





"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase__ :       Optional[int]						     =			logging.getLogger(__name__)




@dataclass
class 				lowercase__:
	'''simple docstring'''

	UpperCamelCase				=  field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,						)
	UpperCamelCase				=  field(default=_UpperCAmelCase		,						metadata={"""help""": """Whether tp freeze the encoder."""}   )
	UpperCamelCase				=  field(default=_UpperCAmelCase		,						metadata={"""help""": """Whether  to freeze the embeddings."""}   )





@dataclass
class 				lowercase__:
	'''simple docstring'''

	UpperCamelCase				=  field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
	UpperCamelCase				=  field(
	    default="""summarization"""		,						metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}		,						)
	UpperCamelCase				=  field(
	    default=10_24		,						metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }		,						)
	UpperCamelCase				=  field(
	    default=1_28		,						metadata={
	        """help""": (
	            """The maximum total sequence length for target text after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }		,						)
	UpperCamelCase				=  field(
	    default=1_42		,						metadata={
	        """help""": (
	            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded. """
	            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
	            """during ``evaluate`` and ``predict``."""
	        )
	    }		,						)
	UpperCamelCase				=  field(
	    default=1_42		,						metadata={
	        """help""": (
	            """The maximum total sequence length for test target text after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }		,						)
	UpperCamelCase				=  field(default=-1		,						metadata={"""help""": """# training examples. -1 means use all."""}   )
	UpperCamelCase				=  field(default=-1		,						metadata={"""help""": """# validation examples. -1 means use all."""}   )
	UpperCamelCase				=  field(default=-1		,						metadata={"""help""": """# test examples. -1 means use all."""}   )
	UpperCamelCase				=  field(default=_UpperCAmelCase		,						metadata={"""help""": """Source language id for translation."""}   )
	UpperCamelCase				=  field(default=_UpperCAmelCase		,						metadata={"""help""": """Target language id for translation."""}   )
	UpperCamelCase				=  field(default=_UpperCAmelCase		,						metadata={"""help""": """# num_beams to use for evaluation."""}   )
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}		,						)





def __A						(   a_ :							str		,		a_ :							List[str]		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		logger.info(F"***** {split} metrics *****"     )
		for key in sorted(metrics.keys()     ):
				logger.info(F"  {key} = {metrics[key]}"     )
		save_json(SCREAMING_SNAKE_CASE_		,		os.path.join(SCREAMING_SNAKE_CASE_		,		F"{split}_results.json"     )     )





def __A						(   )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)     )

		if len(sys.argv     ) == 2 and sys.argv[1].endswith('''.json'''     ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				SCREAMING_SNAKE_CASE       :     str				= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= parser.parse_args_into_dataclasses()

		check_output_dir(SCREAMING_SNAKE_CASE_     )

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,		datefmt='''%m/%d/%Y %H:%M:%S'''		,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,		)
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,		training_args.local_rank		,		training_args.device		,		training_args.n_gpu		,		bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED     )		,		training_args.fpaa		,		)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank     ):
				transformers.utils.logging.set_verbosity_info()
		logger.info('''Training/evaluation parameters %s'''		,		SCREAMING_SNAKE_CASE_     )

		# Set seed
		set_seed(training_args.seed     )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		SCREAMING_SNAKE_CASE       :     Tuple				= AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,		cache_dir=model_args.cache_dir		,		)

		SCREAMING_SNAKE_CASE       :     Dict				= ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
		for p in extra_model_params:
				if getattr(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_     ):
						assert hasattr(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_     ), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
						setattr(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		getattr(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_     )     )

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,		cache_dir=model_args.cache_dir		,		)
		SCREAMING_SNAKE_CASE       :     str				= AutoModelForSeqaSeqLM.from_pretrained(
		    model_args.model_name_or_path		,		from_tf='''.ckpt''' in model_args.model_name_or_path		,		config=SCREAMING_SNAKE_CASE_		,		cache_dir=model_args.cache_dir		,		)

		# use task specific params
		use_task_specific_params(SCREAMING_SNAKE_CASE_		,		data_args.task     )

		# set num_beams for evaluation
		if data_args.eval_beams is None:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= model.config.num_beams

		# set decoder_start_token_id for MBart
		if model.config.decoder_start_token_id is None and isinstance(SCREAMING_SNAKE_CASE_		,		(MBartTokenizer, MBartTokenizerFast)     ):
				assert (
				    data_args.tgt_lang is not None and data_args.src_lang is not None
				), "mBart requires --tgt_lang and --src_lang"
				if isinstance(SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_     ):
						SCREAMING_SNAKE_CASE       :     str				= tokenizer.lang_code_to_id[data_args.tgt_lang]
				else:
						SCREAMING_SNAKE_CASE       :     List[str]				= tokenizer.convert_tokens_to_ids(data_args.tgt_lang     )

		if model_args.freeze_embeds:
				freeze_embeds(SCREAMING_SNAKE_CASE_     )
		if model_args.freeze_encoder:
				freeze_params(model.get_encoder()     )
				assert_all_frozen(model.get_encoder()     )

		SCREAMING_SNAKE_CASE       :     Dict				= SeqaSeqDataset

		# Get datasets
		SCREAMING_SNAKE_CASE       :     Dict				= (
		    dataset_class(
		        SCREAMING_SNAKE_CASE_		,		type_path='''train'''		,		data_dir=data_args.data_dir		,		n_obs=data_args.n_train		,		max_target_length=data_args.max_target_length		,		max_source_length=data_args.max_source_length		,		prefix=model.config.prefix or ''''''		,		)
		    if training_args.do_train
		    else None
		)
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= (
		    dataset_class(
		        SCREAMING_SNAKE_CASE_		,		type_path='''val'''		,		data_dir=data_args.data_dir		,		n_obs=data_args.n_val		,		max_target_length=data_args.val_max_target_length		,		max_source_length=data_args.max_source_length		,		prefix=model.config.prefix or ''''''		,		)
		    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
		    else None
		)
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
		    dataset_class(
		        SCREAMING_SNAKE_CASE_		,		type_path='''test'''		,		data_dir=data_args.data_dir		,		n_obs=data_args.n_test		,		max_target_length=data_args.test_max_target_length		,		max_source_length=data_args.max_source_length		,		prefix=model.config.prefix or ''''''		,		)
		    if training_args.do_predict
		    else None
		)

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE       :     Any				= (
		    build_compute_metrics_fn(data_args.task		,		SCREAMING_SNAKE_CASE_     ) if training_args.predict_with_generate else None
		)
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= SeqaSeqTrainer(
		    model=SCREAMING_SNAKE_CASE_		,		args=SCREAMING_SNAKE_CASE_		,		data_args=SCREAMING_SNAKE_CASE_		,		train_dataset=SCREAMING_SNAKE_CASE_		,		eval_dataset=SCREAMING_SNAKE_CASE_		,		data_collator=SeqaSeqDataCollator(
		        SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		model.config.decoder_start_token_id		,		training_args.tpu_num_cores     )		,		compute_metrics=SCREAMING_SNAKE_CASE_		,		tokenizer=SCREAMING_SNAKE_CASE_		,		)

		SCREAMING_SNAKE_CASE       :     str				= {}
		# Training
		if training_args.do_train:
				logger.info('''*** Train ***'''     )

				SCREAMING_SNAKE_CASE       :     Optional[int]				= trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
				SCREAMING_SNAKE_CASE       :     str				= train_result.metrics
				SCREAMING_SNAKE_CASE       :     List[str]				= data_args.n_train

				trainer.save_model()  # this also saves the tokenizer

				if trainer.is_world_process_zero():
						handle_metrics('''train'''		,		SCREAMING_SNAKE_CASE_		,		training_args.output_dir     )
						all_metrics.update(SCREAMING_SNAKE_CASE_     )

						# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir		,		'''trainer_state.json'''     )     )

						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''     )

				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= trainer.evaluate(metric_key_prefix='''val'''     )
				SCREAMING_SNAKE_CASE       :     List[str]				= data_args.n_val
				SCREAMING_SNAKE_CASE       :     Dict				= round(metrics['''val_loss''']		,		4     )

				if trainer.is_world_process_zero():
						handle_metrics('''val'''		,		SCREAMING_SNAKE_CASE_		,		training_args.output_dir     )
						all_metrics.update(SCREAMING_SNAKE_CASE_     )

		if training_args.do_predict:
				logger.info('''*** Predict ***'''     )

				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= trainer.predict(test_dataset=SCREAMING_SNAKE_CASE_		,		metric_key_prefix='''test'''     )
				SCREAMING_SNAKE_CASE       :     Dict				= test_output.metrics
				SCREAMING_SNAKE_CASE       :     Dict				= data_args.n_test

				if trainer.is_world_process_zero():
						SCREAMING_SNAKE_CASE       :     Tuple				= round(metrics['''test_loss''']		,		4     )
						handle_metrics('''test'''		,		SCREAMING_SNAKE_CASE_		,		training_args.output_dir     )
						all_metrics.update(SCREAMING_SNAKE_CASE_     )

						if training_args.predict_with_generate:
								SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer.batch_decode(
								    test_output.predictions		,		skip_special_tokens=SCREAMING_SNAKE_CASE_		,		clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_     )
								SCREAMING_SNAKE_CASE       :     List[str]				= lmap(str.strip		,		SCREAMING_SNAKE_CASE_     )
								write_txt_file(SCREAMING_SNAKE_CASE_		,		os.path.join(training_args.output_dir		,		'''test_generations.txt'''     )     )

		if trainer.is_world_process_zero():
				save_json(SCREAMING_SNAKE_CASE_		,		os.path.join(training_args.output_dir		,		'''all_results.json'''     )     )

		return all_metrics





def __A						(   a_ :							Optional[Any]     )->							Any:




		'''simple docstring'''






		main()


if __name__ == "__main__":
					main()







"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None





def __A						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		return max(metric_fn(a_		,		a_     ) for gt in ground_truths     )





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE       :     List[Any]				= pd.read_csv(a_		,		sep='''\t'''		,		header=a_     )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE       :     str				= ast.literal_eval(a_     )
						answers.append(a_     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
				SCREAMING_SNAKE_CASE       :     Dict				= [[reference] for reference in references]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for prediction, ground_truths in zip(a_		,		a_     ):
				total += 1
				em += metric_max_over_ground_truths(a_		,		a_		,		a_     )
				fa += metric_max_over_ground_truths(a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

		logger.info(F"F1: {fa:.2f}"     )
		logger.info(F"EM: {em:.2f}"     )





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for hypo, reference in zip(a_		,		a_     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= set(hypo.split('''\t'''     )[:k]     )
				SCREAMING_SNAKE_CASE       :     List[str]				= set(reference.split('''\t'''     )     )
				total += 1
				em += len(hypo_provenance & ref_provenance     ) / k

		SCREAMING_SNAKE_CASE       :     Dict				= 100.0 * em / total
		logger.info(F"Precision@{k}: {em: .2f}"     )





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_		,		)['''input_ids'''].to(args.device     )

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever(
		    a_		,		question_enc_pool_output.cpu().detach().to(torch.floataa     ).numpy()		,		prefix=rag_model.rag.generator.config.prefix		,		n_docs=rag_model.config.n_docs		,		return_tensors='''pt'''		,		)
		SCREAMING_SNAKE_CASE       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.generate(  # rag_model overwrites generate
				    a_		,		attention_mask=a_		,		num_beams=args.num_beams		,		min_length=args.min_length		,		max_length=args.max_length		,		early_stopping=a_		,		num_return_sequences=1		,		bad_words_ids=[[0, 0]]		,		)
				SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever.generator_tokenizer.batch_decode(a_		,		skip_special_tokens=a_     )

				if args.print_predictions:
						for q, a in zip(a_		,		a_     ):
								logger.info('''Q: {} - A: {}'''.format(a_		,		a_     )     )

				return answers





def __A						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''		,		choices=['''rag_sequence''', '''rag_token''', '''bart''']		,		type=a_		,		help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )		,		)
		parser.add_argument(
		    '''--index_name'''		,		default=a_		,		choices=['''exact''', '''compressed''', '''legacy''']		,		type=a_		,		help='''RAG model retriever type'''		,		)
		parser.add_argument(
		    '''--index_path'''		,		default=a_		,		type=a_		,		help='''Path to the retrieval index'''		,		)
		parser.add_argument('''--n_docs'''		,		default=5		,		type=a_		,		help='''Number of retrieved docs'''     )
		parser.add_argument(
		    '''--model_name_or_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''		,		)
		parser.add_argument(
		    '''--eval_mode'''		,		choices=['''e2e''', '''retrieval''']		,		default='''e2e'''		,		type=a_		,		help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )		,		)
		parser.add_argument('''--k'''		,		default=1		,		type=a_		,		help='''k for the precision@k calculation'''     )
		parser.add_argument(
		    '''--evaluation_set'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a file containing evaluation samples'''		,		)
		parser.add_argument(
		    '''--gold_data_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a tab-separated file with gold samples'''		,		)
		parser.add_argument(
		    '''--gold_data_mode'''		,		default='''qa'''		,		type=a_		,		choices=['''qa''', '''ans''']		,		help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )		,		)
		parser.add_argument(
		    '''--predictions_path'''		,		type=a_		,		default='''predictions.txt'''		,		help='''Name of the predictions file, to be stored in the checkpoints directory'''		,		)
		parser.add_argument(
		    '''--eval_all_checkpoints'''		,		action='''store_true'''		,		help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''		,		)
		parser.add_argument(
		    '''--eval_batch_size'''		,		default=8		,		type=a_		,		help='''Batch size per GPU/CPU for evaluation.'''		,		)
		parser.add_argument(
		    '''--recalculate'''		,		help='''Recalculate predictions even if the prediction file exists'''		,		action='''store_true'''		,		)
		parser.add_argument(
		    '''--num_beams'''		,		default=4		,		type=a_		,		help='''Number of beams to be used when generating answers'''		,		)
		parser.add_argument('''--min_length'''		,		default=1		,		type=a_		,		help='''Min length of the generated answers'''     )
		parser.add_argument('''--max_length'''		,		default=50		,		type=a_		,		help='''Max length of the generated answers'''     )

		parser.add_argument(
		    '''--print_predictions'''		,		action='''store_true'''		,		help='''If True, prints predictions while evaluating.'''		,		)
		parser.add_argument(
		    '''--print_docs'''		,		action='''store_true'''		,		help='''If True, prints docs retried while generating.'''		,		)
		SCREAMING_SNAKE_CASE       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= infer_model_type(args.model_name_or_path     )
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= args.n_docs
				if args.index_name is not None:
						SCREAMING_SNAKE_CASE       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     Optional[int]				= (
		    [f.path for f in os.scandir(args.model_name_or_path     ) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info('''Evaluate the following checkpoints: %s'''		,		a_     )

		SCREAMING_SNAKE_CASE       :     int				= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path     ) and (not args.recalculate):
						logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path     )     )
						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(a_     )     )
				logger.info('''  Batch size = %d'''		,		args.eval_batch_size     )
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path     )     )

				if args.model_type.startswith('''rag'''     ):
						SCREAMING_SNAKE_CASE       :     Dict				= RagRetriever.from_pretrained(a_		,		**a_     )
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_class.from_pretrained(a_		,		retriever=a_		,		**a_     )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE       :     str				= model_class.from_pretrained(a_		,		**a_     )
				model.to(args.device     )

				with open(args.evaluation_set		,		'''r'''     ) as eval_file, open(args.predictions_path		,		'''w'''     ) as preds_file:
						SCREAMING_SNAKE_CASE       :     Dict				= []
						for line in tqdm(a_     ):
								questions.append(line.strip()     )
								if len(a_     ) == args.eval_batch_size:
										SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
										preds_file.write('''\n'''.join(a_     ) + '''\n'''     )
										preds_file.flush()
										SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
						if len(a_     ) > 0:
								SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
								preds_file.write('''\n'''.join(a_     )     )
								preds_file.flush()

						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





