





'''simple docstring'''




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


lowercase__       : List[str]         =       getLogger(__name__)


lowercase__       : List[Any]         =       '''cuda''' if torch.cuda.is_available() else '''cpu'''






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					str					,				__snake_case    :					str					,				__snake_case    :					int = 8					,				__snake_case    :					str = DEFAULT_DEVICE					,				__snake_case    :					Union[str, Any]=False					,				__snake_case    :					Optional[int]="summarization"					,				__snake_case    :					Dict=None					,				**__snake_case    :					List[str]					,				)	->	Dict:
     __A		:					Any						=			Path(__snake_case     ).open('w'					,				encoding='utf-8'     )
     __A		:					List[str]						=			str(__snake_case     )
     __A		:					Union[str, Any]						=			AutoModelForSeqaSeqLM.from_pretrained(__snake_case     ).to(__snake_case     )
     if fpaa:
          __A		:					Tuple						=			model.half()

     __A		:					Dict						=			AutoTokenizer.from_pretrained(__snake_case     )
     logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'     )  # if this is wrong, check config.model_type.

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






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






def 			_lowerCAmelCase     (    __snake_case    :					Tuple=True     )	->	int:
     __A		:					Any						=			argparse.ArgumentParser()
     parser.add_argument('model_name'					,				type=__snake_case					,				help='like facebook/bart-large-cnn,t5-base, etc.'     )
     parser.add_argument('input_path'					,				type=__snake_case					,				help='like cnn_dm/test.source'     )
     parser.add_argument('save_path'					,				type=__snake_case					,				help='where to save summaries'     )
     parser.add_argument('--reference_path'					,				type=__snake_case					,				required=__snake_case					,				help='like cnn_dm/test.target'     )
     parser.add_argument('--score_path'					,				type=__snake_case					,				required=__snake_case					,				default='metrics.json'					,				help='where to save metrics'     )
     parser.add_argument('--device'					,				type=__snake_case					,				required=__snake_case					,				default=__snake_case					,				help='cuda, cuda:1, cpu etc.'     )
     parser.add_argument(
         '--prefix'					,				type=__snake_case					,				required=__snake_case					,				default=__snake_case					,				help='will be added to the begininng of src examples'     )
     parser.add_argument('--task'					,				type=__snake_case					,				default='summarization'					,				help='used for task_specific_params + metrics'     )
     parser.add_argument('--bs'					,				type=__snake_case					,				default=8					,				required=__snake_case					,				help='batch size'     )
     parser.add_argument(
         '--n_obs'					,				type=__snake_case					,				default=-1					,				required=__snake_case					,				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=__snake_case					,				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					,__A		:					Optional[Any]						=			parser.parse_known_args()
     __A		:					List[str]						=			parse_numeric_n_bool_cl_kwargs(__snake_case     )
     if parsed_args and verbose:
          print(f'parsed the following generate kwargs: {parsed_args}'     )
     __A		:					List[Any]						=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
     if args.n_obs > 0:
          __A		:					Optional[Any]						=			examples[: args.n_obs]
     Path(args.save_path     ).parent.mkdir(exist_ok=__snake_case     )

     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.'     )

     if args.device == "cpu" and args.fpaa:
          # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
          raise ValueError('Can\'t mix --fp16 and --device cpu'     )

     __A		:					Union[str, Any]						=			generate_summaries_or_translations(
         __snake_case					,				args.save_path					,				args.model_name					,				batch_size=args.bs					,				device=args.device					,				fpaa=args.fpaa					,				task=args.task					,				prefix=args.prefix					,				**__snake_case					,				)

     if args.reference_path is None:
          return {}

     # Compute scores
     __A		:					Dict						=			calculate_bleu if 'translation' in args.task else calculate_rouge
     __A		:					Dict						=			[x.rstrip() for x in open(args.save_path     ).readlines()]
     __A		:					List[str]						=			[x.rstrip() for x in open(args.reference_path     ).readlines()][: len(__snake_case     )]
     __A		:					dict						=			score_fn(__snake_case					,				__snake_case     )
     scores.update(__snake_case     )

     if args.dump_args:
          scores.update(__snake_case     )
     if args.info:
          __A		:					Union[str, Any]						=			args.info

     if verbose:
          print(__snake_case     )

     if args.score_path is not None:
          json.dump(__snake_case					,				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'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)