


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


a					:				Dict       =      logging.get_logger(__name__)

a					:				List[Any]       =      {'vocab_file': 'vocab.txt'}

a					:				int       =      {
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

a					:				Any       =      {
    'tau/splinter-base': 512,
    'tau/splinter-base-qass': 512,
    'tau/splinter-large': 512,
    'tau/splinter-large-qass': 512,
}

a					:				List[Any]       =      {
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   VOCAB_FILES_NAMES
       _UpperCamelCase     		=   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase     		=   PRETRAINED_INIT_CONFIGURATION
       _UpperCamelCase     		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase     		=   SplinterTokenizer

       def __init__(      self						,    a__=None						,    a__=None						,    a__=True						,    a__="[UNK]"						,    a__="[SEP]"						,    a__="[PAD]"						,    a__="[CLS]"						,    a__="[MASK]"						,    a__="[QUESTION]"						,    a__=True						,    a__=None						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(
            a__						,    tokenizer_file=a__						,    do_lower_case=a__						,    unk_token=a__						,    sep_token=a__						,    pad_token=a__						,    cls_token=a__						,    mask_token=a__						,    tokenize_chinese_chars=a__						,    strip_accents=a__						,    additional_special_tokens=(question_token,)						,    **a__						,    )

        snake_case :			Optional[int]							=   json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
        if (
            pre_tok_state.get('''lowercase'''						,    a__       ) != do_lower_case
            or pre_tok_state.get('''strip_accents'''						,    a__       ) != strip_accents
        ):
         snake_case :			Optional[int]							=   getattr(a__						,    pre_tok_state.pop('''type'''       )       )
         snake_case :			Tuple							=   do_lower_case
         snake_case :			str							=   strip_accents
         snake_case :			Union[str, Any]							=   pre_tok_class(**a__       )

        snake_case :			Optional[Any]							=   do_lower_case

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.convert_tokens_to_ids(self.question_token       )

       def 					UpperCAmelCase					(      self						,    a__						,    a__ = None       ):






        '''simple docstring'''






        if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        snake_case :			Dict							=   [self.cls_token_id]
        snake_case :			List[str]							=   [self.sep_token_id]
        snake_case :			Tuple							=   [self.question_token_id] + [self.convert_tokens_to_ids('''.'''       )]
        if self.padding_side == "right":
         # Input is question-then-context
         return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
        else:
         # Input is context-then-question
         return cls + token_ids_a + sep + token_ids_a + question_suffix + sep

       def 					UpperCAmelCase					(      self						,    a__						,    a__ = None       ):






        '''simple docstring'''






        snake_case :			List[str]							=   [self.sep_token_id]
        snake_case :			Any							=   [self.cls_token_id]
        snake_case :			List[str]							=   [self.question_token_id] + [self.convert_tokens_to_ids('''.'''       )]
        if token_ids_a is None:
         return len(cls + token_ids_a + sep       ) * [0]

        if self.padding_side == "right":
         # Input is question-then-context
         return len(cls + token_ids_a + question_suffix + sep       ) * [0] + len(token_ids_a + sep       ) * [1]
        else:
         # Input is context-then-question
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + question_suffix + sep       ) * [1]



       def 					UpperCAmelCase					(      self						,    a__						,    a__ = None       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   self._tokenizer.model.save(a__						,    name=a__       )
        return tuple(a__       )











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

transformers_logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Optional[Any]       )							->					str:







			'''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      (	_UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Optional[Any]       )							->					Dict:







			'''simple docstring'''
			return max(metric_fn(_UpperCAmelCase				,  _UpperCAmelCase       ) for gt in ground_truths       )





def 				A      (	_UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Optional[Any]				,  _UpperCAmelCase							:  str       )							->					Any:







			'''simple docstring'''
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]
			_UpperCAmelCase									=  []

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

			_UpperCAmelCase									=  _UpperCAmelCase									=  _UpperCAmelCase									=  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       )

			_UpperCAmelCase									=  100.0 * em / total
			_UpperCAmelCase									=  100.0 * fa / total

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





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Any       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  args.k
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]

			_UpperCAmelCase									=  _UpperCAmelCase									=  0
			for hypo, reference in zip(_UpperCAmelCase				,  _UpperCAmelCase       ):
						_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      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Union[str, Any]       )							->					List[Any]:







			'''simple docstring'''
			def strip_title(_UpperCAmelCase							:  List[Any]       ):
						if title.startswith('"'       ):
									_UpperCAmelCase									=  title[1:]
						if title.endswith('"'       ):
									_UpperCAmelCase									=  title[:-1]
						return title

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

			_UpperCAmelCase									=  rag_model.rag.question_encoder(_UpperCAmelCase       )
			_UpperCAmelCase									=  question_enc_outputs[0]

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





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  List[str]				,  _UpperCAmelCase							:  Optional[Any]       )							->					str:







			'''simple docstring'''
			with torch.no_grad():
						_UpperCAmelCase									=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    _UpperCAmelCase				,  return_tensors='pt'				,  padding=_UpperCAmelCase				,  truncation=_UpperCAmelCase       )

						_UpperCAmelCase									=  inputs_dict.input_ids.to(args.device       )
						_UpperCAmelCase									=  inputs_dict.attention_mask.to(args.device       )
						_UpperCAmelCase									=  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]]				,  )
						_UpperCAmelCase									=  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 				A      (	)							->					Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  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.'				,  )
			_UpperCAmelCase									=  parser.parse_args()
			_UpperCAmelCase									=  torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
			return args





def 				A      (	_UpperCAmelCase							:  Any       )							->					int:







			'''simple docstring'''
			_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'				,  _UpperCAmelCase       )

			_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(_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'       ):
									_UpperCAmelCase									=  RagRetriever.from_pretrained(_UpperCAmelCase				,  **_UpperCAmelCase       )
									_UpperCAmelCase									=  model_class.from_pretrained(_UpperCAmelCase				,  retriever=_UpperCAmelCase				,  **_UpperCAmelCase       )
									model.retriever.init_retrieval()
						else:
									_UpperCAmelCase									=  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:
									_UpperCAmelCase									=  []
									for line in tqdm(_UpperCAmelCase       ):
												questions.append(line.strip()       )
												if len(_UpperCAmelCase       ) == args.eval_batch_size:
															_UpperCAmelCase									=  evaluate_batch_fn(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )
															preds_file.write('\n'.join(_UpperCAmelCase       ) + '\n'       )
															preds_file.flush()
															_UpperCAmelCase									=  []
									if len(_UpperCAmelCase       ) > 0:
												_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			get_args()
							main(args)
