





'''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


_lowerCamelCase					:						List[str]					     =							getLogger(__name__)


_lowerCamelCase					:						List[str]					     =							'cuda' if torch.cuda.is_available() else 'cpu'




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase = 8							,      UpperCAmelCase = DEFAULT_DEVICE							,      UpperCAmelCase=False							,      UpperCAmelCase="summarization"							,      UpperCAmelCase=None							,      **UpperCAmelCase							,      )   ->Optional[int]:



	"""simple docstring"""







	A					=   Path(_lowerCamelCase     ).open("""w"""							,      encoding="""utf-8"""     )
	A					=   str(_lowerCamelCase     )
	A					=   AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase     ).to(_lowerCamelCase     )
	if fpaa:
		A					=   model.half()

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




def  __a			(  )   ->List[str]:



	"""simple docstring"""







	return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""     )




def  __a			(  UpperCAmelCase=True     )   ->int:



	"""simple docstring"""







	A					=   argparse.ArgumentParser()
	parser.add_argument("""model_name"""							,      type=_lowerCamelCase							,      help="""like facebook/bart-large-cnn,t5-base, etc."""     )
	parser.add_argument("""input_path"""							,      type=_lowerCamelCase							,      help="""like cnn_dm/test.source"""     )
	parser.add_argument("""save_path"""							,      type=_lowerCamelCase							,      help="""where to save summaries"""     )
	parser.add_argument("""--reference_path"""							,      type=_lowerCamelCase							,      required=_lowerCamelCase							,      help="""like cnn_dm/test.target"""     )
	parser.add_argument("""--score_path"""							,      type=_lowerCamelCase							,      required=_lowerCamelCase							,      default="""metrics.json"""							,      help="""where to save metrics"""     )
	parser.add_argument("""--device"""							,      type=_lowerCamelCase							,      required=_lowerCamelCase							,      default=_lowerCamelCase							,      help="""cuda, cuda:1, cpu etc."""     )
	parser.add_argument(
	    """--prefix"""							,      type=_lowerCamelCase							,      required=_lowerCamelCase							,      default=_lowerCamelCase							,      help="""will be added to the begininng of src examples"""     )
	parser.add_argument("""--task"""							,      type=_lowerCamelCase							,      default="""summarization"""							,      help="""used for task_specific_params + metrics"""     )
	parser.add_argument("""--bs"""							,      type=_lowerCamelCase							,      default=8							,      required=_lowerCamelCase							,      help="""batch size"""     )
	parser.add_argument(
	    """--n_obs"""							,      type=_lowerCamelCase							,      default=-1							,      required=_lowerCamelCase							,      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=_lowerCamelCase							,      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(_lowerCamelCase     )
	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=_lowerCamelCase     )
	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(
	    _lowerCamelCase							,      args.save_path							,      args.model_name							,      batch_size=args.bs							,      device=args.device							,      fpaa=args.fpaa							,      task=args.task							,      prefix=args.prefix							,      **_lowerCamelCase							,      )

	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(_lowerCamelCase     )]
	A					=   score_fn(_lowerCamelCase							,      _lowerCamelCase     )
	scores.update(_lowerCamelCase     )

	if args.dump_args:
		scores.update(_lowerCamelCase     )
	if args.info:
		A					=   args.info

	if verbose:
		print(_lowerCamelCase     )

	if args.score_path is not None:
		json.dump(_lowerCamelCase							,      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)


class     snake_case_			:






   def __init__( self							:	Optional[int]      ,		lowercase_							:	Optional[int]=None    )   ->		Optional[int]:
       lowercase__					:  List[Any]          =							data
       lowercase__					:  Any          =							None







   def __repr__( self							:	Optional[Any]    )   ->		int:
       lowercase__					:  Optional[int]          =							""
       lowercase__					:  Any          =							self
       while temp:
           string_rep += F'''<{temp.data}> ---> '''
           lowercase__					:  List[Any]          =							temp.next
       string_rep += "<END>"
       return string_rep



def      lowercase_							(     _lowerCamelCase				:			List[Any]):

    # if elements_list is empty
    if not elements_list:
        raise Exception("The Elements List is empty")

    # Set first element as Head
    lowercase__					:  List[str]          =							Node(elements_list[0])
    lowercase__					:  Tuple          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        lowercase__					:  Tuple          =							Node(_lowerCamelCase)
        lowercase__					:  Union[str, Any]          =							current.next
    return head


UpperCamelCase    				=		[1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print('''Creating Linked List from List.''')
UpperCamelCase    				=		make_linked_list(list_data)
print('''Linked List:''')

print(linked_list)



