code: 
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[str]       						=						('''_front''', '''_back''', '''_len''')
  
  						@dataclass
  						class      UpperCAmelCase_						:
  
  												'''simple docstring'''
  
  
  
  
  												UpperCamelCase__    :				Any       						=						None
  												UpperCamelCase__    :				Deque._Node | None       						=						None
  												UpperCamelCase__    :				Deque._Node | None       						=						None
  
  
  
  
  
  
  
  						class      UpperCAmelCase_						:
  
  												'''simple docstring'''
  
  
  
  
  												UpperCamelCase__    :				Optional[int]       						=						('''_cur''',)
  
  
  
  
  
  
  												def __init__(					self   ,							_A	):
  
  
  
  
  
  
  
  																			'''simple docstring'''
  
  																			__SCREAMING_SNAKE_CASE								=      cur
  
  
  
  
  
  
  												def __iter__(					self	):
  
  
  
  
  
  
  
  																			'''simple docstring'''
  
  																			return self
  
  
  
  
  
  
  												def  _A			(					self	):
  
  
  
  
  
  
  
  																			'''simple docstring'''
  
  																			if self._cur is None:
  																										# finished iterating
  																										raise StopIteration
  																			__SCREAMING_SNAKE_CASE								=      self._cur.val
  																			__SCREAMING_SNAKE_CASE								=      self._cur.next_node
  
  																			return val
  
  
  
  
  
  
  
  						def __init__(					self   ,							_A = None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      0
  
  													if iterable is not None:
  																				# append every value to the deque
  																				for val in iterable:
  																											self.append(_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self._Node(_A   ,							_A   ,							_A	)
  													if self.is_empty():
  																				# front = back
  																				__SCREAMING_SNAKE_CASE								=      __SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      1
  													else:
  																				# connect nodes
  																				__SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      self._back
  																				__SCREAMING_SNAKE_CASE								=      node  # assign new back to the new node
  
  																				self._len += 1
  
  																				# make sure there were no errors
  																				assert not self.is_empty(), "Error on appending value."
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self._Node(_A   ,							_A   ,							_A	)
  													if self.is_empty():
  																				# front = back
  																				__SCREAMING_SNAKE_CASE								=      __SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      1
  													else:
  																				# connect nodes
  																				__SCREAMING_SNAKE_CASE								=      self._front
  																				__SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      node  # assign new front to the new node
  
  																				self._len += 1
  
  																				# make sure there were no errors
  																				assert not self.is_empty(), "Error on appending value."
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for val in iterable:
  																				self.append(_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for val in iterable:
  																				self.appendleft(_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													assert not self.is_empty(), "Deque is empty."
  
  													__SCREAMING_SNAKE_CASE								=      self._back
  													__SCREAMING_SNAKE_CASE								=      self._back.prev_node  # set new back
  													# drop the last node - python will deallocate memory automatically
  													__SCREAMING_SNAKE_CASE								=      None
  
  													self._len -= 1
  
  													return topop.val
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													assert not self.is_empty(), "Deque is empty."
  
  													__SCREAMING_SNAKE_CASE								=      self._front
  													__SCREAMING_SNAKE_CASE								=      self._front.next_node  # set new front and drop the first node
  													__SCREAMING_SNAKE_CASE								=      None
  
  													self._len -= 1
  
  													return topop.val
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return self._front is None
  
  
  
  
  
  
  						def __len__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return self._len
  
  
  
  
  
  
  						def __eq__(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if not isinstance(_A   ,							_A	):
  																				return NotImplemented
  
  													__SCREAMING_SNAKE_CASE								=      self._front
  													__SCREAMING_SNAKE_CASE								=      other._front
  
  													# if the length of the dequeues are not the same, they are not equal
  													if len(self	) != len(_A	):
  																				return False
  
  													while me is not None and oth is not None:
  																				# compare every value
  																				if me.val != oth.val:
  																											return False
  																				__SCREAMING_SNAKE_CASE								=      me.next_node
  																				__SCREAMING_SNAKE_CASE								=      oth.next_node
  
  													return True
  
  
  
  
  
  
  						def __iter__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return Deque._Iterator(self._front	)
  
  
  
  
  
  
  						def __repr__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      []
  													__SCREAMING_SNAKE_CASE								=      self._front
  													while aux is not None:
  																				# append the values in a list to display
  																				values_list.append(aux.val	)
  																				__SCREAMING_SNAKE_CASE								=      aux.next_node
  
  													return f"""[{", ".join(repr(_A	) for val in values_list	)}]"""
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  
  
  
style_context: 
  
  
  
  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__	:  Union[str, Any] 		=getLogger(__name__)
  
  
  lowerCAmelCase__	:  Optional[int] 		='''cuda''' if torch.cuda.is_available() else '''cpu'''
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__							,     a__ = 8							,     a__ = DEFAULT_DEVICE							,     a__=False							,     a__="summarization"							,     a__=None							,     **a__							,     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      Path(a__     ).open('w'							,     encoding='utf-8'     )
  							__SCREAMING_SNAKE_CASE								=      str(a__     )
  							__SCREAMING_SNAKE_CASE								=      AutoModelForSeqaSeqLM.from_pretrained(a__     ).to(a__     )
  							if fpaa:
  														__SCREAMING_SNAKE_CASE								=      model.half()
  
  							__SCREAMING_SNAKE_CASE								=      AutoTokenizer.from_pretrained(a__     )
  							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.
  
  							__SCREAMING_SNAKE_CASE								=      time.time()
  							# update config with task specific params
  							use_task_specific_params(a__							,     a__     )
  							if prefix is None:
  														__SCREAMING_SNAKE_CASE								=      prefix or getattr(model.config							,     'prefix'							,     ''     ) or ''
  							for examples_chunk in tqdm(list(chunks(a__							,     a__     )     )     ):
  														__SCREAMING_SNAKE_CASE								=      [prefix + text for text in examples_chunk]
  														__SCREAMING_SNAKE_CASE								=      tokenizer(a__							,     return_tensors='pt'							,     truncation=a__							,     padding='longest'     ).to(a__     )
  														__SCREAMING_SNAKE_CASE								=      model.generate(
  														    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **a__							,     )
  														__SCREAMING_SNAKE_CASE								=      tokenizer.batch_decode(a__							,     skip_special_tokens=a__							,     clean_up_tokenization_spaces=a__     )
  														for hypothesis in dec:
  																					fout.write(hypothesis + '\n'     )
  																					fout.flush()
  							fout.close()
  							__SCREAMING_SNAKE_CASE								=      int(time.time() - start_time     )  # seconds
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4     )}
  
  
  
  
  
  
  def 			__lowercase     (				)       ->				Dict:
  							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )
  
  
  
  
  
  
  def 			__lowercase     (				a__=True     )       ->				Optional[Any]:
  							__SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
  							parser.add_argument('model_name'							,     type=a__							,     help='like facebook/bart-large-cnn,t5-base, etc.'     )
  							parser.add_argument('input_path'							,     type=a__							,     help='like cnn_dm/test.source'     )
  							parser.add_argument('save_path'							,     type=a__							,     help='where to save summaries'     )
  							parser.add_argument('--reference_path'							,     type=a__							,     required=a__							,     help='like cnn_dm/test.target'     )
  							parser.add_argument('--score_path'							,     type=a__							,     required=a__							,     default='metrics.json'							,     help='where to save metrics'     )
  							parser.add_argument('--device'							,     type=a__							,     required=a__							,     default=a__							,     help='cuda, cuda:1, cpu etc.'     )
  							parser.add_argument(
  							    '--prefix'							,     type=a__							,     required=a__							,     default=a__							,     help='will be added to the begininng of src examples'     )
  							parser.add_argument('--task'							,     type=a__							,     default='summarization'							,     help='used for task_specific_params + metrics'     )
  							parser.add_argument('--bs'							,     type=a__							,     default=8							,     required=a__							,     help='batch size'     )
  							parser.add_argument(
  							    '--n_obs'							,     type=a__							,     default=-1							,     required=a__							,     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=a__							,     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
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      parser.parse_known_args()
  							__SCREAMING_SNAKE_CASE								=      parse_numeric_n_bool_cl_kwargs(a__     )
  							if parsed_args and verbose:
  														print(f"""parsed the following generate kwargs: {parsed_args}"""     )
  							with open(args.input_path     ) as f:
  														__SCREAMING_SNAKE_CASE								=      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  							if args.n_obs > 0:
  														__SCREAMING_SNAKE_CASE								=      examples[: args.n_obs]
  							Path(args.save_path     ).parent.mkdir(exist_ok=a__     )
  							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."""     )
  							__SCREAMING_SNAKE_CASE								=      generate_summaries_or_translations(
  							    a__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **a__							,     )
  
  							if args.reference_path is None:
  														return {}
  
  							# Compute scores
  							__SCREAMING_SNAKE_CASE								=      calculate_bleu if 'translation' in args.task else calculate_rouge
  							__SCREAMING_SNAKE_CASE								=      [x.rstrip() for x in open(args.save_path     ).readlines()]
  							__SCREAMING_SNAKE_CASE								=      [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(a__     )]
  							__SCREAMING_SNAKE_CASE								=      score_fn(a__							,     a__     )
  							scores.update(a__     )
  
  							if args.dump_args:
  														scores.update(a__     )
  							if args.info:
  														__SCREAMING_SNAKE_CASE								=      args.info
  
  							if verbose:
  														print(a__     )
  
  							if args.score_path is not None:
  														json.dump(a__							,     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)
  
  
  
  
  
  
  