"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase_     =	logging.getLogger(__name__)




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				a			=						field(
				    default="NER"   ,       metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
				a			=						field(default=__snake_case   ,       metadata={"help": "Set this flag to use fast tokenization."} )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,       )




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}   ,       )
				a			=						field(
				    default=1_28   ,       metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Overwrite the cached training and evaluation sets"} )



def      lowerCamelCase_()->    int:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
							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  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Tuple			  =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir  )
							    and os.listdir(training_args.output_dir  )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    """ --overwrite_output_dir to overcome."""  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    import_module("""tasks"""  )
							try:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    getattr(__SCREAMING_SNAKE_CASE     ,	model_args.task_type  )
														_SCREAMING_SNAKE_CASE		:						TokenClassificationTask			  =    token_classification_task_clazz()
							except AttributeError:
														raise ValueError(
														    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
														    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""  )

							# 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.local_rank != -1  )     ,	training_args.fpaa     ,	)
							# 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()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""     ,	__SCREAMING_SNAKE_CASE  )

							# Set seed
							set_seed(training_args.seed  )

							# Prepare CONLL-2003 task
							_SCREAMING_SNAKE_CASE		:						Dict			  =    token_classification_task.get_labels(data_args.labels  )
							_SCREAMING_SNAKE_CASE		:						Dict[int, str]			  =    dict(enumerate(__SCREAMING_SNAKE_CASE  )  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    len(__SCREAMING_SNAKE_CASE  )

							# 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		:						int			  =    AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,	num_labels=__SCREAMING_SNAKE_CASE     ,	idalabel=__SCREAMING_SNAKE_CASE     ,	labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE  )}     ,	cache_dir=model_args.cache_dir     ,	)
							_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     ,	use_fast=model_args.use_fast     ,	)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path     ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )     ,	config=__SCREAMING_SNAKE_CASE     ,	cache_dir=model_args.cache_dir     ,	)

							# Get datasets
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    (
							    TokenClassificationDataset(
							        token_classification_task=__SCREAMING_SNAKE_CASE     ,	data_dir=data_args.data_dir     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE     ,	model_type=config.model_type     ,	max_seq_length=data_args.max_seq_length     ,	overwrite_cache=data_args.overwrite_cache     ,	mode=Split.train     ,	)
							    if training_args.do_train
							    else None
							)
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    (
							    TokenClassificationDataset(
							        token_classification_task=__SCREAMING_SNAKE_CASE     ,	data_dir=data_args.data_dir     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE     ,	model_type=config.model_type     ,	max_seq_length=data_args.max_seq_length     ,	overwrite_cache=data_args.overwrite_cache     ,	mode=Split.dev     ,	)
							    if training_args.do_eval
							    else None
							)

							def align_predictions(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) -> Tuple[List[int], List[int]]:
														_SCREAMING_SNAKE_CASE		:						int			  =    np.argmax(__SCREAMING_SNAKE_CASE     ,	axis=2  )

														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    preds.shape

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [[] for _ in range(__SCREAMING_SNAKE_CASE  )]
														_SCREAMING_SNAKE_CASE		:						Dict			  =    [[] for _ in range(__SCREAMING_SNAKE_CASE  )]

														for i in range(__SCREAMING_SNAKE_CASE  ):
																					for j in range(__SCREAMING_SNAKE_CASE  ):
																												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																																			out_label_list[i].append(label_map[label_ids[i][j]]  )
																																			preds_list[i].append(label_map[preds[i][j]]  )

														return preds_list, out_label_list

							def compute_metrics(__SCREAMING_SNAKE_CASE  ) -> Dict:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    align_predictions(p.predictions     ,	p.label_ids  )
														return {
														    "accuracy_score": accuracy_score(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ),
														    "precision": precision_score(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ),
														    "recall": recall_score(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ),
														    "f1": fa_score(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ),
														}

							# Data collator
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataCollatorWithPadding(__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=8  ) if training_args.fpaa else None

							# Initialize our Trainer
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    Trainer(
							    model=__SCREAMING_SNAKE_CASE     ,	args=__SCREAMING_SNAKE_CASE     ,	train_dataset=__SCREAMING_SNAKE_CASE     ,	eval_dataset=__SCREAMING_SNAKE_CASE     ,	compute_metrics=__SCREAMING_SNAKE_CASE     ,	data_collator=__SCREAMING_SNAKE_CASE     ,	)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_process_zero():
																					tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""  )

														_SCREAMING_SNAKE_CASE		:						str			  =    trainer.evaluate()

														_SCREAMING_SNAKE_CASE		:						Dict			  =    os.path.join(training_args.output_dir     ,	"""eval_results.txt"""  )
														if trainer.is_world_process_zero():
																					with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as writer:
																												logger.info("""***** Eval results *****"""  )
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			writer.write("""%s = %s\n""" % (key, value)  )

																					results.update(__SCREAMING_SNAKE_CASE  )

    # Predict
							if training_args.do_predict:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    TokenClassificationDataset(
														    token_classification_task=__SCREAMING_SNAKE_CASE     ,	data_dir=data_args.data_dir     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE     ,	model_type=config.model_type     ,	max_seq_length=data_args.max_seq_length     ,	overwrite_cache=data_args.overwrite_cache     ,	mode=Split.test     ,	)

														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    trainer.predict(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    align_predictions(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Any			  =    os.path.join(training_args.output_dir     ,	"""test_results.txt"""  )
														if trainer.is_world_process_zero():
																					with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as writer:
																												for key, value in metrics.items():
																																			logger.info("""  %s = %s"""     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			writer.write("""%s = %s\n""" % (key, value)  )

        # Save predictions
														_SCREAMING_SNAKE_CASE		:						str			  =    os.path.join(training_args.output_dir     ,	"""test_predictions.txt"""  )
														if trainer.is_world_process_zero():
																					with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as writer:
																												with open(os.path.join(data_args.data_dir     ,	"""test.txt"""  )     ,	"""r"""  ) as f:
																																			token_classification_task.write_predictions_to_file(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							return results



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[Any]:
							# For xla_spawn (TPUs)
							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_     =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							return max(metric_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for gt in ground_truths  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []

							if args.gold_data_mode == "qa":
														_SCREAMING_SNAKE_CASE		:						int			  =    pd.read_csv(__SCREAMING_SNAKE_CASE     ,	sep="""\t"""     ,	header=__SCREAMING_SNAKE_CASE  )
														for answer_list in data[1]:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ast.literal_eval(__SCREAMING_SNAKE_CASE  )
																					answers.append(__SCREAMING_SNAKE_CASE  )
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [[reference] for reference in references]

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0
							for prediction, ground_truths in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														total += 1
														em += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														fa += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    1_00.0 * em / total
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_00.0 * fa / total

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    args.k
							_SCREAMING_SNAKE_CASE		:						int			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Any			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0
							for hypo, reference in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    set(hypo.split("""\t"""  )[:k]  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    set(reference.split("""\t"""  )  )
														total += 1
														em += len(hypo_provenance & ref_provenance  ) / k

							_SCREAMING_SNAKE_CASE		:						int			  =    1_00.0 * em / total
							logger.info(F"""Precision@{k}: {em: .2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							def strip_title(__SCREAMING_SNAKE_CASE  ):
														if title.startswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    title[1:]
														if title.endswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    title[:-1]
														return title

							_SCREAMING_SNAKE_CASE		:						Dict			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	)["""input_ids"""].to(args.device  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    question_enc_outputs[0]

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    rag_model.retriever(
							    __SCREAMING_SNAKE_CASE     ,	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		:						Optional[int]			  =    rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							for docs in all_docs:
														_SCREAMING_SNAKE_CASE		:						str			  =    [strip_title(__SCREAMING_SNAKE_CASE  ) for title in docs["""title"""]]
														provenance_strings.append("""\t""".join(__SCREAMING_SNAKE_CASE  )  )
							return provenance_strings



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE  )

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

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

														return answers



def      lowerCamelCase_()->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    argparse.ArgumentParser()
							parser.add_argument(
							    """--model_type"""     ,	choices=["""rag_sequence""", """rag_token""", """bart"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	choices=["""exact""", """compressed""", """legacy"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""RAG model retriever type"""     ,	)
							parser.add_argument(
							    """--index_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Path to the retrieval index"""     ,	)
							parser.add_argument("""--n_docs"""     ,	default=5     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Number of retrieved docs"""  )
							parser.add_argument(
							    """--model_name_or_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""     ,	)
							parser.add_argument(
							    """--eval_mode"""     ,	choices=["""e2e""", """retrieval"""]     ,	default="""e2e"""     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
							        """ precision@k."""
							    )     ,	)
							parser.add_argument("""--k"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""k for the precision@k calculation"""  )
							parser.add_argument(
							    """--evaluation_set"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a file containing evaluation samples"""     ,	)
							parser.add_argument(
							    """--gold_data_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a tab-separated file with gold samples"""     ,	)
							parser.add_argument(
							    """--gold_data_mode"""     ,	default="""qa"""     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	help="""Number of beams to be used when generating answers"""     ,	)
							parser.add_argument("""--min_length"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Min length of the generated answers"""  )
							parser.add_argument("""--max_length"""     ,	default=50     ,	type=__SCREAMING_SNAKE_CASE     ,	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		:						Dict			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Any			  =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
							return args



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}
							if args.model_type is None:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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[Any]			  =    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		:						Optional[Any]			  =    args.index_name
														if args.index_path is not None:
																					_SCREAMING_SNAKE_CASE		:						Any			  =    args.index_path
							else:
														_SCREAMING_SNAKE_CASE		:						Any			  =    BartForConditionalGeneration

							_SCREAMING_SNAKE_CASE		:						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"""     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_scores if args.eval_mode == """e2e""" else get_precision_at_k
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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(__SCREAMING_SNAKE_CASE     ,	args.predictions_path     ,	args.gold_data_path  )
																					continue

														logger.info("""***** Running evaluation for {} *****""".format(__SCREAMING_SNAKE_CASE  )  )
														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		:						str			  =    RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	retriever=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					model.retriever.init_retrieval()
														else:
																					_SCREAMING_SNAKE_CASE		:						str			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
														model.to(args.device  )

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

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


if __name__ == "__main__":
			lowerCAmelCase_     =	get_args()
			main(args)


