



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__A   			=logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__A   			={
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__A   			={
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__A   			=sorted(arg_to_scheduler.keys())
__A   			="{" + ", ".join(arg_to_scheduler_choices) + "}"

class 		UpperCAmelCase__			(			pl.LightningModule   ):




			'''simple docstring'''






			def __init__(   self							: Dict       ,       a_							: argparse.Namespace       ,       a_							: Optional[int]=None       ,       a_							: str="base"       ,       a_							: Optional[int]=None       ,       a_							: List[Any]=None       ,       a_							: Optional[int]=None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					super().__init__()
					# TODO: move to self.save_hyperparameters()
					# self.save_hyperparameters()
					# can also expand arguments into trainer signature for easier reading

					self.save_hyperparameters(a_ )
					__UpperCAmelCase    :     Optional[int]													=	0
					__UpperCAmelCase    :     Tuple													=	Path(self.hparams.output_dir )
					__UpperCAmelCase    :     Optional[Any]													=	self.hparams.cache_dir if self.hparams.cache_dir else None
					if config is None:
							__UpperCAmelCase    :     Union[str, Any]													=	AutoConfig.from_pretrained(
							    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path       ,       **({'''num_labels''': num_labels} if num_labels is not None else {})       ,       cache_dir=a_       ,       **a_       ,       )
					else:
							__UpperCAmelCase    :     PretrainedConfig													=	config

					__UpperCAmelCase    :     str													=	('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
					for p in extra_model_params:
							if getattr(self.hparams       ,       a_       ,       a_ ):
									assert hasattr(self.config       ,       a_ ), F'model config doesn\'t have a `{p}` attribute'
									setattr(self.config       ,       a_       ,       getattr(self.hparams       ,       a_ ) )

					if tokenizer is None:
							__UpperCAmelCase    :     Optional[Any]													=	AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,       cache_dir=a_       ,       )
					else:
							__UpperCAmelCase    :     PreTrainedTokenizer													=	tokenizer
					__UpperCAmelCase    :     Dict													=	MODEL_MODES[mode]
					if model is None:
							__UpperCAmelCase    :     int													=	self.model_type.from_pretrained(
							    self.hparams.model_name_or_path       ,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path )       ,       config=self.config       ,       cache_dir=a_       ,       )
					else:
							__UpperCAmelCase    :     str													=	model




			def  snake_case__     (   self							: Optional[int]       ,       *a_							: Any       ,       **a_							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.model_type.from_pretrained(*a_       ,       **a_ )




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	arg_to_scheduler[self.hparams.lr_scheduler]
					__UpperCAmelCase    :     Dict													=	get_schedule_func(
					    self.opt       ,       num_warmup_steps=self.hparams.warmup_steps       ,       num_training_steps=self.total_steps() )
					__UpperCAmelCase    :     str													=	{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
					return scheduler




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.model
					__UpperCAmelCase    :     Union[str, Any]													=	['''bias''', '''LayerNorm.weight''']
					__UpperCAmelCase    :     str													=	[
					    {
					        '''params''': [
					            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
					        ],  # check this named paramters
					        '''weight_decay''': self.hparams.weight_decay,
					    },
					    {
					        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
					        '''weight_decay''': 0.0,
					    },
					]
					if self.hparams.adafactor:
							__UpperCAmelCase    :     Optional[Any]													=	Adafactor(
							    a_       ,       lr=self.hparams.learning_rate       ,       scale_parameter=a_       ,       relative_step=a_ )

					else:
							__UpperCAmelCase    :     Optional[int]													=	AdamW(
							    a_       ,       lr=self.hparams.learning_rate       ,       eps=self.hparams.adam_epsilon )
					__UpperCAmelCase    :     Optional[int]													=	optimizer

					__UpperCAmelCase    :     int													=	self.get_lr_scheduler()

					return [optimizer], [scheduler]




			def  snake_case__     (   self							: Any       ,       a_							: str       ,       a_							: Optional[int] ):
					'''simple docstring'''





					return self.validation_step(a_       ,       a_ )




			def  snake_case__     (   self							: Dict       ,       a_							: Tuple ):
					'''simple docstring'''





					return self.validation_end(a_ )




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	max(1       ,       self.hparams.gpus )  # TODO: consider num_tpu_cores
					__UpperCAmelCase    :     Optional[Any]													=	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
					return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Any ):
					'''simple docstring'''





					if stage == "test":
							__UpperCAmelCase    :     Union[str, Any]													=	len(self.test_dataloader().dataset )
					else:
							__UpperCAmelCase    :     Dict													=	self.get_dataloader('''train'''       ,       self.hparams.train_batch_size       ,       shuffle=a_ )
							__UpperCAmelCase    :     Tuple													=	len(self.train_dataloader().dataset )




			def  snake_case__     (   self							: List[str]       ,       a_							: str       ,       a_							: int       ,       a_							: bool = False ):
					'''simple docstring'''





					raise NotImplementedError('''You must implement this for your task''' )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					return self.train_loader




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					return self.get_dataloader('''dev'''       ,       self.hparams.eval_batch_size       ,       shuffle=a_ )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					return self.get_dataloader('''test'''       ,       self.hparams.eval_batch_size       ,       shuffle=a_ )




			def  snake_case__     (   self							: Tuple       ,       a_							: List[str] ):
					'''simple docstring'''





					return os.path.join(
					    self.hparams.data_dir       ,       '''cached_{}_{}_{}'''.format(
					        a_       ,       list(filter(a_       ,       self.hparams.model_name_or_path.split('''/''' ) ) ).pop()       ,       str(self.hparams.max_seq_length )       ,       )       ,       )




			@pl.utilities.rank_zero_only
			def  snake_case__     (   self							: List[Any]       ,       a_							: Dict[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	self.output_dir.joinpath('''best_tfmr''' )
					__UpperCAmelCase    :     List[str]													=	self.step_count
					self.model.save_pretrained(a_ )
					self.tokenizer.save_pretrained(a_ )




			@staticmethod
			def  snake_case__     (   a_							: str       ,       a_							: int ):
					'''simple docstring'''





					parser.add_argument(
					    '''--model_name_or_path'''       ,       default=a_       ,       type=a_       ,       required=a_       ,       help='''Path to pretrained model or model identifier from huggingface.co/models'''       ,       )
					parser.add_argument(
					    '''--config_name'''       ,       default=''''''       ,       type=a_       ,       help='''Pretrained config name or path if not the same as model_name''' )
					parser.add_argument(
					    '''--tokenizer_name'''       ,       default=a_       ,       type=a_       ,       help='''Pretrained tokenizer name or path if not the same as model_name'''       ,       )
					parser.add_argument(
					    '''--cache_dir'''       ,       default=str(Path(a_ ).parent / '''test_run''' / '''cache''' )       ,       type=a_       ,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''       ,       )
					parser.add_argument(
					    '''--encoder_layerdrop'''       ,       type=a_       ,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''       ,       )
					parser.add_argument(
					    '''--decoder_layerdrop'''       ,       type=a_       ,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''       ,       )
					parser.add_argument(
					    '''--dropout'''       ,       type=a_       ,       help='''Dropout probability (Optional). Goes into model.config'''       ,       )
					parser.add_argument(
					    '''--attention_dropout'''       ,       type=a_       ,       help='''Attention dropout probability (Optional). Goes into model.config'''       ,       )
					parser.add_argument('''--learning_rate'''       ,       default=5e-5       ,       type=a_       ,       help='''The initial learning rate for Adam.''' )
					parser.add_argument(
					    '''--lr_scheduler'''       ,       default='''linear'''       ,       choices=a_       ,       metavar=a_       ,       type=a_       ,       help='''Learning rate scheduler'''       ,       )
					parser.add_argument('''--weight_decay'''       ,       default=0.0       ,       type=a_       ,       help='''Weight decay if we apply some.''' )
					parser.add_argument('''--adam_epsilon'''       ,       default=1e-8       ,       type=a_       ,       help='''Epsilon for Adam optimizer.''' )
					parser.add_argument('''--warmup_steps'''       ,       default=0       ,       type=a_       ,       help='''Linear warmup over warmup_steps.''' )
					parser.add_argument('''--num_workers'''       ,       default=4       ,       type=a_       ,       help='''kwarg passed to DataLoader''' )
					parser.add_argument('''--num_train_epochs'''       ,       dest='''max_epochs'''       ,       default=3       ,       type=a_ )
					parser.add_argument('''--train_batch_size'''       ,       default=32       ,       type=a_ )
					parser.add_argument('''--eval_batch_size'''       ,       default=32       ,       type=a_ )
					parser.add_argument('''--adafactor'''       ,       action='''store_true''' )

class 		UpperCAmelCase__			(			pl.Callback   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[str]       ,       a_							: List[str]       ,       a_							: List[str] ):
					'''simple docstring'''





					if (
					    trainer.is_global_zero and trainer.global_rank == 0
					):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.

class 		UpperCAmelCase__			(			pl.Callback   ):




			'''simple docstring'''






			def  snake_case__     (   self							: Optional[int]       ,       a_							: str       ,       a_							: List[str] ):
					'''simple docstring'''





					for name, param in pl_module.model.rag.named_parameters():
							if param.grad is None:
									print(a_ )

class 		UpperCAmelCase__			(			pl.Callback   ):




			'''simple docstring'''






			def  snake_case__     (   self							: Dict       ,       a_							: Dict       ,       a_							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	trainer.lr_schedulers[0]['''scheduler''']
					__UpperCAmelCase    :     List[str]													=	{F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
					pl_module.logger.log_metrics(a_ )




			def  snake_case__     (   self							: List[Any]       ,       a_							: pl.Trainer       ,       a_							: pl.LightningModule ):
					'''simple docstring'''





					rank_zero_info('''***** Validation results *****''' )
					__UpperCAmelCase    :     List[Any]													=	trainer.callback_metrics
					# Log results
					for key in sorted(a_ ):
							if key not in ["log", "progress_bar"]:
									rank_zero_info('''{} = {}\n'''.format(a_       ,       str(metrics[key] ) ) )




			def  snake_case__     (   self							: Any       ,       a_							: pl.Trainer       ,       a_							: pl.LightningModule ):
					'''simple docstring'''





					rank_zero_info('''***** Test results *****''' )
					__UpperCAmelCase    :     Optional[Any]													=	trainer.callback_metrics
					# Log and save results to file
					__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(pl_module.hparams.output_dir       ,       '''test_results.txt''' )
					with open(a_       ,       '''w''' ) as writer:
							for key in sorted(a_ ):
									if key not in ["log", "progress_bar"]:
											rank_zero_info('''{} = {}\n'''.format(a_       ,       str(metrics[key] ) ) )
											writer.write('''{} = {}\n'''.format(a_       ,       str(metrics[key] ) ) )

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		parser.add_argument(
		    '''--output_dir''' ,   default=str(Path(_UpperCAmelCase						).parent / '''test_run''' / '''model_checkpoints'''						) ,   type=_UpperCAmelCase ,   help='''The output directory where the model predictions and checkpoints will be written.''' ,   )
		parser.add_argument(
		    '''--fp16''' ,   action='''store_true''' ,   help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' ,   )

		parser.add_argument(
		    '''--fp16_opt_level''' ,   type=_UpperCAmelCase ,   default='''O2''' ,   help=(
		        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
		        '''See details at https://nvidia.github.io/apex/amp.html'''
		    ) ,   )
		parser.add_argument('''--n_tpu_cores''' ,   dest='''tpu_cores''' ,   type=_UpperCAmelCase						)
		parser.add_argument('''--max_grad_norm''' ,   dest='''gradient_clip_val''' ,   default=1.0 ,   type=_UpperCAmelCase ,   help='''Max gradient norm'''						)
		parser.add_argument('''--do_train''' ,   action='''store_true''' ,   help='''Whether to run training.'''						)
		parser.add_argument('''--do_predict''' ,   action='''store_true''' ,   help='''Whether to run predictions on the test set.'''						)
		parser.add_argument(
		    '''--gradient_accumulation_steps''' ,   dest='''accumulate_grad_batches''' ,   type=_UpperCAmelCase ,   default=1 ,   help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,   )
		parser.add_argument('''--seed''' ,   type=_UpperCAmelCase ,   default=42 ,   help='''random seed for initialization'''						)
		parser.add_argument(
		    '''--data_dir''' ,   default=str(Path(_UpperCAmelCase						).parent / '''test_run''' / '''dummy-train-data'''						) ,   type=_UpperCAmelCase ,   help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' ,   )

def        a						(      _UpperCAmelCase							:					BaseTransformer ,   _UpperCAmelCase							:					argparse.Namespace ,   _UpperCAmelCase							:					str=None ,   _UpperCAmelCase							:					Tuple=True ,   _UpperCAmelCase							:					List[str]=[] ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					List[Any]=None ,   **_UpperCAmelCase							:					Optional[int] ,   ):







		'''simple docstring'''





		pl.seed_everything(args.seed						)

		# init model
		__UpperCAmelCase    :     List[str]													=	Path(model.hparams.output_dir						)
		odir.mkdir(exist_ok=_UpperCAmelCase						)

		# add custom checkpoints
		if checkpoint_callback is None:
				__UpperCAmelCase    :     List[Any]													=	pl.callbacks.ModelCheckpoint(
				    filepath=args.output_dir ,   prefix='''checkpoint''' ,   monitor='''val_loss''' ,   mode='''min''' ,   save_top_k=1						)
		if early_stopping_callback:
				extra_callbacks.append(_UpperCAmelCase						)
		if logging_callback is None:
				__UpperCAmelCase    :     Optional[int]													=	LoggingCallback()

		__UpperCAmelCase    :     Optional[int]													=	{}

		if args.fpaa:
				__UpperCAmelCase    :     Dict													=	16

		if args.gpus > 1:
				__UpperCAmelCase    :     Tuple													=	'''auto'''
				__UpperCAmelCase    :     Any													=	'''ddp'''

		__UpperCAmelCase    :     Tuple													=	args.accumulate_grad_batches
		__UpperCAmelCase    :     Union[str, Any]													=	None
		__UpperCAmelCase    :     Optional[int]													=	'''auto'''

		__UpperCAmelCase    :     List[Any]													=	pl.Trainer.from_argparse_args(
		    _UpperCAmelCase ,   weights_summary=_UpperCAmelCase ,   callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] ,   logger=_UpperCAmelCase ,   val_check_interval=1 ,   num_sanity_val_steps=2 ,   **_UpperCAmelCase ,   )

		if args.do_train:
				trainer.fit(_UpperCAmelCase						)

		else:
				print('''RAG modeling tests with new set functions successfuly executed!'''						)
		return trainer










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


__A   			=getLogger(__name__)


__A   			="cuda" if torch.cuda.is_available() else "cpu"

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					str = DEFAULT_DEVICE ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					Optional[int]="summarization" ,   _UpperCAmelCase							:					Dict=None ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	Path(_UpperCAmelCase						).open('''w''' ,   encoding='''utf-8'''						)
		__UpperCAmelCase    :     Optional[int]													=	str(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
		if fpaa:
				__UpperCAmelCase    :     Dict													=	model.half()

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

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

def        a						(      ):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					Any=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	argparse.ArgumentParser()
		parser.add_argument('''model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
		parser.add_argument('''input_path''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument('''save_path''' ,   type=_UpperCAmelCase ,   help='''where to save summaries'''						)
		parser.add_argument('''--reference_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''like cnn_dm/test.target'''						)
		parser.add_argument('''--score_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default='''metrics.json''' ,   help='''where to save metrics'''						)
		parser.add_argument('''--device''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''cuda, cuda:1, cpu etc.'''						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   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=_UpperCAmelCase ,   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
		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	parser.parse_known_args()
		__UpperCAmelCase    :     Optional[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if parsed_args and verbose:
				print(f'parsed the following generate kwargs: {parsed_args}'						)
		__UpperCAmelCase    :     Optional[int]													=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
		if args.n_obs > 0:
				__UpperCAmelCase    :     Optional[int]													=	examples[: args.n_obs]
		Path(args.save_path						).parent.mkdir(exist_ok=_UpperCAmelCase						)

		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.'						)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

		__UpperCAmelCase    :     int													=	generate_summaries_or_translations(
		    _UpperCAmelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_UpperCAmelCase ,   )

		if args.reference_path is None:
				return {}

		# Compute scores
		__UpperCAmelCase    :     str													=	calculate_bleu if '''translation''' in args.task else calculate_rouge
		__UpperCAmelCase    :     Tuple													=	[x.rstrip() for x in open(args.save_path						).readlines()]
		__UpperCAmelCase    :     int													=	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_UpperCAmelCase						)]
		__UpperCAmelCase    :     dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
		scores.update(_UpperCAmelCase						)

		if args.dump_args:
				scores.update(_UpperCAmelCase						)
		if args.info:
				__UpperCAmelCase    :     Dict													=	args.info

		if verbose:
				print(_UpperCAmelCase						)

		if args.score_path is not None:
				json.dump(_UpperCAmelCase ,   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)





