




'''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


a_   :	List[str]							   = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def   a_							(   __snake_case							:    Dict     )							->		Optional[int]:



       """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_							(   __snake_case							:    int     , __snake_case							:    Any     , __snake_case							:    Any     )							->		Optional[int]:



       """simple docstring"""
       return max(metric_fn(lowerCAmelCase__     , lowerCAmelCase__     ) for gt in ground_truths     )





def   a_							(   __snake_case							:    Any     , __snake_case							:    List[Any]     , __snake_case							:    int     )							->		Any:



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

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

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

       lowerCamelCase_							=1_0_0.0 * em / total
       lowerCamelCase_							=1_0_0.0 * fa / total

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





def   a_							(   __snake_case							:    int     , __snake_case							:    Any     , __snake_case							:    str     )							->		Any:



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

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

       lowerCamelCase_							=1_0_0.0 * em / total
       logger.info(F'''Precision@{k}: {em: .2f}'''     )





def   a_							(   __snake_case							:    int     , __snake_case							:    Optional[int]     , __snake_case							:    Optional[Any]     )							->		List[Any]:



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

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

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

       lowerCamelCase_							=rag_model.retriever(
           lowerCAmelCase__     , 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(lowerCAmelCase__     ) for title in docs['title']]
              provenance_strings.append('''\t'''.join(lowerCAmelCase__     )     )
       return provenance_strings





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    Tuple     , __snake_case							:    Dict     )							->		Any:



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

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

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

              return answers





def   a_							(   )							->		List[Any]:



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



       """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'''     , lowerCAmelCase__     )

       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(lowerCAmelCase__     , args.predictions_path     , args.gold_data_path     )
                     continue

              logger.info('''***** Running evaluation for {} *****'''.format(lowerCAmelCase__     )     )
              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(lowerCAmelCase__     , **lowerCAmelCase__     )
                     lowerCamelCase_							=model_class.from_pretrained(lowerCAmelCase__     , retriever=lowerCAmelCase__     , **lowerCAmelCase__     )
                     model.retriever.init_retrieval()
              else:
                     lowerCamelCase_							=model_class.from_pretrained(lowerCAmelCase__     , **lowerCAmelCase__     )
              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(lowerCAmelCase__     ):
                            questions.append(line.strip()     )
                            if len(lowerCAmelCase__     ) == args.eval_batch_size:
                                   lowerCamelCase_							=evaluate_batch_fn(lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     )
                                   preds_file.write('''\n'''.join(lowerCAmelCase__     ) + '''\n'''     )
                                   preds_file.flush()
                                   lowerCamelCase_							=[]
                     if len(lowerCAmelCase__     ) > 0:
                            lowerCamelCase_							=evaluate_batch_fn(lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     )
                            preds_file.write('''\n'''.join(lowerCAmelCase__     )     )
                            preds_file.flush()

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


if __name__ == "__main__":
     a_   :	Any							   = get_args()
     main(args)







'''simple docstring'''




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


a_   :	Tuple							   = logging.get_logger(__name__)

a_   :	str							   = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Tuple							   = {
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

a_   :	str							   = {
    """openai/clip-vit-base-patch32""": 77,
}


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=VOCAB_FILES_NAMES
       lowercase			:					Optional[int]   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					Any   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=CLIPTokenizer






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="<|endoftext|>",	lowerCAmelCase="<|startoftext|>",	lowerCAmelCase="<|endoftext|>",	lowerCAmelCase="<|endoftext|>",	**lowerCAmelCase,	):



              """simple docstring"""







              super().__init__(
                  lowerCAmelCase,	lowerCAmelCase,	tokenizer_file=lowerCAmelCase,	unk_token=lowerCAmelCase,	bos_token=lowerCAmelCase,	eos_token=lowerCAmelCase,	pad_token=lowerCAmelCase,	**lowerCAmelCase,	)

              if not isinstance(self.backend_tokenizer.pre_tokenizer,	pre_tokenizers.Sequence						):
                     raise ValueError(
                         '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                         ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                         ''' to be compatible with this version.The easiest way to do so is'''
                         ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                         ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                         ''' transformers.'''						)

              self._wrap_decode_method_backend_tokenizer()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.backend_tokenizer.decode

              def new_decode_method(*lowerCAmelCase,	**lowerCAmelCase						):
                     lowerCamelCase_							=orig_decode_method(*lowerCAmelCase,	**lowerCAmelCase						)
                     lowerCamelCase_							=text.replace(self.backend_tokenizer.model.end_of_word_suffix,	''' '''						).strip()
                     return text

              lowerCamelCase_							=new_decode_method






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[self.bos_token_id]
              lowerCamelCase_							=[self.eos_token_id]

              if token_ids_a is None:
                     return bos_token + token_ids_a + eos_token
              return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[self.bos_token_id]
              lowerCamelCase_							=[self.eos_token_id]

              if token_ids_a is None:
                     return len(bos_token + token_ids_a + eos_token						) * [0]
              return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token						) * [0]






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=self._tokenizer.model.save(lowerCAmelCase,	name=lowerCAmelCase						)
              return tuple(lowerCAmelCase						)




