



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__lowerCamelCase      =    getLogger(__name__)


__lowerCamelCase      =    """cuda""" if torch.cuda.is_available() else """cpu"""




def 			UpperCAmelCase__       (							__snake_case  , __snake_case  , __snake_case  , __snake_case = 8  , __snake_case = DEFAULT_DEVICE  , __snake_case=False  , __snake_case="summarization"  , __snake_case=None  , **__snake_case  , )      ->       Optional[int]:
  _A							=			Path(__UpperCAmelCase							).open('''w'''  , encoding='''utf-8'''							)
  _A							=			str(__UpperCAmelCase							)
  _A							=			AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)
  if fpaa:
    _A							=			model.half()

  _A							=			AutoTokenizer.from_pretrained(__UpperCAmelCase							)
  logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''							)  # if this is wrong, check config.model_type.

  _A							=			time.time()
  # update config with task specific params
  use_task_specific_params(__UpperCAmelCase  , __UpperCAmelCase							)
  if prefix is None:
    _A							=			prefix or getattr(model.config  , '''prefix'''  , ''''''							) or """"""
  for examples_chunk in tqdm(list(chunks(__UpperCAmelCase  , __UpperCAmelCase							)							)							):
    _A							=			[prefix + text for text in examples_chunk]
    _A							=			tokenizer(__UpperCAmelCase  , return_tensors='''pt'''  , truncation=__UpperCAmelCase  , padding='''longest'''							).to(__UpperCAmelCase							)
    _A							=			model.generate(
        input_ids=batch.input_ids  , attention_mask=batch.attention_mask  , **__UpperCAmelCase  , )
    _A							=			tokenizer.batch_decode(__UpperCAmelCase  , skip_special_tokens=__UpperCAmelCase  , clean_up_tokenization_spaces=__UpperCAmelCase							)
    for hypothesis in dec:
      fout.write(hypothesis + '''\n'''							)
      fout.flush()
  fout.close()
  _A							=			int(time.time() - start_time							)  # seconds
  _A							=			len(__UpperCAmelCase							)
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs  , 4							)}




def 			UpperCAmelCase__       (							)      ->       Optional[int]:
  return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''							)




def 			UpperCAmelCase__       (							__snake_case=True							)      ->       Any:
  _A							=			argparse.ArgumentParser()
  parser.add_argument('''model_name'''  , type=__UpperCAmelCase  , help='''like facebook/bart-large-cnn,t5-base, etc.'''							)
  parser.add_argument('''input_path'''  , type=__UpperCAmelCase  , help='''like cnn_dm/test.source'''							)
  parser.add_argument('''save_path'''  , type=__UpperCAmelCase  , help='''where to save summaries'''							)
  parser.add_argument('''--reference_path'''  , type=__UpperCAmelCase  , required=__UpperCAmelCase  , help='''like cnn_dm/test.target'''							)
  parser.add_argument('''--score_path'''  , type=__UpperCAmelCase  , required=__UpperCAmelCase  , default='''metrics.json'''  , help='''where to save metrics'''							)
  parser.add_argument('''--device'''  , type=__UpperCAmelCase  , required=__UpperCAmelCase  , default=__UpperCAmelCase  , help='''cuda, cuda:1, cpu etc.'''							)
  parser.add_argument(
      '''--prefix'''  , type=__UpperCAmelCase  , required=__UpperCAmelCase  , default=__UpperCAmelCase  , help='''will be added to the begininng of src examples'''							)
  parser.add_argument('''--task'''  , type=__UpperCAmelCase  , default='''summarization'''  , help='''used for task_specific_params + metrics'''							)
  parser.add_argument('''--bs'''  , type=__UpperCAmelCase  , default=8  , required=__UpperCAmelCase  , help='''batch size'''							)
  parser.add_argument(
      '''--n_obs'''  , type=__UpperCAmelCase  , default=-1  , required=__UpperCAmelCase  , help='''How many observations. Defaults to all.'''							)
  parser.add_argument('''--fp16'''  , action='''store_true'''							)
  parser.add_argument('''--dump-args'''  , action='''store_true'''  , help='''print the custom hparams with the results'''							)
  parser.add_argument(
      '''--info'''  , nargs='''?'''  , type=__UpperCAmelCase  , const=datetime_now()  , help=(
          '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
          ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
      )  , )
  # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  _A							=			parser.parse_known_args()
  _A							=			parse_numeric_n_bool_cl_kwargs(__UpperCAmelCase							)
  if parsed_args and verbose:
    print(F'''parsed the following generate kwargs: {parsed_args}'''							)
  with open(args.input_path							) as f:
    _A							=			[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    _A							=			examples[: args.n_obs]
  Path(args.save_path							).parent.mkdir(exist_ok=__UpperCAmelCase							)
  if args.reference_path is None and Path(args.score_path							).exists():
    warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''							)
  _A							=			generate_summaries_or_translations(
      __UpperCAmelCase  , args.save_path  , args.model_name  , batch_size=args.bs  , device=args.device  , fpaa=args.fpaa  , task=args.task  , prefix=args.prefix  , **__UpperCAmelCase  , )

  if args.reference_path is None:
    return {}

  # Compute scores
  _A							=			calculate_bleu if """translation""" in args.task else calculate_rouge
  _A							=			[x.rstrip() for x in open(args.save_path							).readlines()]
  _A							=			[x.rstrip() for x in open(args.reference_path							).readlines()][: len(__UpperCAmelCase							)]
  _A							=			score_fn(__UpperCAmelCase  , __UpperCAmelCase							)
  scores.update(__UpperCAmelCase							)

  if args.dump_args:
    scores.update(__UpperCAmelCase							)
  if args.info:
    _A							=			args.info

  if verbose:
    print(__UpperCAmelCase							)

  if args.score_path is not None:
    json.dump(__UpperCAmelCase  , open(args.score_path  , '''w'''							)							)

  return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)




"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)

UpperCAmelCase:   Optional[Any]       			=							{
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Dict		    =   "opt"
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   ["past_key_values"]






	def __init__(			self							,UpperCAmelCase_=5_02_72							,UpperCAmelCase_=7_68							,UpperCAmelCase_=12							,UpperCAmelCase_=30_72							,UpperCAmelCase_=20_48							,UpperCAmelCase_=True							,UpperCAmelCase_=False							,UpperCAmelCase_=None							,UpperCAmelCase_=0.1							,UpperCAmelCase_=0.0							,UpperCAmelCase_=12							,UpperCAmelCase_="relu"							,UpperCAmelCase_=0.0							,UpperCAmelCase_=0.02							,UpperCAmelCase_=True							,UpperCAmelCase_=1							,UpperCAmelCase_=2							,UpperCAmelCase_=2							,UpperCAmelCase_=True							,UpperCAmelCase_=True							,**UpperCAmelCase_							,):
			super().__init__(
			    pad_token_id=UpperCAmelCase_							,bos_token_id=UpperCAmelCase_							,eos_token_id=UpperCAmelCase_							,**UpperCAmelCase_							,)
			_lowercase							:      Optional[Any]			=					vocab_size
			_lowercase							:      Tuple			=					max_position_embeddings
			_lowercase							:      Union[str, Any]			=					num_attention_heads
			_lowercase							:      List[str]			=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
			_lowercase							:      List[str]			=					ffn_dim
			_lowercase							:      List[str]			=					hidden_size
			_lowercase							:      List[Any]			=					num_hidden_layers
			_lowercase							:      List[Any]			=					dropout
			_lowercase							:      str			=					attention_dropout
			_lowercase							:      List[Any]			=					activation_function
			_lowercase							:      int			=					init_std
			_lowercase							:      List[Any]			=					layerdrop
			_lowercase							:      Tuple			=					use_cache
			_lowercase							:      Any			=					do_layer_norm_before
			# We keep these variables at `True` for backward compatibility.
			_lowercase							:      int			=					enable_bias
			_lowercase							:      str			=					layer_norm_elementwise_affine

			# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
			# with checkpoints that have been fine-tuned before transformers v4.20.1
			# see https://github.com/facebookresearch/metaseq/pull/164
			_lowercase							:      Optional[Any]			=					_remove_final_layer_norm



