

import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

_SCREAMING_SNAKE_CASE					:  Tuple             =      logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {'''facebook/bart-base''': BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {'''facebook/bart-base''': BartTokenizer}
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''    )
     parser.add_argument(
         '''--validation_file'''				,		type=_A				,		default=_A				,		help='''A csv or a json file containing the validation data.'''    )
     parser.add_argument(
         '''--max_length'''				,		type=_A				,		default=5				,		help='''The maximum total input sequence length after tokenization.'''				,		)
     parser.add_argument(
         '''--num_beams'''				,		type=_A				,		default=_A				,		help=(
             '''Number of beams to use for evaluation. This argument will be '''
             '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
         )				,		)
     parser.add_argument(
         '''--model_name_or_path'''				,		type=_A				,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,		required=_A				,		)
     parser.add_argument(
         '''--config_name'''				,		type=_A				,		default=_A				,		help='''Pretrained config name or path if not the same as model_name'''				,		)
     parser.add_argument(
         '''--device'''				,		type=_A				,		default='''cpu'''				,		help='''Device where the model will be run'''				,		)
     parser.add_argument('''--output_file_path'''				,		type=_A				,		default=_A				,		help='''Where to store the final ONNX file.'''    )

     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     return args
def     UpperCAmelCase_       (						_A				,		_A="cpu"    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   model_dict[model_name].from_pretrained(_A    ).to(_A    )
     SCREAMING_SNAKE_CASE__   =   tokenizer_dict[model_name].from_pretrained(_A    )

     if model_name in ["facebook/bart-base"]:
          SCREAMING_SNAKE_CASE__   =   0
          SCREAMING_SNAKE_CASE__   =   None
          SCREAMING_SNAKE_CASE__   =   0

     return huggingface_model, tokenizer
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     model.eval()

     SCREAMING_SNAKE_CASE__   =   None
     SCREAMING_SNAKE_CASE__   =   torch.jit.script(BARTBeamSearchGenerator(_A    )    )

     with torch.no_grad():
          SCREAMING_SNAKE_CASE__   =   '''My friends are cool but they eat too many carbs.'''
          SCREAMING_SNAKE_CASE__   =   tokenizer([ARTICLE_TO_SUMMARIZE]				,		max_length=10_24				,		return_tensors='''pt'''    ).to(model.device    )

          SCREAMING_SNAKE_CASE__   =   model.generate(
              inputs['''input_ids''']				,		attention_mask=inputs['''attention_mask''']				,		num_beams=_A				,		max_length=_A				,		early_stopping=_A				,		decoder_start_token_id=model.config.decoder_start_token_id				,		)

          torch.onnx.export(
              _A				,		(
                  inputs['''input_ids'''],
                  inputs['''attention_mask'''],
                  num_beams,
                  max_length,
                  model.config.decoder_start_token_id,
              )				,		_A				,		opset_version=14				,		input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']				,		output_names=['''output_ids''']				,		dynamic_axes={
                  '''input_ids''': {0: '''batch''', 1: '''seq'''},
                  '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
              }				,		example_outputs=_A				,		)

          logger.info('''Model exported to {}'''.format(_A    )    )

          SCREAMING_SNAKE_CASE__   =   remove_dup_initializers(os.path.abspath(_A    )    )

          logger.info('''Deduplicated and optimized model written to {}'''.format(_A    )    )

          SCREAMING_SNAKE_CASE__   =   onnxruntime.InferenceSession(_A    )
          SCREAMING_SNAKE_CASE__   =   ort_sess.run(
              _A				,		{
                  '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                  '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                  '''num_beams''': np.array(_A    ),
                  '''max_length''': np.array(_A    ),
                  '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id    ),
              }				,		)

          np.testing.assert_allclose(summary_ids.cpu().numpy()				,		ort_out[0]				,		rtol=1e-3				,		atol=1e-3    )

          logger.info('''Model outputs from torch and ONNX Runtime are similar.'''    )
          logger.info('''Success.'''    )
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   parse_args()
     SCREAMING_SNAKE_CASE__   =   5
     SCREAMING_SNAKE_CASE__   =   4

     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,		datefmt='''%m/%d/%Y %H:%M:%S'''				,		level=logging.INFO				,		)

     logger.setLevel(logging.INFO    )
     transformers.utils.logging.set_verbosity_error()

     SCREAMING_SNAKE_CASE__   =   torch.device(args.device    )

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   load_model_tokenizer(args.model_name_or_path				,		_A    )

     if model.config.decoder_start_token_id is None:
          raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''    )

     model.to(_A    )

     if args.max_length:
          SCREAMING_SNAKE_CASE__   =   args.max_length

     if args.num_beams:
          SCREAMING_SNAKE_CASE__   =   args.num_beams

     if args.output_file_path:
          SCREAMING_SNAKE_CASE__   =   args.output_file_path
     else:
          SCREAMING_SNAKE_CASE__   =   '''BART.onnx'''

     logger.info('''Exporting model to ONNX'''    )
     export_and_validate_model(_A				,		_A				,		_A				,		_A				,		_A    )


if __name__ == "__main__":
       main()





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["input_features"]






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Tuple=80     ,       __lowerCamelCase						:		List[Any]=1_6000     ,       __lowerCamelCase						:		Optional[int]=160     ,       __lowerCamelCase						:		List[str]=30     ,       __lowerCamelCase						:		List[Any]=400     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		str=False     ,       **__lowerCamelCase						:		List[str]     ,       )		->							Any:
       super().__init__(
           feature_size=__lowerCamelCase     ,       sampling_rate=__lowerCamelCase     ,       padding_value=__lowerCamelCase     ,       return_attention_mask=__lowerCamelCase     ,       **__lowerCamelCase     ,       )
       SCREAMING_SNAKE_CASE__   =   n_fft
       SCREAMING_SNAKE_CASE__   =   hop_length
       SCREAMING_SNAKE_CASE__   =   chunk_length
       SCREAMING_SNAKE_CASE__   =   chunk_length * sampling_rate
       SCREAMING_SNAKE_CASE__   =   self.n_samples // hop_length
       SCREAMING_SNAKE_CASE__   =   sampling_rate
       SCREAMING_SNAKE_CASE__   =   mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2     ,       num_mel_filters=__lowerCamelCase     ,       min_frequency=0.0     ,       max_frequency=8000.0     ,       sampling_rate=__lowerCamelCase     ,       norm='''slaney'''     ,       mel_scale='''slaney'''     ,       )






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.array )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   spectrogram(
           __lowerCamelCase     ,       window_function(self.n_fft     ,       '''hann''' )     ,       frame_length=self.n_fft     ,       hop_length=self.hop_length     ,       power=2.0     ,       mel_filters=self.mel_filters     ,       log_mel='''log10'''     ,       )
       SCREAMING_SNAKE_CASE__   =   log_spec[:, :-1]
       SCREAMING_SNAKE_CASE__   =   np.maximum(__lowerCamelCase     ,       log_spec.max() - 8.0 )
       SCREAMING_SNAKE_CASE__   =   (log_spec + 4.0) / 4.0
       return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def 				lowercase_		(							__lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		float = 0.0 )		->							List[np.ndarray]:
       if attention_mask is not None:
            SCREAMING_SNAKE_CASE__   =   np.array(__lowerCamelCase     ,       np.intaa )
            SCREAMING_SNAKE_CASE__   =   []

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

                 normed_input_values.append(__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

       return normed_input_values






  def __call__(							self						:		List[str]     ,       __lowerCamelCase						:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       __lowerCamelCase						:		Optional[str] = "max_length"     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       **__lowerCamelCase						:		List[str]     ,       )		->							BatchFeature:

       if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                     f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                     f''' was sampled with {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__   =   isinstance(__lowerCamelCase     ,       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__   =   is_batched_numpy or (
           isinstance(__lowerCamelCase     ,       (list, tuple) ) and (isinstance(raw_speech[0]     ,       (np.ndarray, tuple, list) ))
       )

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

       # always return batch
       if not is_batched:
            SCREAMING_SNAKE_CASE__   =   [np.asarray([raw_speech] ).T]

       SCREAMING_SNAKE_CASE__   =   BatchFeature({'''input_features''': raw_speech} )

       # convert into correct format for padding

       SCREAMING_SNAKE_CASE__   =   self.pad(
           __lowerCamelCase     ,       padding=__lowerCamelCase     ,       max_length=max_length if max_length else self.n_samples     ,       truncation=__lowerCamelCase     ,       pad_to_multiple_of=__lowerCamelCase     ,       return_attention_mask=return_attention_mask or do_normalize     ,       )

       # zero-mean and unit-variance normalization
       if do_normalize:
            SCREAMING_SNAKE_CASE__   =   self.zero_mean_unit_var_norm(
                padded_inputs['''input_features''']     ,       attention_mask=padded_inputs['''attention_mask''']     ,       padding_value=self.padding_value     ,       )
            SCREAMING_SNAKE_CASE__   =   np.stack(padded_inputs['''input_features''']     ,       axis=0 )

       # make sure list is in array format
       SCREAMING_SNAKE_CASE__   =   padded_inputs.get('''input_features''' ).transpose(2     ,       0     ,       1 )

       SCREAMING_SNAKE_CASE__   =   [self._np_extract_fbank_features(__lowerCamelCase ) for waveform in input_features[0]]

       if isinstance(input_features[0]     ,       __lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   [np.asarray(__lowerCamelCase     ,       dtype=np.floataa ) for feature in input_features]
       else:
            SCREAMING_SNAKE_CASE__   =   input_features

       if return_attention_mask:
            # rescale from sample (48000) to feature (3000)
            SCREAMING_SNAKE_CASE__   =   padded_inputs['''attention_mask'''][:, :: self.hop_length]

       if return_tensors is not None:
            SCREAMING_SNAKE_CASE__   =   padded_inputs.convert_to_tensors(__lowerCamelCase )

       return padded_inputs







  def 				lowercase_		(							self						:		str )		->							Dict[str, Any]:
       SCREAMING_SNAKE_CASE__   =   copy.deepcopy(self.__dict__ )
       SCREAMING_SNAKE_CASE__   =   self.__class__.__name__
       if "mel_filters" in output:
            del output["mel_filters"]
       return output


