





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_    :				Union[str, Any]					=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						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   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Any ,     snake_case_		:						str ,     snake_case_		:						List[Any]						):
     return max(metric_fn(snake_case_ ,     snake_case_						) for gt in ground_truths						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int ,     snake_case_		:						Optional[Any] ,     snake_case_		:						List[str]						):
     __magic_name__				=       [line.strip() for line in open(snake_case_ ,     '''r'''						).readlines()]
     __magic_name__				=       []

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

     __magic_name__				=       __magic_name__				=       __magic_name__				=       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_						)

     __magic_name__				=       100.0 * em / total
     __magic_name__				=       100.0 * fa / total

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




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Any ,     snake_case_		:						Union[str, Any] ,     snake_case_		:						Optional[Any]						):
     __magic_name__				=       args.k
     __magic_name__				=       [line.strip() for line in open(snake_case_ ,     '''r'''						).readlines()]
     __magic_name__				=       [line.strip() for line in open(snake_case_ ,     '''r'''						).readlines()]

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

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




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[int] ,     snake_case_		:						List[str] ,     snake_case_		:						Union[str, Any]						):
     def strip_title(snake_case_		:						Tuple						):
          if title.startswith('''"'''						):
               __magic_name__				=       title[1:]
          if title.endswith('''"'''						):
               __magic_name__				=       title[:-1]
          return title

     __magic_name__				=       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						)

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

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




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						List[str] ,     snake_case_		:						int ,     snake_case_		:						Tuple						):
     with torch.no_grad():
          __magic_name__				=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              snake_case_ ,     return_tensors='''pt''' ,     padding=snake_case_ ,     truncation=snake_case_						)

          __magic_name__				=       inputs_dict.input_ids.to(args.device						)
          __magic_name__				=       inputs_dict.attention_mask.to(args.device						)
          __magic_name__				=       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]] ,     )
          __magic_name__				=       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   _SCREAMING_SNAKE_CASE      (	):
     __magic_name__				=       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.''' ,     )
     __magic_name__				=       parser.parse_args()
     __magic_name__				=       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''						)
     return args




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						List[str]						):
     __magic_name__				=       {}
     if args.model_type is None:
          __magic_name__				=       infer_model_type(args.model_name_or_path						)
          assert args.model_type is not None
     if args.model_type.startswith('''rag'''						):
          __magic_name__				=       RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
          __magic_name__				=       args.n_docs
          if args.index_name is not None:
               __magic_name__				=       args.index_name
          if args.index_path is not None:
               __magic_name__				=       args.index_path
     else:
          __magic_name__				=       BartForConditionalGeneration

     __magic_name__				=       (
         [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_						)

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






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


a_    :				Union[str, Any]					=			logging.get_logger(__name__)

a_    :				Optional[Any]					=			{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							"""vit-hybrid"""





  def __init__(  self	,  A=None	,  A=7_68	,  A=12	,  A=12	,  A=30_72	,  A="gelu"	,  A=0.0	,  A=0.0	,  A=0.02	,  A=1E-12	,  A=2_24	,  A=1	,  A=3	,  A=[1, 10_24, 24, 24]	,  A=True	,  **A	,  )		->   Union[str, Any]:





       '''simple docstring'''







       super().__init__(**A						)

       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''						)
            __magic_name__				=       {
                '''global_padding''': '''same''',
                '''layer_type''': '''bottleneck''',
                '''depths''': [3, 4, 9],
                '''out_features''': ['''stage3'''],
                '''embedding_dynamic_padding''': True,
            }

       if isinstance(A	,  A						):
            if "model_type" in backbone_config:
                 __magic_name__				=       CONFIG_MAPPING[backbone_config['''model_type''']]
            else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
                 __magic_name__				=       BitConfig
            __magic_name__				=       backbone_config_class(**A						)

       __magic_name__				=       backbone_featmap_shape
       __magic_name__				=       backbone_config
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       num_channels
       __magic_name__				=       qkv_bias







  def 			__A      (  self						)		->   Dict[str, any]:





       '''simple docstring'''







       __magic_name__				=       copy.deepcopy(self.__dict__						)
       __magic_name__				=       self.backbone_config.to_dict()
       __magic_name__				=       self.__class__.model_type
       return output