





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

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)






class 					_SCREAMING_SNAKE_CASE(   UpperCamelCase_  ):
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  ['''input_features''', '''attention_mask''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__=80		,SCREAMING_SNAKE_CASE__=1_60_00		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=10		,SCREAMING_SNAKE_CASE__=25		,SCREAMING_SNAKE_CASE__="hamming_window"		,SCREAMING_SNAKE_CASE__=3_27_68.0		,SCREAMING_SNAKE_CASE__=0.9_7		,SCREAMING_SNAKE_CASE__=1.0		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=False		,**SCREAMING_SNAKE_CASE__		,)			->      Union[str, Any]:





             """simple docstring"""






             super().__init__(feature_size=UpperCamelCase__		,sampling_rate=UpperCamelCase__		,padding_value=UpperCamelCase__		,**UpperCamelCase__							)

             __SCREAMING_SNAKE_CASE   :Dict						=  feature_size
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  sampling_rate
             __SCREAMING_SNAKE_CASE   :int						=  padding_value
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  hop_length
             __SCREAMING_SNAKE_CASE   :Tuple						=  win_length
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  frame_signal_scale
             __SCREAMING_SNAKE_CASE   :int						=  preemphasis_coeff
             __SCREAMING_SNAKE_CASE   :List[Any]						=  mel_floor
             __SCREAMING_SNAKE_CASE   :int						=  normalize_means
             __SCREAMING_SNAKE_CASE   :List[str]						=  normalize_vars
             __SCREAMING_SNAKE_CASE   :Any						=  win_function
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  return_attention_mask

             __SCREAMING_SNAKE_CASE   :int						=  win_length * sampling_rate // 10_00
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hop_length * sampling_rate // 10_00

             __SCREAMING_SNAKE_CASE   :int						=  optimal_fft_length(self.sample_size							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  (self.n_fft // 2) + 1




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             if self.win_function == "hamming_window":
                   __SCREAMING_SNAKE_CASE   :List[str]						=  window_function(window_length=self.sample_size		,name=self.win_function		,periodic=UpperCamelCase__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  window_function(window_length=self.sample_size		,name=self.win_function							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  mel_filter_bank(
                 num_frequency_bins=self.n_freqs		,num_mel_filters=self.feature_size		,min_frequency=0.0		,max_frequency=self.sampling_rate / 2.0		,sampling_rate=self.sampling_rate		,)

             __SCREAMING_SNAKE_CASE   :Tuple						=  spectrogram(
                 one_waveform * self.frame_signal_scale		,window=UpperCamelCase__		,frame_length=self.sample_size		,hop_length=self.sample_stride		,fft_length=self.n_fft		,center=UpperCamelCase__		,preemphasis=self.preemphasis_coeff		,mel_filters=UpperCamelCase__		,mel_floor=self.mel_floor		,log_mel='''log'''		,)
             return msfc_features.T




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      List[Any]:





             """simple docstring"""






             if self.normalize_means:
                   __SCREAMING_SNAKE_CASE   :str						=  x[:input_length].mean(axis=0							)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  np.subtract(UpperCamelCase__		,UpperCamelCase__							)
             if self.normalize_vars:
                   __SCREAMING_SNAKE_CASE   :str						=  x[:input_length].std(axis=0							)
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.divide(UpperCamelCase__		,UpperCamelCase__							)

             if input_length < x.shape[0]:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  padding_value

             # make sure array is in float32
             __SCREAMING_SNAKE_CASE   :str						=  x.astype(np.floataa							)

             return x




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
             return [self._normalize_one(UpperCamelCase__		,UpperCamelCase__		,self.padding_value							) for x, n in zip(UpperCamelCase__		,UpperCamelCase__							)]




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[int]:





             """simple docstring"""






             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.'''							)

             __SCREAMING_SNAKE_CASE   :Any						=  isinstance(UpperCamelCase__		,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}'''							)
             __SCREAMING_SNAKE_CASE   :str						=  is_batched_numpy or (
                 isinstance(UpperCamelCase__		,(list, tuple)							) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)							))
             )

             if is_batched:
                   __SCREAMING_SNAKE_CASE   :int						=  [np.asarray(UpperCamelCase__		,dtype=np.floataa							) for speech in raw_speech]
             elif not is_batched and not isinstance(UpperCamelCase__		,np.ndarray							):
                   __SCREAMING_SNAKE_CASE   :Dict						=  np.asarray(UpperCamelCase__		,dtype=np.floataa							)
             elif isinstance(UpperCamelCase__		,np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  raw_speech.astype(np.floataa							)

             # always return batch
             if not is_batched:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  [raw_speech]

             # extract fbank features
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  [self._extract_mfsc_features(UpperCamelCase__							) for one_waveform in raw_speech]

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

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.pad(
                 UpperCamelCase__		,padding=UpperCamelCase__		,max_length=UpperCamelCase__		,truncation=UpperCamelCase__		,pad_to_multiple_of=UpperCamelCase__		,return_attention_mask=UpperCamelCase__		,**UpperCamelCase__		,)
             # make sure list is in array format
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  padded_inputs.get('''input_features'''							)
             if isinstance(input_features[0]		,UpperCamelCase__							):
                   __SCREAMING_SNAKE_CASE   :Any						=  [np.asarray(UpperCamelCase__		,dtype=np.floataa							) for feature in input_features]

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  padded_inputs.get('''attention_mask'''							)
             if attention_mask is not None:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  [np.asarray(UpperCamelCase__		,dtype=np.intaa							) for array in attention_mask]

             if self.normalize_means or self.normalize_vars:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  (
                       np.array(UpperCamelCase__		,dtype=np.intaa							)
                       if self._get_padding_strategies(UpperCamelCase__		,max_length=UpperCamelCase__							) is not PaddingStrategy.DO_NOT_PAD
                       and padding
                       else None
                   )
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  self.normalize(
                       padded_inputs['''input_features''']		,attention_mask=UpperCamelCase__							)

             if return_tensors is not None:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  padded_inputs.convert_to_tensors(UpperCamelCase__							)

             return padded_inputs
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		:       Any									=     getLogger(__name__)


__UpperCamelCase		:       int									=     'cuda' if torch.cuda.is_available() else 'cpu'
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 8		,  _lowercase = DEFAULT_DEVICE		,  _lowercase=False		,  _lowercase="summarization"		,  _lowercase=None		,  **_lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							Path(_lowercase						).open('''w'''		,  encoding='''utf-8'''						)
						SCREAMING_SNAKE_CASE							:    int							=							str(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							AutoModelForSeqaSeqLM.from_pretrained(_lowercase						).to(_lowercase						)
						if fpaa:
												SCREAMING_SNAKE_CASE							:    Dict							=							model.half()

						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoTokenizer.from_pretrained(_lowercase						)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						# update config with task specific params
						use_task_specific_params(_lowercase		,  _lowercase						)
						if prefix is None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							prefix or getattr(model.config		,  '''prefix'''		,  ''''''						) or ''''''
						for examples_chunk in tqdm(list(chunks(_lowercase		,  _lowercase						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase		,  padding='''longest'''						).to(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							model.generate(
												    input_ids=batch.input_ids		,  attention_mask=batch.attention_mask		,  **_lowercase		,  )
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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()
						SCREAMING_SNAKE_CASE							:    Tuple							=							int(time.time() - start_time						)  # seconds
						SCREAMING_SNAKE_CASE							:    str							=							len(_lowercase						)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,  4						)}
def       A	(    ):
						return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)
def       A	(    _lowercase=True						):
						SCREAMING_SNAKE_CASE							:    int							=							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
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parser.parse_known_args()
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parse_numeric_n_bool_cl_kwargs(_lowercase						)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
						if args.n_obs > 0:
												SCREAMING_SNAKE_CASE							:    Any							=							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'''						)

						SCREAMING_SNAKE_CASE							:    List[str]							=							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
						SCREAMING_SNAKE_CASE							:    Dict							=							calculate_bleu if '''translation''' in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[x.rstrip() for x in open(args.save_path						).readlines()]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_lowercase						)]
						SCREAMING_SNAKE_CASE							:    dict							=							score_fn(_lowercase		,  _lowercase						)
						scores.update(_lowercase						)

						if args.dump_args:
												scores.update(_lowercase						)
						if args.info:
												SCREAMING_SNAKE_CASE							:    Tuple							=							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)





