





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


UpperCamelCase__ :			List[Any]       	=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def  A_(      A ):
	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					,   A					,   A ):
	return max(metric_fn(A					,   A ) for gt in ground_truths )




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  [line.strip() for line in open(A					,   """r""" ).readlines()]
	UpperCAmelCase_		    =  []

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

	UpperCAmelCase_		    =  UpperCAmelCase_		    =  UpperCAmelCase_		    =  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 )

	UpperCAmelCase_		    =  100.0 * em / total
	UpperCAmelCase_		    =  100.0 * fa / total

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




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  args.k
	UpperCAmelCase_		    =  [line.strip() for line in open(A					,   """r""" ).readlines()]
	UpperCAmelCase_		    =  [line.strip() for line in open(A					,   """r""" ).readlines()]

	UpperCAmelCase_		    =  UpperCAmelCase_		    =  0
	for hypo, reference in zip(A					,   A ):
		UpperCAmelCase_		    =  set(hypo.split("""\t""" )[:k] )
		UpperCAmelCase_		    =  set(reference.split("""\t""" ) )
		total += 1
		em += len(hypo_provenance & ref_provenance ) / k

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




def  A_(      A					,   A					,   A ):
	def strip_title(A ):
		if title.startswith("""\"""" ):
			UpperCAmelCase_		    =  title[1:]
		if title.endswith("""\"""" ):
			UpperCAmelCase_		    =  title[:-1]
		return title

	UpperCAmelCase_		    =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    A					,   return_tensors="""pt"""					,   padding=A					,   truncation=A					,   )["""input_ids"""].to(args.device )

	UpperCAmelCase_		    =  rag_model.rag.question_encoder(A )
	UpperCAmelCase_		    =  question_enc_outputs[0]

	UpperCAmelCase_		    =  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"""					,   )
	UpperCAmelCase_		    =  rag_model.retriever.index.get_doc_dicts(result.doc_ids )
	UpperCAmelCase_		    =  []
	for docs in all_docs:
		UpperCAmelCase_		    =  [strip_title(A ) for title in docs["""title"""]]
		provenance_strings.append("""\t""".join(A ) )
	return provenance_strings




def  A_(      A					,   A					,   A ):
	with torch.no_grad():
		UpperCAmelCase_		    =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    A					,   return_tensors="""pt"""					,   padding=A					,   truncation=A )

		UpperCAmelCase_		    =  inputs_dict.input_ids.to(args.device )
		UpperCAmelCase_		    =  inputs_dict.attention_mask.to(args.device )
		UpperCAmelCase_		    =  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]]					,   )
		UpperCAmelCase_		    =  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_(      ):
	UpperCAmelCase_		    =  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."""					,   )
	UpperCAmelCase_		    =  parser.parse_args()
	UpperCAmelCase_		    =  torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
	return args




def  A_(      A ):
	UpperCAmelCase_		    =  {}
	if args.model_type is None:
		UpperCAmelCase_		    =  infer_model_type(args.model_name_or_path )
		assert args.model_type is not None
	if args.model_type.startswith("""rag""" ):
		UpperCAmelCase_		    =  RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
		UpperCAmelCase_		    =  args.n_docs
		if args.index_name is not None:
			UpperCAmelCase_		    =  args.index_name
		if args.index_path is not None:
			UpperCAmelCase_		    =  args.index_path
	else:
		UpperCAmelCase_		    =  BartForConditionalGeneration

	UpperCAmelCase_		    =  (
	    [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 )

	UpperCAmelCase_		    =  get_scores if args.eval_mode == """e2e""" else get_precision_at_k
	UpperCAmelCase_		    =  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""" ):
			UpperCAmelCase_		    =  RagRetriever.from_pretrained(A					,   **A )
			UpperCAmelCase_		    =  model_class.from_pretrained(A					,   retriever=A					,   **A )
			model.retriever.init_retrieval()
		else:
			UpperCAmelCase_		    =  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:
			UpperCAmelCase_		    =  []
			for line in tqdm(A ):
				questions.append(line.strip() )
				if len(A ) == args.eval_batch_size:
					UpperCAmelCase_		    =  evaluate_batch_fn(A					,   A					,   A )
					preds_file.write("""\n""".join(A ) + """\n""" )
					preds_file.flush()
					UpperCAmelCase_		    =  []
			if len(A ) > 0:
				UpperCAmelCase_		    =  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__":
				UpperCamelCase__ :			Optional[int]       	=			get_args()
				main(args)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :			Any       	=			{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Any       	=			["""SplinterTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Union[str, Any]       	=			[
				    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SplinterForQuestionAnswering""",
				    """SplinterForPreTraining""",
				    """SplinterLayer""",
				    """SplinterModel""",
				    """SplinterPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
				from .tokenization_splinter import SplinterTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_splinter_fast import SplinterTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_splinter import (
								    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SplinterForPreTraining,
								    SplinterForQuestionAnswering,
								    SplinterLayer,
								    SplinterModel,
								    SplinterPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__ :			List[str]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


