optimization.reduce_bucket_size'''			,							hidden_size * hidden_size					)
              if self.is_zeroa():
                  # automatically assign the optimal config values based on model config
                  self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''			,							0.9 * hidden_size * hidden_size					)
                  self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''			,							10 * hidden_size					)

        # scheduler
          self.fill_match('''scheduler.params.total_num_steps'''			,							snake_case			,							'''num_training_steps (calculated)'''					)
          self.fill_match('''scheduler.params.warmup_num_steps'''			,							args.get_warmup_steps(snake_case					)			,							'''warmup_steps'''					)

          if len(self.mismatches					) > 0:
              __UpperCamelCase			=    '''\n'''.join(self.mismatches					)
              raise ValueError(
                  '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                  F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."					)







a_ 		=  None




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						str:

    """simple docstring"""


    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    __UpperCamelCase			=    weakref.ref(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    global _hf_deepspeed_config_weak_ref
    __UpperCamelCase			=    None




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[Any]:

    """simple docstring"""


    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[Any]:

    """simple docstring"""


    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    from accelerate.utils import DummyOptim, DummyScheduler

    __UpperCamelCase			=    hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    __UpperCamelCase			=    None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                '''Only one optimizer can be configured.'''   )
        __UpperCamelCase			=    DummyOptim(params=lowercase_   )
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''   )

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        __UpperCamelCase			=    trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        __UpperCamelCase			=    True

    __UpperCamelCase			=    None
    if "scheduler" in config:
        __UpperCamelCase			=    DummyScheduler(lowercase_   )
    else:
        if isinstance(lowercase_       ,  lowercase_   ):
            raise ValueError(
                '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                '''Please configure a scheduler in the DeepSpeed config.'''   )
        __UpperCamelCase			=    trainer.create_scheduler(num_training_steps=lowercase_       ,  optimizer=lowercase_   )

    return optimizer, lr_scheduler




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


    from deepspeed.utils import logger as ds_logger

    __UpperCamelCase			=    trainer.model
    __UpperCamelCase			=    trainer.args

    __UpperCamelCase			=    trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(lowercase_       ,  lowercase_       ,  lowercase_   )

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()   )

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''   )

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree('''optimizer'''   )
        hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''   )
        __UpperCamelCase		,   __UpperCamelCase			=    None, None
        __UpperCamelCase			=    None
    else:
        __UpperCamelCase			=    None  # important for when deepspeed_init is used as re-init
        __UpperCamelCase			=    list(filter(lambda lowercase_   : p.requires_grad       ,  model.parameters()   )   )
        __UpperCamelCase		,   __UpperCamelCase			=    deepspeed_optim_sched(
            lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    import glob

    __UpperCamelCase			=    sorted(glob.glob(F"{checkpoint_path}/global_step*"   )   )

    if len(lowercase_   ) > 0:
        logger.info(F"Attempting to resume from {checkpoint_path}"   )
        # this magically updates self.optimizer and self.lr_scheduler
        __UpperCamelCase		,   __UpperCamelCase			=    deepspeed_engine.load_checkpoint(
            lowercase_       ,  load_optimizer_states=lowercase_       ,  load_lr_scheduler_states=lowercase_   )
        if load_path is None:
            raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"   )
    else:
        raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"   )












import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


UpperCamelCase__            =     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


UpperCamelCase__            =     namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])







def       _a (		SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			BertTokenizer.from_pretrained("bert-base-uncased" , do_lower_case=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"	)
					model.to(args.device	)
					model.eval()

					__lowerCAmelCase							  =			{
					    "BOS": tokenizer.vocab["[unused0]"],
					    "EOS": tokenizer.vocab["[unused1]"],
					    "PAD": tokenizer.vocab["[PAD]"],
					}

					if args.compute_rouge:
										__lowerCAmelCase							  =			[]
										__lowerCAmelCase							  =			[]

										import nltk
										import rouge

										nltk.download("punkt"	)
										__lowerCAmelCase							  =			rouge.Rouge(
										    metrics=["rouge-n", "rouge-l"] , max_n=2 , limit_length=SCREAMING_SNAKE_CASE_ , length_limit=args.beam_size , length_limit_type="words" , apply_avg=SCREAMING_SNAKE_CASE_ , apply_best=SCREAMING_SNAKE_CASE_ , alpha=0.5 , weight_factor=1.2 , stemming=SCREAMING_SNAKE_CASE_ , )

					# these (unused) arguments are defined to keep the compatibility
					# with the legacy code and will be deleted in a next iteration.
					__lowerCAmelCase							  =			""
					__lowerCAmelCase							  =			""

					__lowerCAmelCase							  =			build_data_iterator(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			build_predictor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					logger.info("***** Running evaluation *****"	)
					logger.info("  Number examples = %d" , len(data_iterator.dataset	)	)
					logger.info("  Batch size = %d" , args.batch_size	)
					logger.info(""	)
					logger.info("***** Beam Search parameters *****"	)
					logger.info("  Beam size = %d" , args.beam_size	)
					logger.info("  Minimum length = %d" , args.min_length	)
					logger.info("  Maximum length = %d" , args.max_length	)
					logger.info("  Alpha (length penalty) = %.2f" , args.alpha	)
					logger.info("  Trigrams %s be blocked" , ("will" if args.block_trigram else "will NOT")	)

					for batch in tqdm(SCREAMING_SNAKE_CASE_	):
										__lowerCAmelCase							  =			predictor.translate_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			predictor.from_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			[format_summary(SCREAMING_SNAKE_CASE_	) for t in translations]
										save_summaries(SCREAMING_SNAKE_CASE_ , args.summaries_output_dir , batch.document_names	)

										if args.compute_rouge:
															reference_summaries += batch.tgt_str
															generated_summaries += summaries

					if args.compute_rouge:
										__lowerCAmelCase							  =			rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			format_rouge_scores(SCREAMING_SNAKE_CASE_	)
										save_rouge_scores(SCREAMING_SNAKE_CASE_	)
										print(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							Dict	):
					for summary, document_name in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	):
										# Prepare the summary file's name
										if "." in document_name:
															__lowerCAmelCase							  =			".".join(document_name.split("."	)[:-1]	)
															__lowerCAmelCase							  =			document_name.split("."	)[-1]
															__lowerCAmelCase							  =			bare_document_name + "_summary." + extension
										else:
															__lowerCAmelCase							  =			document_name + "_summary"

										__lowerCAmelCase							  =			os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										with open(SCREAMING_SNAKE_CASE_ , "w"	) as output:
															output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			translation
					__lowerCAmelCase							  =			(
					    raw_summary.replace("[unused0]" , ""	)
					    .replace("[unused3]" , ""	)
					    .replace("[PAD]" , ""	)
					    .replace("[unused1]" , ""	)
					    .replace(R" +" , " "	)
					    .replace(" [unused2] " , ". "	)
					    .replace("[unused2]" , ""	)
					    .strip()
					)

					return summary







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
					    scores["rouge-1"]["f"] , scores["rouge-1"]["p"] , scores["rouge-1"]["r"] , scores["rouge-2"]["f"] , scores["rouge-2"]["p"] , scores["rouge-2"]["r"] , scores["rouge-l"]["f"] , scores["rouge-l"]["p"] , scores["rouge-l"]["r"] , )







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					with open("rouge_scores.txt" , "w"	) as output:
										output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase							  =			load_and_cache_examples(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			SequentialSampler(SCREAMING_SNAKE_CASE_	)

					def collate_fn(SCREAMING_SNAKE_CASE_				:							List[Any]	):
										return collate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , block_size=5_12 , device=args.device	)

					__lowerCAmelCase							  =			DataLoader(
					    SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.batch_size , collate_fn=SCREAMING_SNAKE_CASE_ , )

					return iterator







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							List[Any]	):
					__lowerCAmelCase							  =			CNNDMDataset(args.documents_dir	)
					return dataset







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			[x for x in data if not len(x[1]	) == 0]  # remove empty_files
					__lowerCAmelCase							  =			[name for name, _, _ in data]
					__lowerCAmelCase							  =			[" ".join(SCREAMING_SNAKE_CASE_	) for _, _, summary_list in data]

					__lowerCAmelCase							  =			[encode_for_summarization(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) for _, story, summary in data]
					__lowerCAmelCase							  =			torch.tensor(
					    [truncate_or_pad(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer.pad_token_id	) for story, _ in encoded_text]	)
					__lowerCAmelCase							  =			compute_token_type_ids(SCREAMING_SNAKE_CASE_ , tokenizer.cls_token_id	)
					__lowerCAmelCase							  =			build_mask(SCREAMING_SNAKE_CASE_ , tokenizer.pad_token_id	)

					__lowerCAmelCase							  =			Batch(
					    document_names=SCREAMING_SNAKE_CASE_ , batch_size=len(SCREAMING_SNAKE_CASE_	) , src=encoded_stories.to(SCREAMING_SNAKE_CASE_	) , segs=encoder_token_type_ids.to(SCREAMING_SNAKE_CASE_	) , mask_src=encoder_mask.to(SCREAMING_SNAKE_CASE_	) , tgt_str=SCREAMING_SNAKE_CASE_ , )

					return batch







def       _a (		SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					__lowerCAmelCase							  =			summary_tokens.to("cpu"	).numpy()
					__lowerCAmelCase							  =			tokenizer.decode(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			summary.split("."	)
					__lowerCAmelCase							  =			[s + "." for s in sentences]
					return sentences







def       _a (		):
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument(
					    "--documents_dir" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The folder where the documents to summarize are located." , )
					parser.add_argument(
					    "--summaries_output_dir" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The folder in wich the summaries should be written. Defaults to the folder where the documents are" , )
					parser.add_argument(
					    "--compute_rouge" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset." , )
					# EVALUATION options
					parser.add_argument(
					    "--no_cuda" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Whether to force the execution on CPU." , )
					parser.add_argument(
					    "--batch_size" , default=4 , type=SCREAMING_SNAKE_CASE_ , help="Batch size per GPU/CPU for training." , )
					# BEAM SEARCH arguments
					parser.add_argument(
					    "--min_length" , default=50 , type=SCREAMING_SNAKE_CASE_ , help="Minimum number of tokens for the summaries." , )
					parser.add_argument(
					    "--max_length" , default=2_00 , type=SCREAMING_SNAKE_CASE_ , help="Maixmum number of tokens for the summaries." , )
					parser.add_argument(
					    "--beam_size" , default=5 , type=SCREAMING_SNAKE_CASE_ , help="The number of beams to start with for each example." , )
					parser.add_argument(
					    "--alpha" , default=0.95 , type=SCREAMING_SNAKE_CASE_ , help="The value of alpha for the length penalty in the beam search." , )
					parser.add_argument(
					    "--block_trigram" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Whether to block the existence of repeating trigrams in the text generated by beam search." , )
					__lowerCAmelCase							  =			parser.parse_args()

					# Select device (distibuted not available)
					__lowerCAmelCase							  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"	)

					# Check the existence of directories
					if not args.summaries_output_dir:
										__lowerCAmelCase							  =			args.documents_dir

					if not documents_dir_is_valid(args.documents_dir	):
										raise FileNotFoundError(
										    "We could not find the directory you specified for the documents to summarize, or it was empty. Please"
										    " specify a valid path."	)
					os.makedirs(args.summaries_output_dir , exist_ok=SCREAMING_SNAKE_CASE_	)

					evaluate(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					if not os.path.exists(SCREAMING_SNAKE_CASE_	):
										return False

					__lowerCAmelCase							  =			os.listdir(SCREAMING_SNAKE_CASE_	)
					if len(SCREAMING_SNAKE_CASE_	) == 0:
										return False

					return True


if __name__ == "__main__":
					main()




