


'''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,
)

__UpperCAmelCase					       =							logging.getLogger(__name__)

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


def       _snake_case     (  )		->					str:
							lowerCAmelCase__      =			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.''' )

							lowerCAmelCase__      =			parser.parse_args()

							return args


def       _snake_case     (  A				,					A="cpu" )		->					Optional[Any]:
							lowerCAmelCase__      =			model_dict[model_name].from_pretrained(A ).to(A )
							lowerCAmelCase__      =			tokenizer_dict[model_name].from_pretrained(A )

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

							return huggingface_model, tokenizer


def       _snake_case     (  A				,					A				,					A				,					A				,					A )		->					Tuple:
							model.eval()

							lowerCAmelCase__      =			None
							lowerCAmelCase__      =			torch.jit.script(BARTBeamSearchGenerator(A ) )

							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=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 ) )

														lowerCAmelCase__      =			remove_dup_initializers(os.path.abspath(A ) )

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

														lowerCAmelCase__      =			onnxruntime.InferenceSession(A )
														lowerCAmelCase__      =			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       _snake_case     (  )		->					Optional[Any]:
							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				,					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:
														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(A				,					A				,					A				,					A				,					A )


if __name__ == "__main__":
		main()



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig