



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_:int             =						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_:Union[str, Any]             =						{"""facebook/bart-base""": BartForConditionalGeneration}
SCREAMING_SNAKE_CASE_:Optional[int]             =						{"""facebook/bart-base""": BartTokenizer}




def 	__UpperCamelCase	(  )       ->  List[str]:



    """simple docstring"""






    A :       List[str]          =   argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""		)
    parser.add_argument(
        """--validation_file"""      ,     type=_lowerCAmelCase      ,     default=_lowerCAmelCase      ,     help="""A csv or a json file containing the validation data."""		)
    parser.add_argument(
        """--max_length"""      ,     type=_lowerCAmelCase      ,     default=5      ,     help="""The maximum total input sequence length after tokenization."""      ,     )
    parser.add_argument(
        """--num_beams"""      ,     type=_lowerCAmelCase      ,     default=_lowerCAmelCase      ,     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=_lowerCAmelCase      ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,     required=_lowerCAmelCase      ,     )
    parser.add_argument(
        """--config_name"""      ,     type=_lowerCAmelCase      ,     default=_lowerCAmelCase      ,     help="""Pretrained config name or path if not the same as model_name"""      ,     )
    parser.add_argument(
        """--device"""      ,     type=_lowerCAmelCase      ,     default="""cpu"""      ,     help="""Device where the model will be run"""      ,     )
    parser.add_argument("""--output_file_path"""      ,     type=_lowerCAmelCase      ,     default=_lowerCAmelCase      ,     help="""Where to store the final ONNX file."""		)

    A :       Tuple          =   parser.parse_args()

    return args




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase="cpu"		)       ->  str:



    """simple docstring"""






    A :       Optional[Any]          =   model_dict[model_name].from_pretrained(_lowerCAmelCase		).to(_lowerCAmelCase		)
    A :       Tuple          =   tokenizer_dict[model_name].from_pretrained(_lowerCAmelCase		)

    if model_name in ["facebook/bart-base"]:
        A :       Dict          =   0
        A :       Union[str, Any]          =   None
        A :       Any          =   0

    return huggingface_model, tokenizer




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






    model.eval()

    A :       List[str]          =   None
    A :       Dict          =   torch.jit.script(BARTBeamSearchGenerator(_lowerCAmelCase		)		)

    with torch.no_grad():
        A :       Optional[int]          =   """My friends are cool but they eat too many carbs."""
        A :       Optional[int]          =   tokenizer([ARTICLE_TO_SUMMARIZE]      ,     max_length=1024      ,     return_tensors="""pt"""		).to(model.device		)

        A :       Optional[int]          =   model.generate(
            inputs["""input_ids"""]      ,     attention_mask=inputs["""attention_mask"""]      ,     num_beams=_lowerCAmelCase      ,     max_length=_lowerCAmelCase      ,     early_stopping=_lowerCAmelCase      ,     decoder_start_token_id=model.config.decoder_start_token_id      ,     )

        torch.onnx.export(
            _lowerCAmelCase      ,     (
                inputs["""input_ids"""],
                inputs["""attention_mask"""],
                num_beams,
                max_length,
                model.config.decoder_start_token_id,
            )      ,     _lowerCAmelCase      ,     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=_lowerCAmelCase      ,     )

        logger.info("""Model exported to {}""".format(_lowerCAmelCase		)		)

        A :       int          =   remove_dup_initializers(os.path.abspath(_lowerCAmelCase		)		)

        logger.info("""Deduplicated and optimized model written to {}""".format(_lowerCAmelCase		)		)

        A :       Union[str, Any]          =   onnxruntime.InferenceSession(_lowerCAmelCase		)
        A :       Optional[int]          =   ort_sess.run(
            _lowerCAmelCase      ,     {
                """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                """num_beams""": np.array(_lowerCAmelCase		),
                """max_length""": np.array(_lowerCAmelCase		),
                """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	(  )       ->  List[str]:



    """simple docstring"""






    A :       List[Any]          =   parse_args()
    A :       Tuple          =   5
    A :       Optional[Any]          =   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()

    A :       str          =   torch.device(args.device		)

    A     ,							A :       Optional[int]          =   load_model_tokenizer(args.model_name_or_path      ,     _lowerCAmelCase		)

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

    model.to(_lowerCAmelCase		)

    if args.max_length:
        A :       str          =   args.max_length

    if args.num_beams:
        A :       List[str]          =   args.num_beams

    if args.output_file_path:
        A :       str          =   args.output_file_path
    else:
        A :       List[str]          =   """BART.onnx"""

    logger.info("""Exporting model to ONNX"""		)
    export_and_validate_model(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)


if __name__ == "__main__":
     main()







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

import numpy as np
import torch

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_:Dict             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      ["input_features", "is_longer"]






    def __init__(       self,		lowerCamelCase__=64,		lowerCamelCase__=4_8000,		lowerCamelCase__=480,		lowerCamelCase__=10,		lowerCamelCase__=1024,		lowerCamelCase__=0.0,		lowerCamelCase__=False,		lowerCamelCase__ = 0,		lowerCamelCase__ = 1_4000,		lowerCamelCase__ = None,		lowerCamelCase__ = "fusion",		lowerCamelCase__ = "repeatpad",		**lowerCamelCase__,		):
        super().__init__(
            feature_size=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		padding_value=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        A :       Tuple          =   top_db
        A :       Dict          =   truncation
        A :       int          =   padding
        A :       Optional[Any]          =   fft_window_size
        A :       Optional[Any]          =   (fft_window_size >> 1) + 1
        A :       List[Any]          =   hop_length
        A :       Tuple          =   max_length_s
        A :       str          =   max_length_s * sampling_rate
        A :       List[str]          =   sampling_rate
        A :       Dict          =   frequency_min
        A :       str          =   frequency_max
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm=lowerCamelCase__,		mel_scale="""htk""",		)
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm="""slaney""",		mel_scale="""slaney""",		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   copy.deepcopy(self.__dict__		)
        A :       Tuple          =   self.__class__.__name__
        if "mel_filters" in output:
            del output["mel_filters"]
        if "mel_filters_slaney" in output:
            del output["mel_filters_slaney"]
        return output






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       Optional[Any]          =   spectrogram(
            lowerCamelCase__,		window_function(self.fft_window_size,		"""hann"""		),		frame_length=self.fft_window_size,		hop_length=self.hop_length,		power=2.0,		mel_filters=lowerCamelCase__,		log_mel="""dB""",		)
        return log_mel_spectrogram.T






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   np.array_split(list(range(0,		total_frames - chunk_frames + 1		)		),		3		)
        if len(ranges[1]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Any          =   [0]
        if len(ranges[2]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Union[str, Any]          =   [0]
        # randomly choose index for each part
        A :       str          =   np.random.choice(ranges[0]		)
        A :       str          =   np.random.choice(ranges[1]		)
        A :       Tuple          =   np.random.choice(ranges[2]		)

        A :       List[str]          =   mel[idx_front : idx_front + chunk_frames, :]
        A :       Union[str, Any]          =   mel[idx_middle : idx_middle + chunk_frames, :]
        A :       Any          =   mel[idx_back : idx_back + chunk_frames, :]

        A :       Any          =   torch.tensor(mel[None, None, :]		)
        A :       Union[str, Any]          =   torch.nn.functional.interpolate(
            lowerCamelCase__,		size=[chunk_frames, 64],		mode="""bilinear""",		align_corners=lowerCamelCase__		)
        A :       List[Any]          =   mel_shrink[0][0].numpy()
        A :       Any          =   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],		axis=0		)
        return mel_fusion






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
                A :       List[Any]          =   True
                # random crop to max_length (for compatibility) -> this should be handled by self.pad
                A :       int          =   len(lowerCamelCase__		) - max_length
                A :       Optional[int]          =   np.random.randint(0,		overflow + 1		)
                A :       List[Any]          =   waveform[idx : idx + max_length]
                A :       Optional[Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]
            elif truncation == "fusion":
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       List[str]          =   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                A :       Optional[Any]          =   mel.shape[0]
                if chunk_frames == total_frames:
                    # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                    # In this case, we just use the whole audio.
                    A :       str          =   np.stack([mel, mel, mel, mel],		axis=0		)
                    A :       Union[str, Any]          =   False
                else:
                    A :       Dict          =   self._random_mel_fusion(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
                    A :       Tuple          =   True
            else:
                raise NotImplementedError(f'''data_truncating {truncation} not implemented'''		)

        else:
            A :       int          =   False
            # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
            if waveform.shape[0] < max_length:
                if padding == "repeat":
                    A :       Optional[int]          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Union[str, Any]          =   np.stack(np.tile(lowerCamelCase__,		n_repeat + 1		)		)[:max_length]
                if padding == "repeatpad":
                    A :       Dict          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Dict          =   np.stack(np.tile(lowerCamelCase__,		lowerCamelCase__		)		)
                A :       Any          =   np.pad(lowerCamelCase__,		(0, max_length - waveform.shape[0]),		mode="""constant""",		constant_values=0		)

            if truncation == "fusion":
                A :       Union[str, Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       Tuple          =   np.stack([input_mel, input_mel, input_mel, input_mel],		axis=0		)
            else:
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]

        return input_mel, longer






    def __call__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Any          =   truncation if truncation is not None else self.truncation
        A :       Union[str, Any]          =   padding if padding else self.padding

        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."""		)

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

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

        # always return batch
        if not is_batched:
            A :       List[Any]          =   [np.asarray(lowerCamelCase__		)]

        # convert to mel spectrogram, truncate and pad if needed.
        A :       Tuple          =   [
            self._get_input_mel(lowerCamelCase__,		max_length if max_length else self.nb_max_samples,		lowerCamelCase__,		lowerCamelCase__		)
            for waveform in raw_speech
        ]

        A :       Optional[Any]          =   []
        A :       Optional[Any]          =   []
        for mel, longer in padded_inputs:
            input_mel.append(lowerCamelCase__		)
            is_longer.append(lowerCamelCase__		)

        if truncation == "fusion" and sum(lowerCamelCase__		) == 0:
            # if no audio is longer than 10s, then randomly select one audio to be longer
            A :       Tuple          =   np.random.randint(0,		len(lowerCamelCase__		)		)
            A :       str          =   True

        if isinstance(input_mel[0],		lowerCamelCase__		):
            A :       int          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for feature in input_mel]

        # is_longer is a list of bool
        A :       Optional[int]          =   [[longer] for longer in is_longer]

        A :       Union[str, Any]          =   {"""input_features""": input_mel, """is_longer""": is_longer}
        A :       Tuple          =   BatchFeature(lowerCamelCase__		)

        if return_tensors is not None:
            A :       List[str]          =   input_features.convert_to_tensors(lowerCamelCase__		)

        return input_features


