






'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     [line.strip() for line in open(__UpperCamelCase						,		"r"							).readlines()]
   lowercase_	:    Dict           =     []

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

   lowercase_	:    Any           =     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							)

   lowercase_	:    List[Any]           =     100.0 * em / total
   lowercase_	:    List[Any]           =     100.0 * fa / total

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     args.k
   lowercase_	:    int           =     [line.strip() for line in open(__UpperCamelCase						,		"r"							).readlines()]
   lowercase_	:    Dict           =     [line.strip() for line in open(__UpperCamelCase						,		"r"							).readlines()]

   lowercase_	:    Optional[Any]           =     0
   for hypo, reference in zip(__UpperCamelCase						,		__UpperCamelCase							):
      lowercase_	:    List[str]           =     set(hypo.split("\t"							)[:k]							)
      lowercase_	:    int           =     set(reference.split("\t"							)							)
      total += 1
      em += len(hypo_provenance & ref_provenance							) / k

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def strip_title(_UpperCamelCase							):
      if title.startswith("\""							):
         lowercase_	:    Optional[int]           =     title[1:]
      if title.endswith("\""							):
         lowercase_	:    Tuple           =     title[:-1]
      return title

   lowercase_	:    Dict           =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
       __UpperCamelCase						,		return_tensors="pt"						,		padding=__UpperCamelCase						,		truncation=__UpperCamelCase						,		)["input_ids"].to(args.device							)

   lowercase_	:    Optional[Any]           =     rag_model.rag.question_encoder(__UpperCamelCase							)
   lowercase_	:    int           =     question_enc_outputs[0]

   lowercase_	:    Union[str, Any]           =     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"						,		)
   lowercase_	:    str           =     rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
   lowercase_	:    str           =     []
   for docs in all_docs:
      lowercase_	:    List[str]           =     [strip_title(__UpperCamelCase							) for title in docs["title"]]
      provenance_strings.append("\t".join(__UpperCamelCase							)							)
   return provenance_strings




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   with torch.no_grad():
      lowercase_	:    List[Any]           =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          __UpperCamelCase						,		return_tensors="pt"						,		padding=__UpperCamelCase						,		truncation=__UpperCamelCase							)

      lowercase_	:    Optional[int]           =     inputs_dict.input_ids.to(args.device							)
      lowercase_	:    List[Any]           =     inputs_dict.attention_mask.to(args.device							)
      lowercase_	:    Union[str, Any]           =     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]]						,		)
      lowercase_	:    Any           =     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 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Optional[Any]           =     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."						,		)
   lowercase_	:    str           =     parser.parse_args()
   lowercase_	:    Optional[int]           =     torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
   return args




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

   lowercase_	:    List[str]           =     (
       [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							)

   lowercase_	:    Union[str, Any]           =     get_scores if args.eval_mode == "e2e" else get_precision_at_k
   lowercase_	:    int           =     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"							):
         lowercase_	:    Union[str, Any]           =     RagRetriever.from_pretrained(__UpperCamelCase						,		**__UpperCamelCase							)
         lowercase_	:    Dict           =     model_class.from_pretrained(__UpperCamelCase						,		retriever=__UpperCamelCase						,		**__UpperCamelCase							)
         model.retriever.init_retrieval()
      else:
         lowercase_	:    Tuple           =     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:
         lowercase_	:    Optional[Any]           =     []
         for line in tqdm(__UpperCamelCase							):
            questions.append(line.strip()							)
            if len(__UpperCamelCase							) == args.eval_batch_size:
               lowercase_	:    Union[str, Any]           =     evaluate_batch_fn(__UpperCamelCase						,		__UpperCamelCase						,		__UpperCamelCase							)
               preds_file.write("\n".join(__UpperCamelCase							) + "\n"							)
               preds_file.flush()
               lowercase_	:    Any           =     []
         if len(__UpperCamelCase							) > 0:
            lowercase_	:    List[Any]           =     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 typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   str		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   Dict		       =    'Pix2StructImageProcessor'
    __lowerCamelCase:   Union[str, Any]		       =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(			self  :       str  ,     a  :       Dict  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     False
       super().__init__(a  ,     a			)




    def __call__(			self  :       Tuple  ,     a  :       int=None  ,     a  :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     a  :       bool = True  ,     a  :       Union[bool, str, PaddingStrategy] = False  ,     a  :       Union[bool, str, TruncationStrategy] = None  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = 2_0_4_8  ,     a  :       int = 0  ,     a  :       Optional[int] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = True  ,     a  :       Optional[Union[str, TensorType]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       if images is None and text is None:
          raise ValueError("You have to specify either images or text."			)

       # Get only text
       if images is None and not self.image_processor.is_vqa:
          lowercase_	:    Dict           =     self.tokenizer
          lowercase_	:    Tuple           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )
          return text_encoding

       if not self.image_processor.is_vqa:
          # add pixel_values
          lowercase_	:    Optional[int]           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     **a			)
       else:
          # add pixel_values and bbox
          lowercase_	:    Any           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     header_text=a  ,     **a			)

       if text is not None and not self.image_processor.is_vqa:
          lowercase_	:    int           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )

          if "attention_mask" in text_encoding:
             lowercase_	:    str           =     text_encoding.pop("attention_mask"			)
          if "input_ids" in text_encoding:
             lowercase_	:    Dict           =     text_encoding.pop("input_ids"			)
       else:
          lowercase_	:    str           =     None

       if text_encoding is not None:
          encoding_image_processor.update(a			)

       return encoding_image_processor




    def 				lowerCAmelCase__				(			self  :       Any  ,     *a  :       str  ,     **a  :       Tuple			):






       '''simple docstring'''






       return self.tokenizer.batch_decode(*a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     *a  :       Optional[int]  ,     **a  :       Any			):






       '''simple docstring'''






       return self.tokenizer.decode(*a  ,     **a			)





    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.tokenizer.model_input_names
       lowercase_	:    Dict           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




