





'''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


a__							:							str =getLogger(__name__)


a__							:							List[str] ='''cuda''' if torch.cuda.is_available() else '''cpu'''
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  str				,  __lowercase						:  str				,  __lowercase						:  int = 8				,  __lowercase						:  str = DEFAULT_DEVICE				,  __lowercase						:  Union[str, Any]=False				,  __lowercase						:  Optional[int]="summarization"				,  __lowercase						:  List[Any]=None				,  **__lowercase						:  int				,  ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  Path(__lowercase     ).open('w'				,  encoding='utf-8'     )
       __UpperCamelCase       =  str(__lowercase     )
       __UpperCamelCase       =  AutoModelForSeqaSeqLM.from_pretrained(__lowercase     ).to(__lowercase     )
       if fpaa:
              __UpperCamelCase       =  model.half()

       __UpperCamelCase       =  AutoTokenizer.from_pretrained(__lowercase     )
       logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

       __UpperCamelCase       =  time.time()
       # update config with task specific params
       use_task_specific_params(__lowercase				,  __lowercase     )
       if prefix is None:
              __UpperCamelCase       =  prefix or getattr(model.config				,  'prefix'				,  ''     ) or ''
       for examples_chunk in tqdm(list(chunks(__lowercase				,  __lowercase     )     )     ):
              __UpperCamelCase       =  [prefix + text for text in examples_chunk]
              __UpperCamelCase       =  tokenizer(__lowercase				,  return_tensors='pt'				,  truncation=__lowercase				,  padding='longest'     ).to(__lowercase     )
              __UpperCamelCase       =  model.generate(
                  input_ids=batch.input_ids				,  attention_mask=batch.attention_mask				,  **__lowercase				,  )
              __UpperCamelCase       =  tokenizer.batch_decode(__lowercase				,  skip_special_tokens=__lowercase				,  clean_up_tokenization_spaces=__lowercase     )
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'     )
                     fout.flush()
       fout.close()
       __UpperCamelCase       =  int(time.time() - start_time     )  # seconds
       __UpperCamelCase       =  len(__lowercase     )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs				,  4     )}
def 				lowercase__   (   ) ->      str:



       """simple docstring"""






       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )
def 				lowercase__   (   __lowercase						:  Tuple=True     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  argparse.ArgumentParser()
       parser.add_argument('model_name'				,  type=__lowercase				,  help='like facebook/bart-large-cnn,t5-base, etc.'     )
       parser.add_argument('input_path'				,  type=__lowercase				,  help='like cnn_dm/test.source'     )
       parser.add_argument('save_path'				,  type=__lowercase				,  help='where to save summaries'     )
       parser.add_argument('--reference_path'				,  type=__lowercase				,  required=__lowercase				,  help='like cnn_dm/test.target'     )
       parser.add_argument('--score_path'				,  type=__lowercase				,  required=__lowercase				,  default='metrics.json'				,  help='where to save metrics'     )
       parser.add_argument('--device'				,  type=__lowercase				,  required=__lowercase				,  default=__lowercase				,  help='cuda, cuda:1, cpu etc.'     )
       parser.add_argument(
           '--prefix'				,  type=__lowercase				,  required=__lowercase				,  default=__lowercase				,  help='will be added to the begininng of src examples'     )
       parser.add_argument('--task'				,  type=__lowercase				,  default='summarization'				,  help='used for task_specific_params + metrics'     )
       parser.add_argument('--bs'				,  type=__lowercase				,  default=8				,  required=__lowercase				,  help='batch size'     )
       parser.add_argument(
           '--n_obs'				,  type=__lowercase				,  default=-1				,  required=__lowercase				,  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=__lowercase				,  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       =  parser.parse_known_args()
       __UpperCamelCase       =  parse_numeric_n_bool_cl_kwargs(__lowercase     )
       if parsed_args and verbose:
              print(F'''parsed the following generate kwargs: {parsed_args}'''     )
       __UpperCamelCase       =  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
       if args.n_obs > 0:
              __UpperCamelCase       =  examples[: args.n_obs]
       Path(args.save_path     ).parent.mkdir(exist_ok=__lowercase     )

       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.'''     )

       if args.device == "cpu" and args.fpaa:
              # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
              raise ValueError('Can\'t mix --fp16 and --device cpu'     )

       __UpperCamelCase       =  generate_summaries_or_translations(
           __lowercase				,  args.save_path				,  args.model_name				,  batch_size=args.bs				,  device=args.device				,  fpaa=args.fpaa				,  task=args.task				,  prefix=args.prefix				,  **__lowercase				,  )

       if args.reference_path is None:
              return {}

       # Compute scores
       __UpperCamelCase       =  calculate_bleu if 'translation' in args.task else calculate_rouge
       __UpperCamelCase       =  [x.rstrip() for x in open(args.save_path     ).readlines()]
       __UpperCamelCase       =  [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(__lowercase     )]
       __UpperCamelCase       =  score_fn(__lowercase				,  __lowercase     )
       scores.update(__lowercase     )

       if args.dump_args:
              scores.update(__lowercase     )
       if args.info:
              __UpperCamelCase       =  args.info

       if verbose:
              print(__lowercase     )

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







'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a__							:							str =logging.get_logger(__name__)

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=["input_features", "attention_mask"]






 def __init__(       self       :							Union[str, Any]							,	__A       :							Optional[int]=8_0							,	__A       :							Tuple=1_6_0_0_0							,	__A       :							Optional[Any]=8_0							,	__A       :							Any=0.0							,	__A       :							Any=True							,	__A       :							List[str]=True							,	__A       :							str=True							,	**__A       :							List[Any]							,	):
        super().__init__(feature_size=__A							,	sampling_rate=__A							,	padding_value=__A							,	**__A	)
        __UpperCamelCase       =  num_mel_bins
        __UpperCamelCase       =  do_ceptral_normalize
        __UpperCamelCase       =  normalize_means
        __UpperCamelCase       =  normalize_vars
        __UpperCamelCase       =  True






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							np.ndarray							,	):
        __UpperCamelCase       =  waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        __UpperCamelCase       =  torch.from_numpy(__A	).unsqueeze(0	)
        __UpperCamelCase       =  ta_kaldi.fbank(__A							,	num_mel_bins=self.num_mel_bins							,	sample_frequency=self.sampling_rate	)
        return features.numpy()






 @staticmethod
 def        _lowerCamelCase					(       __A       :							np.ndarray							,	__A       :							int							,	__A       :							Optional[bool] = True							,	__A       :							Optional[bool] = True							,	__A       :							float = 0.0							,	):
        # make sure we normalize float32 arrays
        if normalize_means:
               __UpperCamelCase       =  x[:input_length].mean(axis=0	)
               __UpperCamelCase       =  np.subtract(__A							,	__A	)
        if normalize_vars:
               __UpperCamelCase       =  x[:input_length].std(axis=0	)
               __UpperCamelCase       =  np.divide(__A							,	__A	)

        if input_length < x.shape[0]:
               __UpperCamelCase       =  padding_value

        # make sure array is in float32
        __UpperCamelCase       =  x.astype(np.floataa	)

        return x






 def        _lowerCamelCase					(       self       :							int							,	__A       :							List[np.ndarray]							,	__A       :							Optional[np.ndarray] = None	):
        __UpperCamelCase       =  attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(__A							,	__A							,	self.normalize_means							,	self.normalize_vars							,	self.padding_value	)
            for x, n in zip(__A							,	__A	)
        ]






 def __call__(       self       :							List[Any]							,	__A       :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Optional[int] = None							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	**__A       :							Dict							,	):

        if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                          f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                          f''' {self.sampling_rate} and not {sampling_rate}.'''	)
        else:
               logger.warning(
                   'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                   'Failing to do so can result in silent errors that might be hard to debug.'	)

        __UpperCamelCase       =  isinstance(__A							,	np.ndarray	) and len(raw_speech.shape	) > 1
        if is_batched_numpy and len(raw_speech.shape	) > 2:
               raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''	)
        __UpperCamelCase       =  is_batched_numpy or (
            isinstance(__A							,	(list, tuple)	) and (isinstance(raw_speech[0]							,	(np.ndarray, tuple, list)	))
        )

        if is_batched:
               __UpperCamelCase       =  [np.asarray(__A							,	dtype=np.floataa	) for speech in raw_speech]
        elif not is_batched and not isinstance(__A							,	np.ndarray	):
               __UpperCamelCase       =  np.asarray(__A							,	dtype=np.floataa	)
        elif isinstance(__A							,	np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
               __UpperCamelCase       =  raw_speech.astype(np.floataa	)

        # always return batch
        if not is_batched:
               __UpperCamelCase       =  [raw_speech]

        # extract fbank features
        __UpperCamelCase       =  [self._extract_fbank_features(__A	) for waveform in raw_speech]

        # convert into correct format for padding
        __UpperCamelCase       =  BatchFeature({'input_features': features}	)

        __UpperCamelCase       =  self.pad(
            __A							,	padding=__A							,	max_length=__A							,	truncation=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	**__A							,	)

        # make sure list is in array format
        __UpperCamelCase       =  padded_inputs.get('input_features'	)
        if isinstance(input_features[0]							,	__A	):
               __UpperCamelCase       =  [np.asarray(__A							,	dtype=np.floataa	) for feature in input_features]

        __UpperCamelCase       =  padded_inputs.get('attention_mask'	)
        if attention_mask is not None:
               __UpperCamelCase       =  [np.asarray(__A							,	dtype=np.intaa	) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
               __UpperCamelCase       =  (
                   np.array(__A							,	dtype=np.intaa	)
                   if self._get_padding_strategies(__A							,	max_length=__A	) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               __UpperCamelCase       =  self.normalize(
                   padded_inputs['input_features']							,	attention_mask=__A	)

        if return_tensors is not None:
               __UpperCamelCase       =  padded_inputs.convert_to_tensors(__A	)

        return padded_inputs
