code: 
  '''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
  
  
  _UpperCAmelCase   :      Tuple 						=  getLogger(__name__)
  
  
  _UpperCAmelCase   :      Tuple 						=  """cuda""" if torch.cuda.is_available() else """cpu"""
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase = 8			,UpperCamelCase = DEFAULT_DEVICE			,UpperCamelCase=False			,UpperCamelCase="summarization"			,UpperCamelCase=None			,**UpperCamelCase			,) ->      Dict:
  		_UpperCamelCase       : Any    =      Path(UpperCamelCase	).open('''w'''			,encoding='''utf-8'''	)
  		_UpperCamelCase       : Union[str, Any]    =      str(UpperCamelCase	)
  		_UpperCamelCase       : Optional[Any]    =      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase	).to(UpperCamelCase	)
  		if fpaa:
  				_UpperCamelCase       : Any    =      model.half()
  
  		_UpperCamelCase       : Any    =      AutoTokenizer.from_pretrained(UpperCamelCase	)
  		logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.
  
  		_UpperCamelCase       : Any    =      time.time()
  		# update config with task specific params
  		use_task_specific_params(UpperCamelCase			,UpperCamelCase	)
  		if prefix is None:
  				_UpperCamelCase       : Optional[int]    =      prefix or getattr(model.config			,'''prefix'''			,''''''	) or ''''''
  		for examples_chunk in tqdm(list(chunks(UpperCamelCase			,UpperCamelCase	)	)	):
  				_UpperCamelCase       : Optional[int]    =      [prefix + text for text in examples_chunk]
  				_UpperCamelCase       : Union[str, Any]    =      tokenizer(UpperCamelCase			,return_tensors='''pt'''			,truncation=UpperCamelCase			,padding='''longest'''	).to(UpperCamelCase	)
  				_UpperCamelCase       : Dict    =      model.generate(
  				    input_ids=batch.input_ids			,attention_mask=batch.attention_mask			,**UpperCamelCase			,)
  				_UpperCamelCase       : Optional[int]    =      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()
  		_UpperCamelCase       : List[Any]    =      int(time.time() - start_time	)  # seconds
  		_UpperCamelCase       : Optional[int]    =      len(UpperCamelCase	)
  		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,4	)}
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Union[str, Any]:
  		return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase=True	) ->      Tuple:
  		_UpperCamelCase       : Optional[int]    =      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
  		_UpperCamelCase,							_UpperCamelCase       : Tuple    =      parser.parse_known_args()
  		_UpperCamelCase       : Tuple    =      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:
  				_UpperCamelCase       : Union[str, Any]    =      [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
  		if args.n_obs > 0:
  				_UpperCamelCase       : Union[str, Any]    =      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.'''	)
  		_UpperCamelCase       : Optional[int]    =      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
  		_UpperCamelCase       : Union[str, Any]    =      calculate_bleu if '''translation''' in args.task else calculate_rouge
  		_UpperCamelCase       : List[Any]    =      [x.rstrip() for x in open(args.save_path	).readlines()]
  		_UpperCamelCase       : Union[str, Any]    =      [x.rstrip() for x in open(args.reference_path	).readlines()][: len(UpperCamelCase	)]
  		_UpperCamelCase       : dict    =      score_fn(UpperCamelCase			,UpperCamelCase	)
  		scores.update(UpperCamelCase	)
  
  		if args.dump_args:
  				scores.update(UpperCamelCase	)
  		if args.info:
  				_UpperCamelCase       : Union[str, Any]    =      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)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import unittest
  
  from transformers.image_processing_utils import get_size_dict
  
  class 			UpperCAmelCase				(					unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def _lowercase  (    self     )				->    Dict:
  				# Test a dict with the wrong keys raises an error
  				_UpperCamelCase       : List[Any]    =      {'''wrong_key''': 224}
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(_snake_case     )
  
  				_UpperCamelCase       : Dict    =      {'''height''': 224}
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(_snake_case     )
  
  				_UpperCamelCase       : str    =      {'''width''': 224, '''shortest_edge''': 224}
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(_snake_case     )
  
  				# Test a dict with the correct keys is returned as is
  				_UpperCamelCase       : Any    =      {'''height''': 224, '''width''': 224}
  				_UpperCamelCase       : Optional[int]    =      get_size_dict(_snake_case     )
  				self.assertEqual(_snake_case  ,					_snake_case     )
  
  				_UpperCamelCase       : Optional[Any]    =      {'''shortest_edge''': 224}
  				_UpperCamelCase       : int    =      get_size_dict(_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224}     )
  
  				_UpperCamelCase       : int    =      {'''longest_edge''': 224, '''shortest_edge''': 224}
  				_UpperCamelCase       : Tuple    =      get_size_dict(_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''longest_edge''': 224, '''shortest_edge''': 224}     )
  
  				# Test a single int value which  represents (size, size)
  				_UpperCamelCase       : Any    =      get_size_dict(224     )
  				self.assertEqual(_snake_case  ,					{'''height''': 224, '''width''': 224}     )
  
  				# Test a single int value which represents the shortest edge
  				_UpperCamelCase       : List[Any]    =      get_size_dict(224  ,					default_to_square=_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224}     )
  
  				# Test a tuple of ints which represents (height, width)
  				_UpperCamelCase       : Any    =      get_size_dict((150, 200)     )
  				self.assertEqual(_snake_case  ,					{'''height''': 150, '''width''': 200}     )
  
  				# Test a tuple of ints which represents (width, height)
  				_UpperCamelCase       : List[str]    =      get_size_dict((150, 200)  ,					height_width_order=_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''height''': 200, '''width''': 150}     )
  
  				# Test an int representing the shortest edge and max_size which represents the longest edge
  				_UpperCamelCase       : Any    =      get_size_dict(224  ,					max_size=256  ,					default_to_square=_snake_case     )
  				self.assertEqual(_snake_case  ,					{'''shortest_edge''': 224, '''longest_edge''': 256}     )
  
  				# Test int with default_to_square=True and max_size fails
  				with self.assertRaises(_snake_case     ):
  						get_size_dict(224  ,					max_size=256  ,					default_to_square=_snake_case     )
  
  
  
  
  
  