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       : 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'''
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      Any 						=  {
      """google/bigbird-pegasus-large-arxiv""": (
          """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
      ),
      """google/bigbird-pegasus-large-pubmed""": (
          """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
      ),
      """google/bigbird-pegasus-large-bigpatent""": (
          """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
      ),
      # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
  }
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   List[str]          =    'bigbird_pegasus'
  		A__  :   Union[str, Any]          =    ['past_key_values']
  		A__  :   Optional[int]          =    {
  		    'num_attention_heads': 'encoder_attention_heads',
  		    'hidden_size': 'd_model',
  		    'attention_probs_dropout_prob': 'attention_dropout',
  		}
  		def __init__(    self  ,					_snake_case=96103  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu_new"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=2  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=1  ,					_snake_case="block_sparse"  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=False  ,					**_snake_case  ,					)				->    Optional[Any]:
  				_UpperCamelCase       : str    =      vocab_size
  				_UpperCamelCase       : Tuple    =      max_position_embeddings
  				_UpperCamelCase       : int    =      d_model
  				_UpperCamelCase       : List[str]    =      encoder_ffn_dim
  				_UpperCamelCase       : str    =      encoder_layers
  				_UpperCamelCase       : Dict    =      encoder_attention_heads
  				_UpperCamelCase       : str    =      decoder_ffn_dim
  				_UpperCamelCase       : List[str]    =      decoder_layers
  				_UpperCamelCase       : Any    =      decoder_attention_heads
  				_UpperCamelCase       : str    =      dropout
  				_UpperCamelCase       : int    =      attention_dropout
  				_UpperCamelCase       : Optional[int]    =      activation_dropout
  				_UpperCamelCase       : Optional[Any]    =      activation_function
  				_UpperCamelCase       : Tuple    =      init_std
  				_UpperCamelCase       : Any    =      encoder_layerdrop
  				_UpperCamelCase       : List[str]    =      decoder_layerdrop
  				_UpperCamelCase       : Any    =      classifier_dropout
  				_UpperCamelCase       : int    =      use_cache
  				_UpperCamelCase       : Union[str, Any]    =      encoder_layers
  				_UpperCamelCase       : str    =      scale_embedding  # scale factor will be sqrt(d_model) if True
  
  				# extra config
  				_UpperCamelCase       : str    =      attention_type
  				_UpperCamelCase       : Tuple    =      block_size
  				_UpperCamelCase       : Optional[Any]    =      num_random_blocks
  				_UpperCamelCase       : Dict    =      use_bias
  
  				super().__init__(
  				    pad_token_id=_snake_case  ,					bos_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_encoder_decoder=_snake_case  ,					decoder_start_token_id=_snake_case  ,					**_snake_case  ,					)
  
  
  
  
  
  
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : str    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						    ]     )
  
  						if self.use_past:
  								_UpperCamelCase       : List[Any]    =      {0: '''batch'''}
  								_UpperCamelCase       : Tuple    =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
  						else:
  								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}
  								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}
  
  						if self.use_past:
  								self.fill_with_past_key_values_(_snake_case  ,					direction='''inputs'''     )
  				elif self.task == "causal-lm":
  						# TODO: figure this case out.
  						_UpperCamelCase       : Optional[int]    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						    ]     )
  						if self.use_past:
  								_UpperCamelCase,							_UpperCamelCase       : List[str]    =      self.num_layers
  								for i in range(_snake_case     ):
  										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  										_UpperCamelCase       : Dict    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  				else:
  						_UpperCamelCase       : Optional[int]    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
  						        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
  						    ]     )
  
  				return common_inputs
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : int    =      super().outputs
  				else:
  						_UpperCamelCase       : Optional[int]    =      super(_snake_case  ,					self     ).outputs
  						if self.use_past:
  								_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
  								for i in range(_snake_case     ):
  										_UpperCamelCase       : int    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  				return common_outputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				_UpperCamelCase       : List[str]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  				# Generate decoder inputs
  				_UpperCamelCase       : Optional[int]    =      seq_length if not self.use_past else 1
  				_UpperCamelCase       : Optional[int]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  				_UpperCamelCase       : Optional[int]    =      {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
  				_UpperCamelCase       : Any    =      dict(**_snake_case  ,					**_snake_case     )
  
  				if self.use_past:
  						if not is_torch_available():
  								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
  						else:
  								import torch
  						_UpperCamelCase,							_UpperCamelCase       : Dict    =      common_inputs['''input_ids'''].shape
  						_UpperCamelCase       : Dict    =      common_inputs['''decoder_input_ids'''].shape[1]
  						_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_attention_heads
  						_UpperCamelCase       : List[Any]    =      (
  						    batch,
  						    num_encoder_attention_heads,
  						    encoder_seq_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  						_UpperCamelCase       : int    =      decoder_seq_length + 3
  						_UpperCamelCase       : Union[str, Any]    =      (
  						    batch,
  						    num_decoder_attention_heads,
  						    decoder_past_length,
  						    self._config.hidden_size // num_decoder_attention_heads,
  						)
  
  						_UpperCamelCase       : Any    =      torch.cat(
  						    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,					_snake_case     )]  ,					dim=1     )
  
  						_UpperCamelCase       : Any    =      []
  						# If the number of encoder and decoder layers are present in the model configuration, both are considered
  						_UpperCamelCase,							_UpperCamelCase       : int    =      self.num_layers
  						_UpperCamelCase       : Union[str, Any]    =      min(_snake_case  ,					_snake_case     )
  						_UpperCamelCase       : Union[str, Any]    =      max(_snake_case  ,					_snake_case     ) - min_num_layers
  						_UpperCamelCase       : Optional[int]    =      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
  
  						for _ in range(_snake_case     ):
  								common_inputs["past_key_values"].append(
  								    (
  								        torch.zeros(_snake_case     ),
  								        torch.zeros(_snake_case     ),
  								        torch.zeros(_snake_case     ),
  								        torch.zeros(_snake_case     ),
  								    )     )
  						# TODO: test this.
  						_UpperCamelCase       : Optional[Any]    =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
  						for _ in range(_snake_case  ,					_snake_case     ):
  								common_inputs["past_key_values"].append((torch.zeros(_snake_case     ), torch.zeros(_snake_case     ))     )
  				return common_inputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				_UpperCamelCase       : Dict    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  				if self.use_past:
  						if not is_torch_available():
  								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
  						else:
  								import torch
  						_UpperCamelCase,							_UpperCamelCase       : Union[str, Any]    =      common_inputs['''input_ids'''].shape
  						# Not using the same length for past_key_values
  						_UpperCamelCase       : str    =      seqlen + 2
  						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_layers
  						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_attention_heads
  						_UpperCamelCase       : List[str]    =      (
  						    batch,
  						    num_encoder_attention_heads,
  						    past_key_values_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  
  						_UpperCamelCase       : Optional[int]    =      common_inputs['''attention_mask'''].dtype
  						_UpperCamelCase       : Union[str, Any]    =      torch.cat(
  						    [common_inputs['''attention_mask'''], torch.ones(_snake_case  ,					_snake_case  ,					dtype=_snake_case     )]  ,					dim=1     )
  						_UpperCamelCase       : int    =      [
  						    (torch.zeros(_snake_case     ), torch.zeros(_snake_case     )) for _ in range(_snake_case     )
  						]
  				return common_inputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				# Copied from OnnxConfig.generate_dummy_inputs
  				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
  				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
  				_UpperCamelCase       : Union[str, Any]    =      compute_effective_axis_dimension(
  				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_batch  ,					num_token_to_add=0     )
  
  				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  				_UpperCamelCase       : List[Any]    =      tokenizer.num_special_tokens_to_add(_snake_case     )
  				_UpperCamelCase       : List[str]    =      compute_effective_axis_dimension(
  				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_sequence  ,					num_token_to_add=_snake_case     )
  
  				# Generate dummy inputs according to compute batch and sequence
  				_UpperCamelCase       : Optional[Any]    =      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
  				_UpperCamelCase       : Any    =      dict(tokenizer(_snake_case  ,					return_tensors=_snake_case     )     )
  				return common_inputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
  
  				elif self.task == "causal-lm":
  						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_causal_lm(
  						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
  				else:
  						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
  
  				return common_inputs
  
  
  
  
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Tuple:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : List[Any]    =      super()._flatten_past_key_values_(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  				else:
  						_UpperCamelCase       : int    =      super(_snake_case  ,					self     )._flatten_past_key_values_(
  						    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  
  
  
  
  