"""simple docstring"""







import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[Any]    =    ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      assert Path(hparams.data_dir	).exists()
      __lowerCamelCase    		=					Path(hparams.output_dir	)
      self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.output_dir.joinpath('student'	)

      __lowerCamelCase    		=					str(_SCREAMING_SNAKE_CASE	)  # Tell lightning we are training the student
      __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher	).eval()
      use_task_specific_params(_SCREAMING_SNAKE_CASE ,				hparams.task	)  # We copy good generation parameters to student by default
      if hparams.student is not None:
         __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.student	)
         use_task_specific_params(_SCREAMING_SNAKE_CASE ,				hparams.task	)
         __lowerCamelCase		,__lowerCamelCase    		=					None, None
      else:
         __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					create_student_by_copying_alternating_layers(
             _SCREAMING_SNAKE_CASE ,				e=hparams.student_encoder_layers ,				d=hparams.student_decoder_layers ,				save_path=_SCREAMING_SNAKE_CASE	)

      if hparams.length_penalty != -1:
         __lowerCamelCase    		=					hparams.length_penalty
      __lowerCamelCase    		=					hparams.teacher  # Use teacher's tokenizer
      super().__init__(_SCREAMING_SNAKE_CASE ,				model=_SCREAMING_SNAKE_CASE ,				config=student.config	)
      assert student.config.model_type == teacher.config.model_type, (
          F'''teacher, student model types should be the same, got {student.config.model_type} !='''
          F''' {teacher.config.model_type}'''
      )

      if student.config.model_type == "t5":
         __lowerCamelCase    		=					len(student.get_encoder().block	)
         __lowerCamelCase    		=					len(student.get_decoder().block	)
         __lowerCamelCase    		=					len(teacher.get_encoder().block	)
         __lowerCamelCase    		=					len(teacher.get_decoder().block	)
      else:
         __lowerCamelCase    		=					student.config.encoder_layers
         __lowerCamelCase    		=					student.config.decoder_layers
         __lowerCamelCase    		=					teacher.config.encoder_layers
         __lowerCamelCase    		=					teacher.config.decoder_layers

      __lowerCamelCase    		=					not (hparams.student is None or hparams.teacher == hparams.student)
      __lowerCamelCase    		=					(not self.different_base_models) and hparams.alpha_hid > 0
      __lowerCamelCase    		=					self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      __lowerCamelCase    		=					teacher
      freeze_params(self.teacher	)

      if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
         try:
            del self.teacher.model.encoder
         except AttributeError:  # T5
            del self.teacher.encoder

      if e_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)
      if d_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)

      __lowerCamelCase		,__lowerCamelCase    		=					e_layer_ids, d_layer_ids  # type: List[int], List[int]

      if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
         if hparams.supervise_forward:
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.e_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.d_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
         else:  # student layer should emulate hidden states of the teacher layer it was copied from
            __lowerCamelCase    		=					self.e_layer_ids
            __lowerCamelCase    		=					self.d_layer_ids
      else:
         __lowerCamelCase    		=					None
         __lowerCamelCase    		=					None

      __lowerCamelCase    		=					nn.KLDivLoss(reduction='batchmean'	)
      __lowerCamelCase    		=					2.0
      __lowerCamelCase    		=					hparams.alpha_mlm
      __lowerCamelCase    		=					hparams.alpha_ce
      __lowerCamelCase    		=					hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					s_logits.size(-1	)
      __lowerCamelCase    		=					torch.masked_select(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					torch.masked_select(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					s_logits_slct.view(-1 ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					t_logits_slct.view(-1 ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      __lowerCamelCase    		=					(
          self.ce_loss_fct(
              nn.functional.log_softmax(s_logits_slct / self.temperature ,				dim=-1	) ,				nn.functional.softmax(t_logits_slct / self.temperature ,				dim=-1	) ,				)
          * (self.temperature) ** 2
      )
      return loss_ce







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      add_distill_args(_SCREAMING_SNAKE_CASE	)
      return parser







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.tokenizer.pad_token_id
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.model._shift_right(_SCREAMING_SNAKE_CASE	)
      else:
         __lowerCamelCase    		=					shift_tokens_right(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      # noinspection PyCallingNonCallable
      __lowerCamelCase    		=					self(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				output_attentions=_SCREAMING_SNAKE_CASE ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					student_outputs['logits']

      # Same cross entropy vs. label smoothing logic as finetune.py
      assert lm_logits.shape[-1] == self.model.config.vocab_size
      if self.hparams.label_smoothing == 0:
         # Same behavior as modeling_bart.py, besides ignoring pad_token_id
         __lowerCamelCase    		=					nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					loss_fct(lm_logits.view(-1 ,				lm_logits.shape[-1]	) ,				labels.view(-1	)	)
      else:
         __lowerCamelCase    		=					nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,				dim=-1	)
         __lowerCamelCase		,__lowerCamelCase    		=					label_smoothed_nll_loss(
             _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				self.hparams.label_smoothing ,				ignore_index=_SCREAMING_SNAKE_CASE	)

      def zero_tensor():
         return torch.tensor(0.0	).type_as(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					student_outputs[
          'encoder_last_hidden_state'
      ]  # use this unless self.different_base_models
      __lowerCamelCase		,__lowerCamelCase    		=					zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
         __lowerCamelCase    		=					self.teacher.get_encoder()(
             _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				)
         if self.different_base_models:
            __lowerCamelCase    		=					all_teacher_encoder_outputs['last_hidden_state']
         elif self.do_calc_hidden_loss:
            __lowerCamelCase    		=					self.calc_hidden_loss(
                _SCREAMING_SNAKE_CASE ,				student_outputs['encoder_hidden_states'] ,				all_teacher_encoder_outputs['hidden_states'] ,				self.e_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

      __lowerCamelCase    		=					self.teacher(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				encoder_outputs=(teacher_enc_outputs,) ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					decoder_input_ids.ne(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					self.calc_ce_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				teacher_outputs['logits']	)
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
         __lowerCamelCase    		=					self.calc_hidden_loss(
             _SCREAMING_SNAKE_CASE ,				student_outputs['decoder_hidden_states'] ,				teacher_outputs['decoder_hidden_states'] ,				self.d_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

      __lowerCamelCase    		=					(
          self.alpha_ce * loss_ce
          + self.alpha_mlm * student_lm_loss
          + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
      )
      return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					'expected list or tuple for hidden_states, got tensor of shape: '
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states.shape}'''
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states_T.shape}'''
      __lowerCamelCase    		=					attention_mask.to(hidden_states[0]	)
      __lowerCamelCase    		=					mask.sum() * hidden_states[0].size(-1	)
      __lowerCamelCase    		=					torch.stack([hidden_states[i] for i in range(len(_SCREAMING_SNAKE_CASE	)	)]	)
      __lowerCamelCase    		=					torch.stack([hidden_states_T[j] for j in matches]	)
      assert student_states.shape == teacher_states.shape, F'''{student_states.shape} != {teacher_states.shape}'''
      if normalize_hidden:
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				student_states.shape[1:]	)
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				teacher_states.shape[1:]	)
      __lowerCamelCase    		=					nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				reduction='none'	)
      __lowerCamelCase    		=					(mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
      return masked_mse



def       A__    (	lowercase_     :	List[Any]						)  ->							List[Any]:
   # NOTE: if --student argument was specified and the teacher and student base models
   # are different, the models still have to have the same tokenizer, specified by
   # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
   # from bart to t5. This s because if the tokenizers are different, the output space
   # for the two models is also different and their logits are not comparable.
   parser.add_argument('--teacher'      , type=lowercase_						)
   parser.add_argument('--alpha_ce'      , default=0.8      , type=lowercase_						)
   parser.add_argument('--alpha_mlm'      , default=0.2      , type=lowercase_						)
   parser.add_argument('--alpha_hid'      , default=0.0      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student'      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_decoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_encoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--no_teacher'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--length_penalty'      , type=lowercase_      , default=-1						)
   parser.add_argument('--supervise_forward'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--normalize_hidden'      , action='store_true'      , default=lowercase_						)






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    "translation"
   __UpperCAmelCase	:							List[Any]    =    ["bleu"]
   __UpperCAmelCase	:							Dict    =    "bleu"







   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      __lowerCamelCase    		=					hparams.src_lang
      __lowerCamelCase    		=					hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.tokenizer.lang_code_to_id[hparams.tgt_lang]







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return calculate_bleu(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      add_distill_args(_SCREAMING_SNAKE_CASE	)
      return parser



def       A__    (	lowercase_     :	str						)  ->							Any:
   if args.no_teacher:
      __lowerCamelCase    		=					TranslationModule if 'translation' in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      __lowerCamelCase    		=					TranslationDistiller if 'translation' in args.task else SummarizationDistiller
   __lowerCamelCase    		=					module_cls.__name__
   print(F'''using module {args.setup_cls}'''						)
   __lowerCamelCase    		=					module_cls(lowercase_						)
   return model



def       A__    (	lowercase_     :	Dict						)  ->							List[Any]:
   Path(args.output_dir						).mkdir(exist_ok=lowercase_						)
   check_output_dir(lowercase_      , expected_items=3						)

   __lowerCamelCase    		=					create_module(lowercase_						)
   return ft_main(lowercase_      , model=lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Any   =   argparse.ArgumentParser()
     _SCREAMING_SNAKE_CASE	:		List[Any]   =   pl.Trainer.add_argparse_args(parser)
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
     _SCREAMING_SNAKE_CASE	:		Any   =   parser.parse_args()

     distill_main(args)








"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase		:    Dict			=       getLogger(__name__)


lowercase		:    Dict			=       'cuda' if torch.cuda.is_available() else 'cpu'


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_ = 8						,					lowerCamelCase_ = DEFAULT_DEVICE						,					lowerCamelCase_=False						,					lowerCamelCase_="summarization"						,					lowerCamelCase_=None						,					**lowerCamelCase_						,					)  ->      Dict:


					'''simple docstring'''
					__a:    str		     =       Path(lowerCamelCase_			).open("""w"""						,					encoding="""utf-8"""			)
					__a:    Tuple		     =       str(lowerCamelCase_			)
					__a:    List[Any]		     =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).to(lowerCamelCase_			)
					if fpaa:
										__a:    List[str]		     =       model.half()

					__a:    Optional[int]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_			)
					logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

					__a:    str		     =       time.time()
					# update config with task specific params
					use_task_specific_params(lowerCamelCase_						,					lowerCamelCase_			)
					if prefix is None:
										__a:    Tuple		     =       prefix or getattr(model.config						,					"""prefix"""						,					""""""			) or """"""
					for examples_chunk in tqdm(list(chunks(lowerCamelCase_						,					lowerCamelCase_			)			)			):
										__a:    Tuple		     =       [prefix + text for text in examples_chunk]
										__a:    Optional[int]		     =       tokenizer(lowerCamelCase_						,					return_tensors="""pt"""						,					truncation=lowerCamelCase_						,					padding="""longest"""			).to(lowerCamelCase_			)
										__a:    Any		     =       model.generate(
										    input_ids=batch.input_ids						,					attention_mask=batch.attention_mask						,					**lowerCamelCase_						,					)
										__a:    Optional[int]		     =       tokenizer.batch_decode(lowerCamelCase_						,					skip_special_tokens=lowerCamelCase_						,					clean_up_tokenization_spaces=lowerCamelCase_			)
										for hypothesis in dec:
															fout.write(hypothesis + """\n"""			)
															fout.flush()
					fout.close()
					__a:    Any		     =       int(time.time() - start_time			)  # seconds
					__a:    Optional[int]		     =       len(lowerCamelCase_			)
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,					4			)}


def      lowerCamelCase_     (						)  ->      str:


					'''simple docstring'''
					return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""			)


def      lowerCamelCase_     (						lowerCamelCase_=True			)  ->      Any:


					'''simple docstring'''
					__a:    Any		     =       argparse.ArgumentParser()
					parser.add_argument("""model_name"""						,					type=lowerCamelCase_						,					help="""like facebook/bart-large-cnn,t5-base, etc."""			)
					parser.add_argument("""input_path"""						,					type=lowerCamelCase_						,					help="""like cnn_dm/test.source"""			)
					parser.add_argument("""save_path"""						,					type=lowerCamelCase_						,					help="""where to save summaries"""			)
					parser.add_argument("""--reference_path"""						,					type=lowerCamelCase_						,					required=lowerCamelCase_						,					help="""like cnn_dm/test.target"""			)
					parser.add_argument("""--score_path"""						,					type=lowerCamelCase_						,					required=lowerCamelCase_						,					default="""metrics.json"""						,					help="""where to save metrics"""			)
					parser.add_argument("""--device"""						,					type=lowerCamelCase_						,					required=lowerCamelCase_						,					default=lowerCamelCase_						,					help="""cuda, cuda:1, cpu etc."""			)
					parser.add_argument(
					    """--prefix"""						,					type=lowerCamelCase_						,					required=lowerCamelCase_						,					default=lowerCamelCase_						,					help="""will be added to the begininng of src examples"""			)
					parser.add_argument("""--task"""						,					type=lowerCamelCase_						,					default="""summarization"""						,					help="""used for task_specific_params + metrics"""			)
					parser.add_argument("""--bs"""						,					type=lowerCamelCase_						,					default=8						,					required=lowerCamelCase_						,					help="""batch size"""			)
					parser.add_argument(
					    """--n_obs"""						,					type=lowerCamelCase_						,					default=-1						,					required=lowerCamelCase_						,					help="""How many observations. Defaults to all."""			)
					parser.add_argument("""--fp16"""						,					action="""store_true"""			)
					parser.add_argument("""--dump-args"""						,					action="""store_true"""						,					help="""print the custom hparams with the results"""			)
					parser.add_argument(
					    """--info"""						,					nargs="""?"""						,					type=lowerCamelCase_						,					const=datetime_now()						,					help=(
					        """use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
					        """ lang=en-ru. If no value is passed, the current datetime string will be used."""
					    )						,					)
					# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
					__a     ,					__a:    int		     =       parser.parse_known_args()
					__a:    List[Any]		     =       parse_numeric_n_bool_cl_kwargs(lowerCamelCase_			)
					if parsed_args and verbose:
										print(f"""parsed the following generate kwargs: {parsed_args}"""			)
					with open(args.input_path			) as f:
										__a:    Tuple		     =       [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										__a:    Any		     =       examples[: args.n_obs]
					Path(args.save_path			).parent.mkdir(exist_ok=lowerCamelCase_			)
					if args.reference_path is None and Path(args.score_path			).exists():
										warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""			)
					__a:    Tuple		     =       generate_summaries_or_translations(
					    lowerCamelCase_						,					args.save_path						,					args.model_name						,					batch_size=args.bs						,					device=args.device						,					fpaa=args.fpaa						,					task=args.task						,					prefix=args.prefix						,					**lowerCamelCase_						,					)

					if args.reference_path is None:
										return {}

					# Compute scores
					__a:    Optional[Any]		     =       calculate_bleu if """translation""" in args.task else calculate_rouge
					__a:    Union[str, Any]		     =       [x.rstrip() for x in open(args.save_path			).readlines()]
					__a:    List[str]		     =       [x.rstrip() for x in open(args.reference_path			).readlines()][: len(lowerCamelCase_			)]
					__a:    dict		     =       score_fn(lowerCamelCase_						,					lowerCamelCase_			)
					scores.update(lowerCamelCase_			)

					if args.dump_args:
										scores.update(lowerCamelCase_			)
					if args.info:
										__a:    Optional[Any]		     =       args.info

					if verbose:
										print(lowerCamelCase_			)

					if args.score_path is not None:
										json.dump(lowerCamelCase_						,					open(args.score_path						,					"""w"""			)			)

					return scores


if __name__ == "__main__":
			# Usage for MT:
			# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
			run_generate(verbose=True)
