
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    :      Dict          =			logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE    :      str          =			{'facebook/bart-base': BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE    :      Dict          =			{'facebook/bart-base': BartTokenizer}

def 				SCREAMING_SNAKE_CASE     (      ) ->       Dict:

						"""simple docstring"""







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

						A__  :	Dict         =							parser.parse_args()

						return args

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Optional[int]="cpu"       ) ->       Union[str, Any]:

						"""simple docstring"""







						A__  :	Dict         =							model_dict[model_name].from_pretrained(_lowercase       ).to(_lowercase       )
						A__  :	Optional[Any]         =							tokenizer_dict[model_name].from_pretrained(_lowercase       )

						if model_name in ["facebook/bart-base"]:
												A__  :	Dict         =							0
												A__  :	List[str]         =							None
												A__  :	int         =							0

						return huggingface_model, tokenizer

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						Any							,						__UpperCamelCase :						int							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						str       ) ->       str:

						"""simple docstring"""







						model.eval()

						A__  :	Any         =							None
						A__  :	Dict         =							torch.jit.script(BARTBeamSearchGenerator(_lowercase       )       )

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

												A__  :	str         =							model.generate(
												    inputs['''input_ids''']							,						attention_mask=inputs['''attention_mask''']							,						num_beams=_lowercase							,						max_length=_lowercase							,						early_stopping=_lowercase							,						decoder_start_token_id=model.config.decoder_start_token_id							,						)

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

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

												A__  :	str         =							remove_dup_initializers(os.path.abspath(_lowercase       )       )

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

												A__  :	int         =							onnxruntime.InferenceSession(_lowercase       )
												A__  :	int         =							ort_sess.run(
												    _lowercase							,						{
												        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
												        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
												        '''num_beams''': np.array(_lowercase       ),
												        '''max_length''': np.array(_lowercase       ),
												        '''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 				SCREAMING_SNAKE_CASE     (      ) ->       Dict:

						"""simple docstring"""







						A__  :	Any         =							parse_args()
						A__  :	str         =							5
						A__  :	Optional[int]         =							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__  :	int         =							torch.device(args.device       )

						A__  :	List[Any]         =							load_model_tokenizer(args.model_name_or_path							,						_lowercase       )

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

						model.to(_lowercase       )

						if args.max_length:
												A__  :	Optional[Any]         =							args.max_length

						if args.num_beams:
												A__  :	Dict         =							args.num_beams

						if args.output_file_path:
												A__  :	Optional[int]         =							args.output_file_path
						else:
												A__  :	Optional[Any]         =							'''BART.onnx'''

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


if __name__ == "__main__":
					main()

import numpy as np

_SCREAMING_SNAKE_CASE    :      Any          =			[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self		):
					A__  :	List[Any]         =							np.array(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__  :	Any         =							np.where(letter == self.SQUARE		)
					A__  :	int         =							np.concatenate([indexa + 1, indexa + 1]		)
					return indexes





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							self.SQUARE[indexa - 1, indexa - 1]
					return letter





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							message.lower()
					A__  :	str         =							message.replace(''' '''				,       ''''''		)
					A__  :	Union[str, Any]         =							message.replace('''j'''				,       '''i'''		)

					A__  :	List[Any]         =							np.empty((2, len(UpperCamelCase__		))		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	Any         =							self.letter_to_numbers(message[letter_index]		)

									A__  :	Optional[Any]         =							numbers[0]
									A__  :	List[str]         =							numbers[1]

					A__  :	List[str]         =							first_step.reshape(2 * len(UpperCamelCase__		)		)
					A__  :	List[Any]         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Dict         =							int(second_step[numbers_index * 2]		)
									A__  :	List[str]         =							int(second_step[(numbers_index * 2) + 1]		)
									A__  :	Dict         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							encoded_message + letter

					return encoded_message





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							message.lower()
					message.replace(''' '''				,       ''''''		)
					A__  :	List[Any]         =							np.empty(2 * len(UpperCamelCase__		)		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	List[str]         =							self.letter_to_numbers(message[letter_index]		)
									A__  :	Dict         =							numbers[0]
									A__  :	int         =							numbers[1]

					A__  :	Optional[Any]         =							first_step.reshape((2, len(UpperCamelCase__		))		)
					A__  :	int         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Tuple         =							int(second_step[0, numbers_index]		)
									A__  :	Dict         =							int(second_step[1, numbers_index]		)
									A__  :	List[str]         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							decoded_message + letter

					return decoded_message