





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


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

transformers_logging.set_verbosity_info()

def   a_   (			lowerCAmelCase_				:      List[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   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Union[str, Any]     ):
   return max(metric_fn(lowercase__,     lowercase__     ) for gt in ground_truths     )

def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Union[str, Any]     ):
   __lowerCAmelCase							     =     [line.strip() for line in open(lowercase__,     'r'     ).readlines()]
   __lowerCAmelCase							     =     []

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

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

   __lowerCAmelCase							     =     100.0 * em / total
   __lowerCAmelCase							     =     100.0 * fa / total

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

def   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     args.k
   __lowerCAmelCase							     =     [line.strip() for line in open(lowercase__,     'r'     ).readlines()]
   __lowerCAmelCase							     =     [line.strip() for line in open(lowercase__,     'r'     ).readlines()]

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

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

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Tuple     ):
   def strip_title(lowerCAmelCase_				:      int     ):
      if title.startswith('"'     ):
         __lowerCAmelCase							     =     title[1:]
      if title.endswith('"'     ):
         __lowerCAmelCase							     =     title[:-1]
      return title

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

   __lowerCAmelCase							     =     rag_model.rag.question_encoder(lowercase__     )
   __lowerCAmelCase							     =     question_enc_outputs[0]

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

def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Dict     ):
   with torch.no_grad():
      __lowerCAmelCase							     =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          lowercase__,     return_tensors='pt',     padding=lowercase__,     truncation=lowercase__     )

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

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

      return answers

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

   __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(lowercase__,     args.predictions_path,     args.gold_data_path     )
         continue

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

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


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








from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int = 1_6 ,  lowerCAmelCase_			:		int = 8_8 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 3_2 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		str = "geglu" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
     super().__init__()
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     attention_head_dim
     __lowerCAmelCase							     =     num_attention_heads * attention_head_dim

     __lowerCAmelCase							     =     in_channels

     __lowerCAmelCase							     =     torch.nn.GroupNorm(num_groups=lowerCAmelCase_ ,  num_channels=lowerCAmelCase_ ,  eps=1e-6 ,  affine=lowerCAmelCase_					)
     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 3. Define transformers blocks
     __lowerCAmelCase							     =     nn.ModuleList(
         [
             BasicTransformerBlock(
                 lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  dropout=lowerCAmelCase_ ,  cross_attention_dim=lowerCAmelCase_ ,  activation_fn=lowerCAmelCase_ ,  attention_bias=lowerCAmelCase_ ,  double_self_attention=lowerCAmelCase_ ,  norm_elementwise_affine=lowerCAmelCase_ ,  )
             for d in range(lowerCAmelCase_					)
         ]					)

     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		bool = True ,  )							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     hidden_states.shape
     __lowerCAmelCase							     =     batch_frames // num_frames

     __lowerCAmelCase							     =     hidden_states

     __lowerCAmelCase							     =     hidden_states[None, :].reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  2 ,  1 ,  3 ,  4					)

     __lowerCAmelCase							     =     self.norm(lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  3 ,  4 ,  2 ,  1					).reshape(batch_size * height * width ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.proj_in(lowerCAmelCase_					)

     # 2. Blocks
     for block in self.transformer_blocks:
        __lowerCAmelCase							     =     block(
            lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  cross_attention_kwargs=lowerCAmelCase_ ,  class_labels=lowerCAmelCase_ ,  )

     # 3. Output
     __lowerCAmelCase							     =     self.proj_out(lowerCAmelCase_					)
     __lowerCAmelCase							     =     (
         hidden_states[None, None, :]
         .reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
         .permute(0 ,  3 ,  4 ,  1 ,  2					)
         .contiguous()
     )
     __lowerCAmelCase							     =     hidden_states.reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=lowerCAmelCase_					)

