"""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			   = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[int]:



   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 )  ->  str:
   return max(metric_fn(A__       ,    A__ ) for gt in ground_truths )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Tuple:
   lowercase__:   Dict					=      [line.strip() for line in open(A__       ,    '''r''' ).readlines()]
   lowercase__:   int					=      []

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

   lowercase__:   Optional[Any]					=      0
   for prediction, ground_truths in zip(A__       ,    A__ ):
      total += 1
      em += metric_max_over_ground_truths(A__       ,    A__       ,    A__ )
      fa += metric_max_over_ground_truths(A__       ,    A__       ,    A__ )

   lowercase__:   Any					=      1_0_0.0 * em / total
   lowercase__:   Any					=      1_0_0.0 * fa / total

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   lowercase__:   List[str]					=      args.k
   lowercase__:   Any					=      [line.strip() for line in open(A__       ,    '''r''' ).readlines()]
   lowercase__:   Dict					=      [line.strip() for line in open(A__       ,    '''r''' ).readlines()]

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

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:



   def strip_title(__UpperCAmelCase ):
      if title.startswith('''\"''' ):
         lowercase__:   List[Any]					=      title[1:]
      if title.endswith('''\"''' ):
         lowercase__:   Optional[int]					=      title[:-1]
      return title

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

   lowercase__:   List[Any]					=      rag_model.rag.question_encoder(A__ )
   lowercase__:   Dict					=      question_enc_outputs[0]

   lowercase__:   str					=      rag_model.retriever(
       A__       ,    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__:   int					=      []
   for docs in all_docs:
      lowercase__:   Optional[int]					=      [strip_title(A__ ) for title in docs['''title''']]
      provenance_strings.append('''\t'''.join(A__ ) )
   return provenance_strings



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Dict:



   with torch.no_grad():
      lowercase__:   Union[str, Any]					=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          A__       ,    return_tensors='''pt'''       ,    padding=A__       ,    truncation=A__ )

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

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

      return answers



def SCREAMING_SNAKE_CASE__			(   )  ->  Union[str, Any]:
   lowercase__:   Any					=      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type'''       ,    choices=['''rag_sequence''', '''rag_token''', '''bart''']       ,    type=A__       ,    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=A__       ,    choices=['''exact''', '''compressed''', '''legacy''']       ,    type=A__       ,    help='''RAG model retriever type'''       ,    )
   parser.add_argument(
       '''--index_path'''       ,    default=A__       ,    type=A__       ,    help='''Path to the retrieval index'''       ,    )
   parser.add_argument('''--n_docs'''       ,    default=5       ,    type=A__       ,    help='''Number of retrieved docs''' )
   parser.add_argument(
       '''--model_name_or_path'''       ,    default=A__       ,    type=A__       ,    required=A__       ,    help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''       ,    )
   parser.add_argument(
       '''--eval_mode'''       ,    choices=['''e2e''', '''retrieval''']       ,    default='''e2e'''       ,    type=A__       ,    help=(
           '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
           ''' precision@k.'''
       )       ,    )
   parser.add_argument('''--k'''       ,    default=1       ,    type=A__       ,    help='''k for the precision@k calculation''' )
   parser.add_argument(
       '''--evaluation_set'''       ,    default=A__       ,    type=A__       ,    required=A__       ,    help='''Path to a file containing evaluation samples'''       ,    )
   parser.add_argument(
       '''--gold_data_path'''       ,    default=A__       ,    type=A__       ,    required=A__       ,    help='''Path to a tab-separated file with gold samples'''       ,    )
   parser.add_argument(
       '''--gold_data_mode'''       ,    default='''qa'''       ,    type=A__       ,    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=A__       ,    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=A__       ,    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=A__       ,    help='''Number of beams to be used when generating answers'''       ,    )
   parser.add_argument('''--min_length'''       ,    default=1       ,    type=A__       ,    help='''Min length of the generated answers''' )
   parser.add_argument('''--max_length'''       ,    default=5_0       ,    type=A__       ,    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__:   Tuple					=      parser.parse_args()
   lowercase__:   str					=      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
   return args



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Tuple					=      {}
   if args.model_type is None:
      lowercase__:   Dict					=      infer_model_type(args.model_name_or_path )
      assert args.model_type is not None
   if args.model_type.startswith('''rag''' ):
      lowercase__:   Any					=      RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
      lowercase__:   Dict					=      args.n_docs
      if args.index_name is not None:
         lowercase__:   Optional[int]					=      args.index_name
      if args.index_path is not None:
         lowercase__:   List[Any]					=      args.index_path
   else:
      lowercase__:   Optional[int]					=      BartForConditionalGeneration

   lowercase__:   Dict					=      (
       [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'''       ,    A__ )

   lowercase__:   Optional[int]					=      get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
   lowercase__:   Any					=      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(A__       ,    args.predictions_path       ,    args.gold_data_path )
         continue

      logger.info('''***** Running evaluation for {} *****'''.format(A__ ) )
      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__:   List[Any]					=      RagRetriever.from_pretrained(A__       ,    **A__ )
         lowercase__:   Any					=      model_class.from_pretrained(A__       ,    retriever=A__       ,    **A__ )
         model.retriever.init_retrieval()
      else:
         lowercase__:   List[Any]					=      model_class.from_pretrained(A__       ,    **A__ )
      model.to(args.device )

      with open(args.evaluation_set       ,    '''r''' ) as eval_file, open(args.predictions_path       ,    '''w''' ) as preds_file:
         lowercase__:   Union[str, Any]					=      []
         for line in tqdm(A__ ):
            questions.append(line.strip() )
            if len(A__ ) == args.eval_batch_size:
               lowercase__:   Any					=      evaluate_batch_fn(A__       ,    A__       ,    A__ )
               preds_file.write('''\n'''.join(A__ ) + '''\n''' )
               preds_file.flush()
               lowercase__:   Tuple					=      []
         if len(A__ ) > 0:
            lowercase__:   Optional[Any]					=      evaluate_batch_fn(A__       ,    A__       ,    A__ )
            preds_file.write('''\n'''.join(A__ ) )
            preds_file.flush()

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


if __name__ == "__main__":
       __A			   = get_args()
       main(args)






import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
       import torch.distributed.checkpoint as dist_cp
       from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
       from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
       from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCAmelCase_									=	get_logger(__name__)




def 					lowerCamelCase__       (						A__       :					Union[str, Any]  ,			A__       :					str  ,			A__       :					Any  ,			A__       :					Dict  ,			A__       :					Any=0	):





       '''simple docstring'''



       os.makedirs(A__  ,			exist_ok=A__	)
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              __lowerCamelCase   =						model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     if accelerator.process_index == 0:
                            logger.info(f'Saving model to {output_model_file}'	)
                            torch.save(A__  ,			A__	)
                            logger.info(f'Model saved to {output_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     __lowerCamelCase   =						(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Saving model to {output_model_file}'	)
                     torch.save(A__  ,			A__	)
                     logger.info(f'Model saved to {output_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving model to {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""model""": state_dict}

                     dist_cp.save_state_dict(
                         state_dict=A__  ,			storage_writer=dist_cp.FileSystemWriter(A__	)  ,			planner=DefaultSavePlanner()  ,			)
                     logger.info(f'Model saved to {ckpt_dir}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Dict  ,			A__       :					int  ,			A__       :					List[str]  ,			A__       :					Any=0	):





       '''simple docstring'''



       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if type(A__	) != FSDP and accelerator.process_index != 0:
                            if not fsdp_plugin.sync_module_states:
                                   raise ValueError(
                                       """Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
                                       """initializing FSDP object"""	)
                            return
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     __lowerCamelCase   =						(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						(
                         os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                         if f'{MODEL_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading model from {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""model""": model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=A__  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			planner=DefaultLoadPlanner()  ,			)
                     __lowerCamelCase   =						state_dict["""model"""]
                     logger.info(f'Model loaded from {ckpt_dir}'	)
              model.load_state_dict(A__	)




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[str]  ,			A__       :					str  ,			A__       :					Dict  ,			A__       :					Optional[Any]  ,			A__       :					Optional[int]=0	):





       '''simple docstring'''



       os.makedirs(A__  ,			exist_ok=A__	)
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              __lowerCamelCase   =						FSDP.optim_state_dict(A__  ,			A__	)
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            __lowerCamelCase   =						(
                                f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                            logger.info(f'Saving Optimizer state to {output_optimizer_file}'	)
                            torch.save(A__  ,			A__	)
                            logger.info(f'Optimizer state saved in {output_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving Optimizer state to {ckpt_dir}'	)
                     dist_cp.save_state_dict(
                         state_dict={"""optimizer""": optim_state}  ,			storage_writer=dist_cp.FileSystemWriter(A__	)  ,			planner=DefaultSavePlanner()  ,			)
                     logger.info(f'Optimizer state saved in {ckpt_dir}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					List[str]  ,			A__       :					int  ,			A__       :					Any  ,			A__       :					Union[str, Any]  ,			A__       :					List[Any]=0	):





       '''simple docstring'''



       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     __lowerCamelCase   =						None
                     # below check should work but currently it isn't working (mostly opytorch issue),
                     # in the meantime disabling it at the cost of excess memory usage
                     # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                     __lowerCamelCase   =						(
                         f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading Optimizer state from {input_optimizer_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Optimizer state loaded from {input_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						(
                         os.path.join(A__  ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
                         if f'{OPTIMIZER_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading Optimizer from {ckpt_dir}'	)
                     __lowerCamelCase   =						load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()  ,			optimizer_key="""optimizer"""  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			)
                     __lowerCamelCase   =						optim_state["""optimizer"""]
                     logger.info(f'Optimizer loaded from {ckpt_dir}'	)
              __lowerCamelCase   =						FSDP.optim_state_dict_to_load(A__  ,			A__  ,			A__	)
              optimizer.load_state_dict(A__	)

