


'''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


__lowercase	:						Dict       =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  [line.strip() for line in open(_SCREAMING_SNAKE_CASE						,      'r'   ).readlines()]
					__a			:       List[Any]					    =  []

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

					__a			:       List[Any]					    =  0
					for prediction, ground_truths in zip(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										total += 1
										em += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

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

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






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

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

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






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

					__a			:       str					    =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    _SCREAMING_SNAKE_CASE						,      return_tensors='pt'						,      padding=_SCREAMING_SNAKE_CASE						,      truncation=_SCREAMING_SNAKE_CASE						,      )['input_ids'].to(args.device   )

					__a			:       Tuple					    =  rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  question_enc_outputs[0]

					__a			:       int					    =  rag_model.retriever(
					    _SCREAMING_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'						,      )
					__a			:       Tuple					    =  rag_model.retriever.index.get_doc_dicts(result.doc_ids   )
					__a			:       Tuple					    =  []
					for docs in all_docs:
										__a			:       Dict					    =  [strip_title(_SCREAMING_SNAKE_CASE   ) for title in docs['title']]
										provenance_strings.append('\t'.join(_SCREAMING_SNAKE_CASE   )   )
					return provenance_strings






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					with torch.no_grad():
										__a			:       Dict					    =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    _SCREAMING_SNAKE_CASE						,      return_tensors='pt'						,      padding=_SCREAMING_SNAKE_CASE						,      truncation=_SCREAMING_SNAKE_CASE   )

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

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

										return answers






def   lowerCamelCase							():
					__a			:       Union[str, Any]					    =  argparse.ArgumentParser()
					parser.add_argument(
					    '--model_type'						,      choices=['rag_sequence', 'rag_token', 'bart']						,      type=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,      choices=['exact', 'compressed', 'legacy']						,      type=_SCREAMING_SNAKE_CASE						,      help='RAG model retriever type'						,      )
					parser.add_argument(
					    '--index_path'						,      default=_SCREAMING_SNAKE_CASE						,      type=_SCREAMING_SNAKE_CASE						,      help='Path to the retrieval index'						,      )
					parser.add_argument('--n_docs'						,      default=5						,      type=_SCREAMING_SNAKE_CASE						,      help='Number of retrieved docs'   )
					parser.add_argument(
					    '--model_name_or_path'						,      default=_SCREAMING_SNAKE_CASE						,      type=_SCREAMING_SNAKE_CASE						,      required=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,      help='k for the precision@k calculation'   )
					parser.add_argument(
					    '--evaluation_set'						,      default=_SCREAMING_SNAKE_CASE						,      type=_SCREAMING_SNAKE_CASE						,      required=_SCREAMING_SNAKE_CASE						,      help='Path to a file containing evaluation samples'						,      )
					parser.add_argument(
					    '--gold_data_path'						,      default=_SCREAMING_SNAKE_CASE						,      type=_SCREAMING_SNAKE_CASE						,      required=_SCREAMING_SNAKE_CASE						,      help='Path to a tab-separated file with gold samples'						,      )
					parser.add_argument(
					    '--gold_data_mode'						,      default='qa'						,      type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,      help='Number of beams to be used when generating answers'						,      )
					parser.add_argument('--min_length'						,      default=1						,      type=_SCREAMING_SNAKE_CASE						,      help='Min length of the generated answers'   )
					parser.add_argument('--max_length'						,      default=50						,      type=_SCREAMING_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.'						,      )
					__a			:       Dict					    =  parser.parse_args()
					__a			:       Dict					    =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )
					return args






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Tuple					    =  {}
					if args.model_type is None:
										__a			:       str					    =  infer_model_type(args.model_name_or_path   )
										assert args.model_type is not None
					if args.model_type.startswith('rag'   ):
										__a			:       int					    =  RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
										__a			:       int					    =  args.n_docs
										if args.index_name is not None:
															__a			:       Optional[Any]					    =  args.index_name
										if args.index_path is not None:
															__a			:       List[str]					    =  args.index_path
					else:
										__a			:       Tuple					    =  BartForConditionalGeneration

					__a			:       int					    =  (
					    [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'						,      _SCREAMING_SNAKE_CASE   )

					__a			:       str					    =  get_scores if args.eval_mode == 'e2e' else get_precision_at_k
					__a			:       Optional[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(_SCREAMING_SNAKE_CASE						,      args.predictions_path						,      args.gold_data_path   )
															continue

										logger.info('***** Running evaluation for {} *****'.format(_SCREAMING_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'   ):
															__a			:       Tuple					    =  RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )
															__a			:       Union[str, Any]					    =  model_class.from_pretrained(_SCREAMING_SNAKE_CASE						,      retriever=_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )
															model.retriever.init_retrieval()
										else:
															__a			:       Union[str, Any]					    =  model_class.from_pretrained(_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )
										model.to(args.device   )

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

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


if __name__ == "__main__":
	__lowercase	:						Optional[Any]       =	get_args()
	main(args)







'''simple docstring'''



#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'   ) as fh:
										fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_EX   )
										try:
															print(*_SCREAMING_SNAKE_CASE   )
										finally:
															fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_UN   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	f'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, Any]       =	dist.get_world_size()

	printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(f'''{gpu} is broken''')
	raise



