


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,
)

UpperCAmelCase   :    Dict     =		logging.getLogger(__name__)

UpperCAmelCase   :    Optional[Any]     =		{"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase   :    List[Any]     =		{"facebook/bart-base": BartTokenizer}






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  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."""      )

			lowerCamelCase    		=  parser.parse_args()

			return args






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]  ,							lowerCamelCase__		:						Any="cpu"      ):





			'''simple docstring'''




			lowerCamelCase    		=  model_dict[model_name].from_pretrained(lowerCamelCase__      ).to(lowerCamelCase__      )
			lowerCamelCase    		=  tokenizer_dict[model_name].from_pretrained(lowerCamelCase__      )

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

			return huggingface_model, tokenizer






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			model.eval()

			lowerCamelCase    		=  None
			lowerCamelCase    		=  torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase__      )      )

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

						lowerCamelCase    		=  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__      )      )

						lowerCamelCase    		=  remove_dup_initializers(os.path.abspath(lowerCamelCase__      )      )

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

						lowerCamelCase    		=  onnxruntime.InferenceSession(lowerCamelCase__      )
						lowerCamelCase    		=  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 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  parse_args()
			lowerCamelCase    		=  5
			lowerCamelCase    		=  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()

			lowerCamelCase    		=  torch.device(args.device      )

			lowerCamelCase		,		lowerCamelCase    		=  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:
						lowerCamelCase    		=  args.max_length

			if args.num_beams:
						lowerCamelCase    		=  args.num_beams

			if args.output_file_path:
						lowerCamelCase    		=  args.output_file_path
			else:
						lowerCamelCase    		=  """BART.onnx"""

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


if __name__ == "__main__":
		main()





from math import ceil
from typing import List, Optional, Union

import numpy as np

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


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		["audio_values", "audio_mask"]







		def __init__( self		,      A=20_48		,      A=1		,      A=[16, 16]		,      A=1_28		,      A=4_41_00		,      A=86		,      A=20_48		,      A=0.0		,      **A		,      )  -> Dict:







					'''simple docstring'''







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

					lowerCamelCase    		=  spectrogram_length
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  feature_size // self.patch_size[1]
					lowerCamelCase    		=  n_fft
					lowerCamelCase    		=  sampling_rate // hop_length_to_sampling_rate
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=A		,      min_frequency=0.0		,      max_frequency=22050.0		,      sampling_rate=A		,      norm="""slaney"""		,      mel_scale="""slaney"""		,      ).T







		def 					__A   ( self		,      A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  spectrogram(
					    A		,      window_function(self.n_fft		,      """hann"""	)		,      frame_length=self.n_fft		,      hop_length=self.hop_length		,      power=2.0		,      mel_filters=self.mel_filters.T		,      log_mel="""dB"""		,      db_range=80.0		,      )
					lowerCamelCase    		=  log_spec[:, :-1]
					lowerCamelCase    		=  log_spec - 20.0
					lowerCamelCase    		=  np.clip(log_spec / 40.0		,      -2.0		,      0.0	) + 1.0
					return log_spec







		def __call__( self		,      A		,      A = None		,      A = True		,      A = None		,      A = False		,      A = False		,      **A		,      )  -> BatchFeature:







					'''simple docstring'''







					if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
											raise ValueError(
											    """This feature extractor is set to support sampling rate"""
											    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
											    F' 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."""	)

					lowerCamelCase    		=  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}'	)
					lowerCamelCase    		=  is_batched_numpy or (
					    isinstance(A		,      (list, tuple)	) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)	))
					)
					if is_batched:
								lowerCamelCase    		=  [np.asarray([speech]		,      dtype=np.floataa	).T for speech in raw_speech]
					elif not is_batched and not isinstance(A		,      np.ndarray	):
								lowerCamelCase    		=  np.asarray(A		,      dtype=np.floataa	)
					elif isinstance(A		,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								lowerCamelCase    		=  raw_speech.astype(np.floataa	)
					# always return batch
					if not is_batched:
								lowerCamelCase    		=  [np.asarray([raw_speech]	).T]

					# Convert audio signals to log mel spectrograms, truncate by time axis
					lowerCamelCase    		=  [
					    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
					]
					if isinstance(audio_features[0]		,      A	):
								lowerCamelCase    		=  [np.asarray(A		,      dtype=np.floataa	) for feature in audio_features]

					# Create audio attention mask
					lowerCamelCase    		=  max(
					    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
					if return_attention_mask:
								lowerCamelCase    		=  [
								    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
								    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
								    for feature in audio_features
								]
								lowerCamelCase    		=  np.array(A	).astype(np.floataa	)

					# convert into correct format for padding
					lowerCamelCase    		=  max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
					lowerCamelCase    		=  np.ones([len(A	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
					lowerCamelCase    		=  padded_audio_features * self.padding_value
					for i in range(len(A	)	):
								lowerCamelCase    		=  audio_features[i]
								lowerCamelCase    		=  feature

					# return as BatchFeature
					if return_attention_mask:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
					else:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features}

					lowerCamelCase    		=  BatchFeature(data=A		,      tensor_type=A	)
					return encoded_inputs

