


"""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		= getLogger(__name__)


__UpperCamelCase		= '''cuda''' if torch.cuda.is_available() else '''cpu'''


def        lowercase     (SCREAMING_SNAKE_CASE_						:							List[str]					,     SCREAMING_SNAKE_CASE_						:							str					,     SCREAMING_SNAKE_CASE_						:							str					,     SCREAMING_SNAKE_CASE_						:							int = 8					,     SCREAMING_SNAKE_CASE_						:							str = DEFAULT_DEVICE					,     SCREAMING_SNAKE_CASE_						:							Union[str, Any]=False					,     SCREAMING_SNAKE_CASE_						:							Dict="summarization"					,     SCREAMING_SNAKE_CASE_						:							Optional[int]=None					,     **SCREAMING_SNAKE_CASE_						:							List[str]					,     )  ->		Dict:
						SCREAMING_SNAKE_CASE						=   Path(__snake_case    ).open('w'					,     encoding='utf-8'    )
						SCREAMING_SNAKE_CASE						=   str(__snake_case    )
						SCREAMING_SNAKE_CASE						=   AutoModelForSeqaSeqLM.from_pretrained(__snake_case    ).to(__snake_case    )
						if fpaa:
												SCREAMING_SNAKE_CASE						=   model.half()

						SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained(__snake_case    )
						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(__snake_case					,     __snake_case    )
						if prefix is None:
												SCREAMING_SNAKE_CASE						=   prefix or getattr(model.config					,     'prefix'					,     ''    ) or ''
						for examples_chunk in tqdm(list(chunks(__snake_case					,     __snake_case    )    )    ):
												SCREAMING_SNAKE_CASE						=   [prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE						=   tokenizer(__snake_case					,     return_tensors='pt'					,     truncation=__snake_case					,     padding='longest'    ).to(__snake_case    )
												SCREAMING_SNAKE_CASE						=   model.generate(
												    input_ids=batch.input_ids					,     attention_mask=batch.attention_mask					,     **__snake_case					,     )
												SCREAMING_SNAKE_CASE						=   tokenizer.batch_decode(__snake_case					,     skip_special_tokens=__snake_case					,     clean_up_tokenization_spaces=__snake_case    )
												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(__snake_case    )
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,     4    )}


def        lowercase     ()  ->		str:
						return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'    )


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

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

						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(__snake_case    )]
						SCREAMING_SNAKE_CASE						=   score_fn(__snake_case					,     __snake_case    )
						scores.update(__snake_case    )

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

						if verbose:
												print(__snake_case    )

						if args.score_path is not None:
												json.dump(__snake_case					,     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'''

import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


A_     :						List[Any]    							=logging.get_logger(__name__)


class     __UpperCAmelCase       ( __a		):
  __A :				int											= ['input_values', 'attention_mask']





  def __init__(	self    ,     _lowerCamelCase = 1    ,     _lowerCamelCase = 1_6000    ,     _lowerCamelCase = 0.0    ,     _lowerCamelCase = False    ,     _lowerCamelCase = 80    ,     _lowerCamelCase = 16    ,     _lowerCamelCase = 64    ,     _lowerCamelCase = "hann_window"    ,     _lowerCamelCase = 1.0    ,     _lowerCamelCase = 80    ,     _lowerCamelCase = 7600    ,     _lowerCamelCase = 1E-10    ,     _lowerCamelCase = 2    ,     _lowerCamelCase = True    ,     **_lowerCamelCase    ,     ):
     super().__init__(feature_size=_lowerCamelCase    ,     sampling_rate=_lowerCamelCase    ,     padding_value=_lowerCamelCase    ,     **_lowerCamelCase )
     lowerCAmelCase_			=      do_normalize
     lowerCAmelCase_			=      return_attention_mask

     lowerCAmelCase_			=      num_mel_bins
     lowerCAmelCase_			=      hop_length
     lowerCAmelCase_			=      win_length
     lowerCAmelCase_			=      win_function
     lowerCAmelCase_			=      frame_signal_scale
     lowerCAmelCase_			=      fmin
     lowerCAmelCase_			=      fmax
     lowerCAmelCase_			=      mel_floor
     lowerCAmelCase_			=      reduction_factor

     lowerCAmelCase_			=      win_length * sampling_rate // 1000
     lowerCAmelCase_			=      hop_length * sampling_rate // 1000
     lowerCAmelCase_			=      optimal_fft_length(self.sample_size )
     lowerCAmelCase_			=      (self.n_fft // 2) + 1

     lowerCAmelCase_			=      window_function(window_length=self.sample_size    ,     name=self.win_function    ,     periodic=_lowerCamelCase )

     lowerCAmelCase_			=      mel_filter_bank(
         num_frequency_bins=self.n_freqs    ,     num_mel_filters=self.num_mel_bins    ,     min_frequency=self.fmin    ,     max_frequency=self.fmax    ,     sampling_rate=self.sampling_rate    ,     norm='''slaney'''    ,     mel_scale='''slaney'''    ,     )

     if frame_signal_scale != 1.0:
        warnings.warn(
            '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''    ,     _lowerCamelCase    ,     )
     if reduction_factor != 2.0:
        warnings.warn(
            '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''    ,     _lowerCamelCase    ,     )





  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def 						UpperCAmelCase_	(	_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = 0.0 ):
     if attention_mask is not None:
        lowerCAmelCase_			=      np.array(_lowerCamelCase    ,     np.intaa )
        lowerCAmelCase_			=      []

        for vector, length in zip(_lowerCamelCase    ,     attention_mask.sum(-1 ) ):
           lowerCAmelCase_			=      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
           if length < normed_slice.shape[0]:
              lowerCAmelCase_			=      padding_value

           normed_input_values.append(_lowerCamelCase )
     else:
        lowerCAmelCase_			=      [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

     return normed_input_values





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     ):
     lowerCAmelCase_			=      spectrogram(
         _lowerCamelCase    ,     window=self.window    ,     frame_length=self.sample_size    ,     hop_length=self.sample_stride    ,     fft_length=self.n_fft    ,     mel_filters=self.mel_filters    ,     mel_floor=self.mel_floor    ,     log_mel='''log10'''    ,     )
     return log_mel_spec.T





  def __call__(	self    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     if audio is None and audio_target is None:
        raise ValueError('''You must provide either `audio` or `audio_target` values.''' )

     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 audio 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.''' )

     if audio is not None:
        lowerCAmelCase_			=      self._process_audio(
            _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase    ,     )
     else:
        lowerCAmelCase_			=      None

     if audio_target is not None:
        lowerCAmelCase_			=      self._process_audio(
            _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase    ,     )

        if inputs is None:
           return inputs_target
        else:
           lowerCAmelCase_			=      inputs_target['''input_values''']
           lowerCAmelCase_			=      inputs_target.get('''attention_mask''' )
           if decoder_attention_mask is not None:
              lowerCAmelCase_			=      decoder_attention_mask

     return inputs





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = False    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      isinstance(_lowerCamelCase    ,     np.ndarray ) and len(speech.shape ) > 1
     if is_batched_numpy and len(speech.shape ) > 2:
        raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
     lowerCAmelCase_			=      is_batched_numpy or (
         isinstance(_lowerCamelCase    ,     (list, tuple) ) and (isinstance(speech[0]    ,     (np.ndarray, tuple, list) ))
     )

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

     # always return batch
     if not is_batched:
        lowerCAmelCase_			=      [speech]

     # needed to make pad() work on spectrogram inputs
     lowerCAmelCase_			=      self.feature_size

     # convert into correct format for padding
     if is_target:
        lowerCAmelCase_			=      [self._extract_mel_features(_lowerCamelCase ) for waveform in speech]
        lowerCAmelCase_			=      BatchFeature({'''input_values''': features} )
        lowerCAmelCase_			=      self.num_mel_bins
     else:
        lowerCAmelCase_			=      BatchFeature({'''input_values''': speech} )

     lowerCAmelCase_			=      self.pad(
         _lowerCamelCase    ,     padding=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     pad_to_multiple_of=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

     lowerCAmelCase_			=      feature_size_hack

     # convert input values to correct format
     lowerCAmelCase_			=      padded_inputs['''input_values''']
     if not isinstance(input_values[0]    ,     np.ndarray ):
        lowerCAmelCase_			=      [np.asarray(_lowerCamelCase    ,     dtype=np.floataa ) for array in input_values]
     elif (
         not isinstance(_lowerCamelCase    ,     np.ndarray )
         and isinstance(input_values[0]    ,     np.ndarray )
         and input_values[0].dtype is np.dtype(np.floataa )
     ):
        lowerCAmelCase_			=      [array.astype(np.floataa ) for array in input_values]
     elif isinstance(_lowerCamelCase    ,     np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
        lowerCAmelCase_			=      input_values.astype(np.floataa )

     # convert attention_mask to correct format
     lowerCAmelCase_			=      padded_inputs.get('''attention_mask''' )
     if attention_mask is not None:
        lowerCAmelCase_			=      [np.asarray(_lowerCamelCase    ,     dtype=np.intaa ) for array in attention_mask]

     # zero-mean and unit-variance normalization
     if not is_target and self.do_normalize:
        lowerCAmelCase_			=      (
            attention_mask
            if self._get_padding_strategies(_lowerCamelCase    ,     max_length=_lowerCamelCase ) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        lowerCAmelCase_			=      self.zero_mean_unit_var_norm(
            padded_inputs['''input_values''']    ,     attention_mask=_lowerCamelCase    ,     padding_value=self.padding_value )

     if return_tensors is not None:
        lowerCAmelCase_			=      padded_inputs.convert_to_tensors(_lowerCamelCase )

     return padded_inputs







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      super().to_dict()

     # Don't serialize these as they are derived from the other properties.
     lowerCAmelCase_			=      ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
     for name in names:
        if name in output:
           del output[name]

     return output





