






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


__snake_case     :     Union[str, Any]       =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]		)	-> Optional[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 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       List[str]		)	-> Optional[int]:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    [line.strip() for line in open(UpperCamelCase_     ,							'r'		).readlines()]
						lowerCAmelCase__         =    []

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

						lowerCAmelCase__         =    lowerCAmelCase__         =    lowerCAmelCase__         =    0
						for prediction, ground_truths in zip(UpperCamelCase_     ,							UpperCamelCase_		):
												total += 1
												em += metric_max_over_ground_truths(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)
												fa += metric_max_over_ground_truths(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    100.0 * em / total
						lowerCAmelCase__         =    100.0 * fa / total

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       List[str]     ,							UpperCamelCase_							:       Dict		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    args.k
						lowerCAmelCase__         =    [line.strip() for line in open(UpperCamelCase_     ,							'r'		).readlines()]
						lowerCAmelCase__         =    [line.strip() for line in open(UpperCamelCase_     ,							'r'		).readlines()]

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

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Any		)	-> List[str]:

						"""simple docstring"""



						def strip_title(UpperCamelCase_							:       Tuple		):
												if title.startswith('"'		):
																		lowerCAmelCase__         =    title[1:]
												if title.endswith('"'		):
																		lowerCAmelCase__         =    title[:-1]
												return title

						lowerCAmelCase__         =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    UpperCamelCase_     ,							return_tensors='pt'     ,							padding=UpperCamelCase_     ,							truncation=UpperCamelCase_     ,							)['input_ids'].to(args.device		)

						lowerCAmelCase__         =    rag_model.rag.question_encoder(UpperCamelCase_		)
						lowerCAmelCase__         =    question_enc_outputs[0]

						lowerCAmelCase__         =    rag_model.retriever(
						    UpperCamelCase_     ,							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'     ,							)
						lowerCAmelCase__         =    rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
						lowerCAmelCase__         =    []
						for docs in all_docs:
												lowerCAmelCase__         =    [strip_title(UpperCamelCase_		) for title in docs['title']]
												provenance_strings.append('\t'.join(UpperCamelCase_		)		)
						return provenance_strings







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       List[str]     ,							UpperCamelCase_							:       str		)	-> Optional[Any]:

						"""simple docstring"""



						with torch.no_grad():
												lowerCAmelCase__         =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    UpperCamelCase_     ,							return_tensors='pt'     ,							padding=UpperCamelCase_     ,							truncation=UpperCamelCase_		)

												lowerCAmelCase__         =    inputs_dict.input_ids.to(args.device		)
												lowerCAmelCase__         =    inputs_dict.attention_mask.to(args.device		)
												lowerCAmelCase__         =    rag_model.generate(  # rag_model overwrites generate
												    UpperCamelCase_     ,							attention_mask=UpperCamelCase_     ,							num_beams=args.num_beams     ,							min_length=args.min_length     ,							max_length=args.max_length     ,							early_stopping=UpperCamelCase_     ,							num_return_sequences=1     ,							bad_words_ids=[[0, 0]]     ,							)
												lowerCAmelCase__         =    rag_model.retriever.generator_tokenizer.batch_decode(UpperCamelCase_     ,							skip_special_tokens=UpperCamelCase_		)

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

												return answers







def 						_UpperCamelCase (     )	-> Optional[int]:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       Any		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}
						if args.model_type is None:
												lowerCAmelCase__         =    infer_model_type(args.model_name_or_path		)
												assert args.model_type is not None
						if args.model_type.startswith('rag'		):
												lowerCAmelCase__         =    RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
												lowerCAmelCase__         =    args.n_docs
												if args.index_name is not None:
																		lowerCAmelCase__         =    args.index_name
												if args.index_path is not None:
																		lowerCAmelCase__         =    args.index_path
						else:
												lowerCAmelCase__         =    BartForConditionalGeneration

						lowerCAmelCase__         =    (
						    [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'     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    get_scores if args.eval_mode == 'e2e' else get_precision_at_k
						lowerCAmelCase__         =    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(UpperCamelCase_     ,							args.predictions_path     ,							args.gold_data_path		)
																		continue

												logger.info('***** Running evaluation for {} *****'.format(UpperCamelCase_		)		)
												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'		):
																		lowerCAmelCase__         =    RagRetriever.from_pretrained(UpperCamelCase_     ,							**UpperCamelCase_		)
																		lowerCAmelCase__         =    model_class.from_pretrained(UpperCamelCase_     ,							retriever=UpperCamelCase_     ,							**UpperCamelCase_		)
																		model.retriever.init_retrieval()
												else:
																		lowerCAmelCase__         =    model_class.from_pretrained(UpperCamelCase_     ,							**UpperCamelCase_		)
												model.to(args.device		)

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

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


if __name__ == "__main__":
					__snake_case     :     List[Any]       =  get_args()
					main(args)













import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__snake_case     :     Optional[int]       =  """\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

__snake_case     :     Any       =  """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

__snake_case     :     Optional[int]       =  """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION)
class      __SCREAMING_SNAKE_CASE       ( datasets.Metric):






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION  ,    citation=_CITATION  ,    homepage='https://github.com/chakki-works/seqeval'  ,    inputs_description=_KWARGS_DESCRIPTION  ,    features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('string'  ,    id='label' )  ,    id='sequence' ),
												            'references': datasets.Sequence(datasets.Value('string'  ,    id='label' )  ,    id='sequence' ),
												        } )  ,    codebase_urls=['https://github.com/chakki-works/seqeval']  ,    reference_urls=['https://github.com/chakki-works/seqeval']  ,    )







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = "warn"  ,    ):






												"""simple docstring"""
												if scheme is not None:
																		try:
																								lowerCAmelCase__         =    importlib.import_module('seqeval.scheme' )
																								lowerCAmelCase__         =    getattr(_UpperCamelCase  ,    _UpperCamelCase )
																		except AttributeError:
																								raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}" )
												lowerCAmelCase__         =    classification_report(
												    y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase  ,    suffix=_UpperCamelCase  ,    output_dict=_UpperCamelCase  ,    scheme=_UpperCamelCase  ,    mode=_UpperCamelCase  ,    sample_weight=_UpperCamelCase  ,    zero_division=_UpperCamelCase  ,    )
												report.pop('macro avg' )
												report.pop('weighted avg' )
												lowerCAmelCase__         =    report.pop('micro avg' )

												lowerCAmelCase__         =    {
												    type_name: {
												        'precision': score['precision'],
												        'recall': score['recall'],
												        'f1': score['f1-score'],
												        'number': score['support'],
												    }
												    for type_name, score in report.items()
												}
												lowerCAmelCase__         =    overall_score['precision']
												lowerCAmelCase__         =    overall_score['recall']
												lowerCAmelCase__         =    overall_score['f1-score']
												lowerCAmelCase__         =    accuracy_score(y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase )

												return scores





