code: 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__    :       int						=			getLogger(__name__)
  
  
  lowercase__    :       str						=			"cuda" if torch.cuda.is_available() else "cpu"
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__ = 8     ,						snake_case__ = DEFAULT_DEVICE     ,						snake_case__=False     ,						snake_case__="summarization"     ,						snake_case__=None     ,						**snake_case__     ,						)  ->			Dict:
  							lowerCAmelCase				=	Path(snake_case_							).open('''w'''     ,						encoding='''utf-8'''							)
  							lowerCAmelCase				=	str(snake_case_							)
  							lowerCAmelCase				=	AutoModelForSeqaSeqLM.from_pretrained(snake_case_							).to(snake_case_							)
  							if fpaa:
  														lowerCAmelCase				=	model.half()
  
  							lowerCAmelCase				=	AutoTokenizer.from_pretrained(snake_case_							)
  							logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"							)  # if this is wrong, check config.model_type.
  
  							lowerCAmelCase				=	time.time()
  							# update config with task specific params
  							use_task_specific_params(snake_case_     ,						snake_case_							)
  							if prefix is None:
  														lowerCAmelCase				=	prefix or getattr(model.config     ,						'''prefix'''     ,						''''''							) or ''''''
  							for examples_chunk in tqdm(list(chunks(snake_case_     ,						snake_case_							)							)							):
  														lowerCAmelCase				=	[prefix + text for text in examples_chunk]
  														lowerCAmelCase				=	tokenizer(snake_case_     ,						return_tensors='''pt'''     ,						truncation=snake_case_     ,						padding='''longest'''							).to(snake_case_							)
  														lowerCAmelCase				=	model.generate(
  														    input_ids=batch.input_ids     ,						attention_mask=batch.attention_mask     ,						**snake_case_     ,						)
  														lowerCAmelCase				=	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()
  							lowerCAmelCase				=	int(time.time() - start_time							)  # seconds
  							lowerCAmelCase				=	len(snake_case_							)
  							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,						4							)}
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			List[Any]:
  							return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''							)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__=True							)  ->			int:
  							lowerCAmelCase				=	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
  							lowerCAmelCase				=	parser.parse_known_args()
  							lowerCAmelCase				=	parse_numeric_n_bool_cl_kwargs(snake_case_							)
  							if parsed_args and verbose:
  														print(f"parsed the following generate kwargs: {parsed_args}"							)
  							with open(args.input_path							) as f:
  														lowerCAmelCase				=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
  							if args.n_obs > 0:
  														lowerCAmelCase				=	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."							)
  							lowerCAmelCase				=	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
  							lowerCAmelCase				=	calculate_bleu if '''translation''' in args.task else calculate_rouge
  							lowerCAmelCase				=	[x.rstrip() for x in open(args.save_path							).readlines()]
  							lowerCAmelCase				=	[x.rstrip() for x in open(args.reference_path							).readlines()][: len(snake_case_							)]
  							lowerCAmelCase				=	score_fn(snake_case_     ,						snake_case_							)
  							scores.update(snake_case_							)
  
  							if args.dump_args:
  														scores.update(snake_case_							)
  							if args.info:
  														lowerCAmelCase				=	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)
  
  
  
  
style_context: from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)
  
  lowercase__    :       Union[str, Any]						=			{
      '''microsoft/prophetnet-large-uncased''': (
          '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
      ),
  }
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
  	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
  	UpperCAmelCase_						:      Any     =       {
  	    """num_attention_heads""": """num_encoder_attention_heads""",
  	}
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	encoder_ffn_dim
  								lowerCAmelCase				=	num_encoder_layers
  								lowerCAmelCase				=	num_encoder_attention_heads
  								lowerCAmelCase				=	decoder_ffn_dim
  								lowerCAmelCase				=	num_decoder_layers
  								lowerCAmelCase				=	num_decoder_attention_heads
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
  								lowerCAmelCase				=	activation_function
  
  								# parameters for prophetnet
  								lowerCAmelCase				=	ngram
  								lowerCAmelCase				=	num_buckets
  								lowerCAmelCase				=	relative_max_distance
  								lowerCAmelCase				=	disable_ngram_loss
  								lowerCAmelCase				=	eps
  
  								# 3 Types of Dropout
  								lowerCAmelCase				=	attention_dropout
  								lowerCAmelCase				=	activation_dropout
  								lowerCAmelCase				=	dropout
  
  								lowerCAmelCase				=	use_cache
  
  								super().__init__(
  								    pad_token_id=__SCREAMING_SNAKE_CASE	,    bos_token_id=__SCREAMING_SNAKE_CASE	,    eos_token_id=__SCREAMING_SNAKE_CASE	,    is_encoder_decoder=__SCREAMING_SNAKE_CASE	,    add_cross_attention=__SCREAMING_SNAKE_CASE	,    decoder_start_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  	@num_hidden_layers.setter
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								raise NotImplementedError(
  								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
  								    ''' `num_decoder_layers`.'''       )
  
  
  
  