






'''simple docstring'''







from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float = 1 / sqrt(2   )   ):
				a_		:	int									=   tau * frequency / samplerate
				a_		:	Optional[int]									=   sin(__A   )
				a_		:	Tuple									=   cos(__A   )
				a_		:	Any									=   _sin / (2 * q_factor)

				a_		:	List[Any]									=   (1 - _cos) / 2
				a_		:	str									=   1 - _cos

				a_		:	Any									=   1 + alpha
				a_		:	Optional[Any]									=   -2 * _cos
				a_		:	List[str]									=   1 - alpha

				a_		:	Any									=   IIRFilter(2   )
				filt.set_coefficients([aa, aa, aa]    ,		[ba, ba, ba]   )
				return filt





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float = 1 / sqrt(2   )   ):
				a_		:	Optional[int]									=   tau * frequency / samplerate
				a_		:	Dict									=   sin(__A   )
				a_		:	Dict									=   cos(__A   )
				a_		:	Optional[int]									=   _sin / (2 * q_factor)

				a_		:	Any									=   (1 + _cos) / 2
				a_		:	Optional[Any]									=   -1 - _cos

				a_		:	Optional[int]									=   1 + alpha
				a_		:	str									=   -2 * _cos
				a_		:	List[str]									=   1 - alpha

				a_		:	Optional[Any]									=   IIRFilter(2   )
				filt.set_coefficients([aa, aa, aa]    ,		[ba, ba, ba]   )
				return filt





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float = 1 / sqrt(2   )   ):
				a_		:	Optional[int]									=   tau * frequency / samplerate
				a_		:	Union[str, Any]									=   sin(__A   )
				a_		:	Optional[int]									=   cos(__A   )
				a_		:	Dict									=   _sin / (2 * q_factor)

				a_		:	Optional[int]									=   _sin / 2
				a_		:	Optional[int]									=   0
				a_		:	List[str]									=   -ba

				a_		:	List[str]									=   1 + alpha
				a_		:	Union[str, Any]									=   -2 * _cos
				a_		:	List[Any]									=   1 - alpha

				a_		:	Dict									=   IIRFilter(2   )
				filt.set_coefficients([aa, aa, aa]    ,		[ba, ba, ba]   )
				return filt





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float = 1 / sqrt(2   )   ):
				a_		:	Any									=   tau * frequency / samplerate
				a_		:	Any									=   sin(__A   )
				a_		:	Dict									=   cos(__A   )
				a_		:	List[Any]									=   _sin / (2 * q_factor)

				a_		:	Any									=   1 - alpha
				a_		:	int									=   -2 * _cos
				a_		:	Any									=   1 + alpha

				a_		:	Dict									=   IIRFilter(2   )
				filt.set_coefficients([ba, ba, ba]    ,		[ba, ba, ba]   )
				return filt





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float    ,		__A				: float = 1 / sqrt(2   )    ,		):
				a_		:	List[Any]									=   tau * frequency / samplerate
				a_		:	List[str]									=   sin(__A   )
				a_		:	Dict									=   cos(__A   )
				a_		:	Dict									=   _sin / (2 * q_factor)
				a_		:	Optional[int]									=   10 ** (gain_db / 40)

				a_		:	Optional[Any]									=   1 + alpha * big_a
				a_		:	Dict									=   -2 * _cos
				a_		:	Optional[int]									=   1 - alpha * big_a
				a_		:	Optional[int]									=   1 + alpha / big_a
				a_		:	Optional[int]									=   -2 * _cos
				a_		:	Union[str, Any]									=   1 - alpha / big_a

				a_		:	List[str]									=   IIRFilter(2   )
				filt.set_coefficients([aa, aa, aa]    ,		[ba, ba, ba]   )
				return filt





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float    ,		__A				: float = 1 / sqrt(2   )    ,		):
				a_		:	Tuple									=   tau * frequency / samplerate
				a_		:	Any									=   sin(__A   )
				a_		:	int									=   cos(__A   )
				a_		:	List[Any]									=   _sin / (2 * q_factor)
				a_		:	Any									=   10 ** (gain_db / 40)
				a_		:	Dict									=   (big_a + 1) - (big_a - 1) * _cos
				a_		:	List[str]									=   (big_a + 1) + (big_a - 1) * _cos
				a_		:	List[str]									=   (big_a - 1) - (big_a + 1) * _cos
				a_		:	List[Any]									=   (big_a - 1) + (big_a + 1) * _cos
				a_		:	Optional[Any]									=   2 * sqrt(__A   ) * alpha

				a_		:	int									=   big_a * (pmc + aaa)
				a_		:	Tuple									=   2 * big_a * mpc
				a_		:	Any									=   big_a * (pmc - aaa)
				a_		:	Tuple									=   ppmc + aaa
				a_		:	List[Any]									=   -2 * pmpc
				a_		:	Union[str, Any]									=   ppmc - aaa

				a_		:	int									=   IIRFilter(2   )
				filt.set_coefficients([aa, aa, aa]    ,		[ba, ba, ba]   )
				return filt





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: float    ,		__A				: float = 1 / sqrt(2   )    ,		):
				a_		:	str									=   tau * frequency / samplerate
				a_		:	Any									=   sin(__A   )
				a_		:	List[Any]									=   cos(__A   )
				a_		:	List[Any]									=   _sin / (2 * q_factor)
				a_		:	Optional[int]									=   10 ** (gain_db / 40)
				a_		:	Dict									=   (big_a + 1) - (big_a - 1) * _cos
				a_		:	Optional[int]									=   (big_a + 1) + (big_a - 1) * _cos
				a_		:	Union[str, Any]									=   (big_a - 1) - (big_a + 1) * _cos
				a_		:	Tuple									=   (big_a - 1) + (big_a + 1) * _cos
				a_		:	Tuple									=   2 * sqrt(__A   ) * alpha

				a_		:	List[Any]									=   big_a * (ppmc + aaa)
				a_		:	List[str]									=   -2 * big_a * pmpc
				a_		:	Tuple									=   big_a * (ppmc - aaa)
				a_		:	Optional[Any]									=   pmc + aaa
				a_		:	int									=   2 * mpc
				a_		:	Dict									=   pmc - aaa

				a_		:	Any									=   IIRFilter(2   )
				filt.set_coefficients([aa, aa, aa]    ,		[ba, ba, ba]   )
				return filt












'''simple docstring'''







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,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

__lowerCAmelCase									=   {'facebook/bart-base': BartForConditionalGeneration}
__lowerCAmelCase									=   {'facebook/bart-base': BartTokenizer}





def 		_UpperCAmelCase (				):
				a_		:	int									=   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.'''   )

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

				if model_name in ["facebook/bart-base"]:
								a_		:	str									=   0
								a_		:	Any									=   None
								a_		:	int									=   0

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

				a_		:	Union[str, Any]									=   None
				a_		:	str									=   torch.jit.script(BARTBeamSearchGenerator(__A   )   )

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

								a_		:	Dict									=   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   )   )

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   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 (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[str]									=   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_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   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:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

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


if __name__ == "__main__":
		main()




