"""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


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

transformers_logging.set_verbosity_info()







def    UpperCAmelCase       (						UpperCAmelCase   )							-> Union[str, Any]:
     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		,   UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     return max(metric_fn(UpperCAmelCase		,   UpperCAmelCase   ) for gt in ground_truths   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   [line.strip() for line in open(UpperCAmelCase		,   'r'   ).readlines()]
     snake_case_   			=   []

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

     snake_case_   			=   snake_case_   			=   snake_case_   			=   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   )

     snake_case_   			=   100.0 * em / total
     snake_case_   			=   100.0 * fa / total

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> List[Any]:
     snake_case_   			=   args.k
     snake_case_   			=   [line.strip() for line in open(UpperCAmelCase		,   'r'   ).readlines()]
     snake_case_   			=   [line.strip() for line in open(UpperCAmelCase		,   'r'   ).readlines()]

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

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Any:
     def strip_title(UpperCAmelCase   ):
          if title.startswith('"'   ):
               snake_case_   			=   title[1:]
          if title.endswith('"'   ):
               snake_case_   			=   title[:-1]
          return title

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

     snake_case_   			=   rag_model.rag.question_encoder(UpperCAmelCase   )
     snake_case_   			=   question_enc_outputs[0]

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     with torch.no_grad():
          snake_case_   			=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              UpperCAmelCase		,   return_tensors='pt'		,   padding=UpperCAmelCase		,   truncation=UpperCAmelCase   )

          snake_case_   			=   inputs_dict.input_ids.to(args.device   )
          snake_case_   			=   inputs_dict.attention_mask.to(args.device   )
          snake_case_   			=   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]]		,   )
          snake_case_   			=   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       (						)							-> List[str]:
     snake_case_   			=   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.'		,   )
     snake_case_   			=   parser.parse_args()
     snake_case_   			=   torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )
     return args







def    UpperCAmelCase       (						UpperCAmelCase   )							-> Optional[Any]:
     snake_case_   			=   {}
     if args.model_type is None:
          snake_case_   			=   infer_model_type(args.model_name_or_path   )
          assert args.model_type is not None
     if args.model_type.startswith('rag'   ):
          snake_case_   			=   RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
          snake_case_   			=   args.n_docs
          if args.index_name is not None:
               snake_case_   			=   args.index_name
          if args.index_path is not None:
               snake_case_   			=   args.index_path
     else:
          snake_case_   			=   BartForConditionalGeneration

     snake_case_   			=   (
         [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   )

     snake_case_   			=   get_scores if args.eval_mode == 'e2e' else get_precision_at_k
     snake_case_   			=   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'   ):
               snake_case_   			=   RagRetriever.from_pretrained(UpperCAmelCase		,   **UpperCAmelCase   )
               snake_case_   			=   model_class.from_pretrained(UpperCAmelCase		,   retriever=UpperCAmelCase		,   **UpperCAmelCase   )
               model.retriever.init_retrieval()
          else:
               snake_case_   			=   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:
               snake_case_   			=   []
               for line in tqdm(UpperCAmelCase   ):
                    questions.append(line.strip()   )
                    if len(UpperCAmelCase   ) == args.eval_batch_size:
                         snake_case_   			=   evaluate_batch_fn(UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )
                         preds_file.write('\n'.join(UpperCAmelCase   ) + '\n'   )
                         preds_file.flush()
                         snake_case_   			=   []
               if len(UpperCAmelCase   ) > 0:
                    snake_case_   			=   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)


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

