'''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_:					int  =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def      lowerCAmelCase_    ( snake_case_	:			List[Any]      )   ->				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_    ( snake_case_	:			List[Any] ,   snake_case_	:			str ,   snake_case_	:			Any      )   ->				Optional[Any]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int] ,   snake_case_	:			List[str]      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       [line.strip() for line in open(snake_case_ ,   "r"      ).readlines()]
     UpperCAmelCase_	   =       []

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

     UpperCAmelCase_	   =       UpperCAmelCase_	   =       UpperCAmelCase_	   =       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_      )

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

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





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int] ,   snake_case_	:			Tuple      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       args.k
     UpperCAmelCase_	   =       [line.strip() for line in open(snake_case_ ,   "r"      ).readlines()]
     UpperCAmelCase_	   =       [line.strip() for line in open(snake_case_ ,   "r"      ).readlines()]

     UpperCAmelCase_	   =       UpperCAmelCase_	   =       0
     for hypo, reference in zip(snake_case_ ,   snake_case_      ):
          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      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			Optional[Any] ,   snake_case_	:			List[str]      )   ->				Tuple:


     '''simple docstring'''




     def strip_title(snake_case_	:			Optional[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(
         snake_case_ ,   return_tensors="pt" ,   padding=snake_case_ ,   truncation=snake_case_ ,   )["input_ids"].to(args.device      )

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

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





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			List[str] ,   snake_case_	:			str      )   ->				Tuple:


     '''simple docstring'''




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

          UpperCAmelCase_	   =       inputs_dict.input_ids.to(args.device      )
          UpperCAmelCase_	   =       inputs_dict.attention_mask.to(args.device      )
          UpperCAmelCase_	   =       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]] ,   )
          UpperCAmelCase_	   =       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_    ( )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       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=50 ,   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." ,   )
     UpperCAmelCase_	   =       parser.parse_args()
     UpperCAmelCase_	   =       torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
     return args





def      lowerCAmelCase_    ( snake_case_	:			Dict      )   ->				Any:


     '''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" ,   snake_case_      )

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





'''simple docstring'''





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   Dict       ,      __a   :   Any			):
         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       [label.strip() for label in labels.split(","			) if label.strip()]
         return labels






    def __call__(self   :   Union[str, Any]       ,      __a   :   Optional[int]       ,      __a   :   Optional[int]       ,      __a   :   int			):
         if len(__a			) == 0 or len(__a			) == 0:
              raise ValueError("You must include at least one label and at least one sequence."			)
         if hypothesis_template.format(labels[0]			) == hypothesis_template:
              raise ValueError(
                  (
                      "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
                      "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
                  ).format(__a			)			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       [sequences]

         UpperCAmelCase_	   =       []
         for sequence in sequences:
              sequence_pairs.extend([[sequence, hypothesis_template.format(__a			)] for label in labels]			)

         return sequence_pairs, sequences




@add_end_docstrings(UpperCamelCase__     )
class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Optional[Any]       ,      __a   :   Union[str, Any]=ZeroShotClassificationArgumentHandler()       ,      *__a   :   Optional[int]       ,      **__a   :   List[str]			):
         UpperCAmelCase_	   =       args_parser
         super().__init__(*__a       ,      **__a			)
         if self.entailment_id == -1:
              logger.warning(
                  "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
                  "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."			)



    @property
    def  _lowercase						(self   :   str			):
         for label, ind in self.model.config.labelaid.items():
              if label.lower().startswith("entail"			):
                   return ind
         return -1



    def  _lowercase						(self   :   Any       ,      __a   :   Any       ,      __a   :   int=True       ,      __a   :   Dict=True       ,      __a   :   Any=TruncationStrategy.ONLY_FIRST       ,      **__a   :   Tuple			):
         UpperCAmelCase_	   =       self.framework
         if self.tokenizer.pad_token is None:
              # Override for tokenizers not supporting padding
              logger.error(
                  "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
                  " `pad_token=eos_token`"			)
              UpperCAmelCase_	   =       self.tokenizer.eos_token
         try:
              UpperCAmelCase_	   =       self.tokenizer(
                  __a       ,      add_special_tokens=__a       ,      return_tensors=__a       ,      padding=__a       ,      truncation=__a       ,      )
         except Exception as e:
              if "too short" in str(__a			):
                   # tokenizers might yell that we want to truncate
                   # to a value that is not even reached by the input.
                   # In that case we don't want to truncate.
                   # It seems there's not a really better way to catch that
                   # exception.

                   UpperCAmelCase_	   =       self.tokenizer(
                       __a       ,      add_special_tokens=__a       ,      return_tensors=__a       ,      padding=__a       ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE       ,      )
              else:
                   raise e

         return inputs



    def  _lowercase						(self   :   List[str]       ,      **__a   :   Tuple			):
         if kwargs.get("multi_class"       ,      __a			) is not None:
              UpperCAmelCase_	   =       kwargs["multi_class"]
              logger.warning(
                  "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
                  "`multi_class` will be removed in a future version of Transformers."			)
         UpperCAmelCase_	   =       {}
         if "candidate_labels" in kwargs:
              UpperCAmelCase_	   =       self._args_parser._parse_labels(kwargs["candidate_labels"]			)
         if "hypothesis_template" in kwargs:
              UpperCAmelCase_	   =       kwargs["hypothesis_template"]

         UpperCAmelCase_	   =       {}
         if "multi_label" in kwargs:
              UpperCAmelCase_	   =       kwargs["multi_label"]
         return preprocess_params, {}, postprocess_params



    def __call__(self   :   Tuple       ,      __a   :   Union[str, List[str]]       ,      *__a   :   Optional[Any]       ,      **__a   :   Tuple       ,      ):
         if len(__a			) == 0:
              pass
         elif len(__a			) == 1 and "candidate_labels" not in kwargs:
              UpperCAmelCase_	   =       args[0]
         else:
              raise ValueError(f"""Unable to understand extra arguments {args}"""			)

         return super().__call__(__a       ,      **__a			)



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   Optional[Any]       ,      __a   :   List[str]=None       ,      __a   :   Any="This example is {}."			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self._args_parser(__a       ,      __a       ,      __a			)

         for i, (candidate_label, sequence_pair) in enumerate(zip(__a       ,      __a			)			):
              UpperCAmelCase_	   =       self._parse_and_tokenize([sequence_pair]			)

              yield {
                  "candidate_label": candidate_label,
                  "sequence": sequences[0],
                  "is_last": i == len(__a			) - 1,
                  **model_input,
              }



    def  _lowercase						(self   :   List[str]       ,      __a   :   Any			):
         UpperCAmelCase_	   =       inputs["candidate_label"]
         UpperCAmelCase_	   =       inputs["sequence"]
         UpperCAmelCase_	   =       {k: inputs[k] for k in self.tokenizer.model_input_names}
         UpperCAmelCase_	   =       self.model(**__a			)

         UpperCAmelCase_	   =       {
             "candidate_label": candidate_label,
             "sequence": sequence,
             "is_last": inputs["is_last"],
             **outputs,
         }
         return model_outputs






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   List[str]       ,      __a   :   Tuple=False			):
         UpperCAmelCase_	   =       [outputs["candidate_label"] for outputs in model_outputs]
         UpperCAmelCase_	   =       [outputs["sequence"] for outputs in model_outputs]
         UpperCAmelCase_	   =       np.concatenate([output["logits"].numpy() for output in model_outputs]			)
         UpperCAmelCase_	   =       logits.shape[0]
         UpperCAmelCase_	   =       len(__a			)
         UpperCAmelCase_	   =       N // n
         UpperCAmelCase_	   =       logits.reshape((num_sequences, n, -1)			)

         if multi_label or len(__a			) == 1:
              # softmax over the entailment vs. contradiction dim for each label independently
              UpperCAmelCase_	   =       self.entailment_id
              UpperCAmelCase_	   =       -1 if entailment_id == 0 else 0
              UpperCAmelCase_	   =       reshaped_outputs[..., [contradiction_id, entailment_id]]
              UpperCAmelCase_	   =       np.exp(__a			) / np.exp(__a			).sum(-1       ,      keepdims=__a			)
              UpperCAmelCase_	   =       scores[..., 1]
         else:
              # softmax the "entailment" logits over all candidate labels
              UpperCAmelCase_	   =       reshaped_outputs[..., self.entailment_id]
              UpperCAmelCase_	   =       np.exp(__a			) / np.exp(__a			).sum(-1       ,      keepdims=__a			)

         UpperCAmelCase_	   =       list(reversed(scores[0].argsort()			)			)
         return {
             "sequence": sequences[0],
             "labels": [candidate_labels[i] for i in top_inds],
             "scores": scores[0, top_inds].tolist(),
         }




