

'''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


SCREAMING_SNAKE_CASE_    =   logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def   lowerCamelCase__       (      a__)		->    Any:



    """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   lowerCamelCase__       (      a__			,      a__			,      a__)		->    Any:



    """simple docstring"""







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

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    List[str]:



    """simple docstring"""







    _snake_case : str												=		[line.strip() for line in open(snake_case__			,      'r').readlines()]
    _snake_case : Union[str, Any]												=		[]

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

    _snake_case : int												=		0
    for prediction, ground_truths in zip(snake_case__			,      snake_case__):
        total += 1
        em += metric_max_over_ground_truths(snake_case__			,      snake_case__			,      snake_case__)
        fa += metric_max_over_ground_truths(snake_case__			,      snake_case__			,      snake_case__)

    _snake_case : List[Any]												=		1_0_0.0 * em / total
    _snake_case : Dict												=		1_0_0.0 * fa / total

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

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    str:



    """simple docstring"""







    _snake_case : Dict												=		args.k
    _snake_case : str												=		[line.strip() for line in open(snake_case__			,      'r').readlines()]
    _snake_case : str												=		[line.strip() for line in open(snake_case__			,      'r').readlines()]

    _snake_case : Optional[int]												=		0
    for hypo, reference in zip(snake_case__			,      snake_case__):
        _snake_case : Any												=		set(hypo.split('\t')[:k])
        _snake_case : Optional[Any]												=		set(reference.split('\t'))
        total += 1
        em += len(hypo_provenance & ref_provenance) / k

    _snake_case : Dict												=		1_0_0.0 * em / total
    logger.info(F"""Precision@{k}: {em: .2f}""")

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    Optional[int]:



    """simple docstring"""







    def strip_title(a__):
        if title.startswith('"'):
            _snake_case : Union[str, Any]												=		title[1:]
        if title.endswith('"'):
            _snake_case : Optional[int]												=		title[:-1]
        return title

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

    _snake_case : List[Any]												=		rag_model.rag.question_encoder(snake_case__)
    _snake_case : Optional[int]												=		question_enc_outputs[0]

    _snake_case : Union[str, Any]												=		rag_model.retriever(
        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'			,      )
    _snake_case : List[Any]												=		rag_model.retriever.index.get_doc_dicts(result.doc_ids)
    _snake_case : List[str]												=		[]
    for docs in all_docs:
        _snake_case : Any												=		[strip_title(snake_case__) for title in docs['title']]
        provenance_strings.append('\t'.join(snake_case__))
    return provenance_strings

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    Dict:



    """simple docstring"""







    with torch.no_grad():
        _snake_case : str												=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            snake_case__			,      return_tensors='pt'			,      padding=snake_case__			,      truncation=snake_case__)

        _snake_case : Optional[int]												=		inputs_dict.input_ids.to(args.device)
        _snake_case : List[str]												=		inputs_dict.attention_mask.to(args.device)
        _snake_case : Dict												=		rag_model.generate(  # rag_model overwrites generate
            snake_case__			,      attention_mask=snake_case__			,      num_beams=args.num_beams			,      min_length=args.min_length			,      max_length=args.max_length			,      early_stopping=snake_case__			,      num_return_sequences=1			,      bad_words_ids=[[0, 0]]			,      )
        _snake_case : str												=		rag_model.retriever.generator_tokenizer.batch_decode(snake_case__			,      skip_special_tokens=snake_case__)

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

        return answers

def   lowerCamelCase__       (      )		->    Optional[int]:



    """simple docstring"""







    _snake_case : str												=		argparse.ArgumentParser()
    parser.add_argument(
        '--model_type'			,      choices=['rag_sequence', 'rag_token', 'bart']			,      type=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=snake_case__			,      choices=['exact', 'compressed', 'legacy']			,      type=snake_case__			,      help='RAG model retriever type'			,      )
    parser.add_argument(
        '--index_path'			,      default=snake_case__			,      type=snake_case__			,      help='Path to the retrieval index'			,      )
    parser.add_argument('--n_docs'			,      default=5			,      type=snake_case__			,      help='Number of retrieved docs')
    parser.add_argument(
        '--model_name_or_path'			,      default=snake_case__			,      type=snake_case__			,      required=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=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=snake_case__			,      help='k for the precision@k calculation')
    parser.add_argument(
        '--evaluation_set'			,      default=snake_case__			,      type=snake_case__			,      required=snake_case__			,      help='Path to a file containing evaluation samples'			,      )
    parser.add_argument(
        '--gold_data_path'			,      default=snake_case__			,      type=snake_case__			,      required=snake_case__			,      help='Path to a tab-separated file with gold samples'			,      )
    parser.add_argument(
        '--gold_data_mode'			,      default='qa'			,      type=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=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=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=snake_case__			,      help='Number of beams to be used when generating answers'			,      )
    parser.add_argument('--min_length'			,      default=1			,      type=snake_case__			,      help='Min length of the generated answers')
    parser.add_argument('--max_length'			,      default=5_0			,      type=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.'			,      )
    _snake_case : List[str]												=		parser.parse_args()
    _snake_case : List[Any]												=		torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    return args

def   lowerCamelCase__       (      a__)		->    Any:



    """simple docstring"""







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

    _snake_case : Tuple												=		(
        [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'			,      snake_case__)

    _snake_case : Optional[int]												=		get_scores if args.eval_mode == 'e2e' else get_precision_at_k
    _snake_case : List[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(snake_case__			,      args.predictions_path			,      args.gold_data_path)
            continue

        logger.info('***** Running evaluation for {} *****'.format(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'):
            _snake_case : int												=		RagRetriever.from_pretrained(snake_case__			,      **snake_case__)
            _snake_case : Optional[Any]												=		model_class.from_pretrained(snake_case__			,      retriever=snake_case__			,      **snake_case__)
            model.retriever.init_retrieval()
        else:
            _snake_case : Any												=		model_class.from_pretrained(snake_case__			,      **snake_case__)
        model.to(args.device)

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

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


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






'''simple docstring'''



from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
   import torch

SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
   from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

   def   lowerCamelCase__       (      cls)		->    Optional[Any]:



       """simple docstring"""







       return cls

def   lowerCamelCase__       (      a__			,      a__ = None			,      a__ = None			,      a__ = None			,      a__ = None			,      )		->    Dict:



    """simple docstring"""







    _snake_case : Optional[Any]												=		device
    _snake_case : Any												=		shape[0]

    _snake_case : str												=		layout or torch.strided
    _snake_case : str												=		device or torch.device('cpu')

    if generator is not None:
        _snake_case : Any												=		generator.device.type if not isinstance(a__			,      a__) else generator[0].device.type
        if gen_device_type != device.type and gen_device_type == "cpu":
            _snake_case : Optional[Any]												=		'cpu'
            if device != "mps":
                logger.info(
                    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                    F""" slighly speed up this function by passing a generator that was created on the {device} device.""")
        elif gen_device_type != device.type and gen_device_type == "cuda":
            raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}.""")

    if isinstance(a__			,      a__):
        _snake_case : Any												=		(1,) + shape[1:]
        _snake_case : int												=		[
            torch.randn(a__			,      generator=generator[i]			,      device=a__			,      dtype=a__			,      layout=a__)
            for i in range(a__)
        ]
        _snake_case : Optional[int]												=		torch.cat(a__			,      dim=0).to(a__)
    else:
        _snake_case : Optional[Any]												=		torch.randn(a__			,      generator=a__			,      device=a__			,      dtype=a__			,      layout=a__).to(a__)

    return latents




def   lowerCamelCase__       (      a__)		->    Optional[Any]:



    """simple docstring"""







    if is_torch_version('<'			,      '2.0.0') or not hasattr(a__			,      '_dynamo'):
        return False
    return isinstance(a__			,      torch._dynamo.eval_frame.OptimizedModule)



