
"""simple docstring"""



import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_PRETRAINING_MAPPING,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
           MobileBertModel,
       )




class 		a_						:







    def __init__(			self			:       Dict     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[int]=13     ,					__UpperCamelCase			:       Dict=7     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       int=True     ,					__UpperCamelCase			:       int=True     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       List[Any]=99     ,					__UpperCamelCase			:       int=64     ,					__UpperCamelCase			:       Optional[int]=32     ,					__UpperCamelCase			:       Tuple=5     ,					__UpperCamelCase			:       Tuple=4     ,					__UpperCamelCase			:       List[Any]=37     ,					__UpperCamelCase			:       str="gelu"     ,					__UpperCamelCase			:       List[Any]=0.1     ,					__UpperCamelCase			:       Optional[Any]=0.1     ,					__UpperCamelCase			:       Dict=5_12     ,					__UpperCamelCase			:       Union[str, Any]=16     ,					__UpperCamelCase			:       int=2     ,					__UpperCamelCase			:       Dict=0.0_2     ,					__UpperCamelCase			:       List[str]=3     ,					__UpperCamelCase			:       int=4     ,					__UpperCamelCase			:       List[str]=None     ,					)		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_input_mask
        _UpperCAmelCase          =   use_token_type_ids
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   embedding_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   intermediate_size
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   max_position_embeddings
        _UpperCAmelCase          =   type_vocab_size
        _UpperCAmelCase          =   type_sequence_label_size
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   num_labels
        _UpperCAmelCase          =   num_choices
        _UpperCAmelCase          =   scope







    def      _snake_case					(			self			:       Dict     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

        _UpperCAmelCase          =   None
        if self.use_input_mask:
            _UpperCAmelCase          =   random_attention_mask([self.batch_size, self.seq_length]     )

        _UpperCAmelCase          =   None
        if self.use_token_type_ids:
            _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.type_vocab_size     )

        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   None
        if self.use_labels:
            _UpperCAmelCase          =   ids_tensor([self.batch_size]     ,					self.type_sequence_label_size     )
            _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.num_labels     )
            _UpperCAmelCase          =   ids_tensor([self.batch_size]     ,					self.num_choices     )

        _UpperCAmelCase          =   self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def      _snake_case					(			self			:       Any     )		->Dict:






        '''simple docstring'''






        return MobileBertConfig(
            vocab_size=self.vocab_size     ,					hidden_size=self.hidden_size     ,					num_hidden_layers=self.num_hidden_layers     ,					num_attention_heads=self.num_attention_heads     ,					intermediate_size=self.intermediate_size     ,					embedding_size=self.embedding_size     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					max_position_embeddings=self.max_position_embeddings     ,					type_vocab_size=self.type_vocab_size     ,					is_decoder=__UpperCamelCase     ,					initializer_range=self.initializer_range     ,					)







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )

        self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertEqual(result.pooler_output.shape     ,					(self.batch_size, self.hidden_size)     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       str     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertForMaskedLM(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					labels=__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[str]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertForNextSentencePrediction(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            __UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					labels=__UpperCamelCase     ,					)
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, 2)     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Union[str, Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertForPreTraining(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            __UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					labels=__UpperCamelCase     ,					next_sentence_label=__UpperCamelCase     ,					)
        self.parent.assertEqual(result.prediction_logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)     )
        self.parent.assertEqual(result.seq_relationship_logits.shape     ,					(self.batch_size, 2)     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertForQuestionAnswering(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            __UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					start_positions=__UpperCamelCase     ,					end_positions=__UpperCamelCase     ,					)
        self.parent.assertEqual(result.start_logits.shape     ,					(self.batch_size, self.seq_length)     )
        self.parent.assertEqual(result.end_logits.shape     ,					(self.batch_size, self.seq_length)     )







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Tuple     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_labels
        _UpperCAmelCase          =   MobileBertForSequenceClassification(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					labels=__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)     )







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Tuple     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_labels
        _UpperCAmelCase          =   MobileBertForTokenClassification(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					labels=__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.num_labels)     )







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Any     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_choices
        _UpperCAmelCase          =   MobileBertForMultipleChoice(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   input_ids.unsqueeze(1     ).expand(-1     ,					self.num_choices     ,					-1     ).contiguous()
        _UpperCAmelCase          =   token_type_ids.unsqueeze(1     ).expand(-1     ,					self.num_choices     ,					-1     ).contiguous()
        _UpperCAmelCase          =   input_mask.unsqueeze(1     ).expand(-1     ,					self.num_choices     ,					-1     ).contiguous()
        _UpperCAmelCase          =   model(
            __UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					token_type_ids=__UpperCamelCase     ,					labels=__UpperCamelCase     ,					)
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)     )







    def      _snake_case					(			self			:       int     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )    ,(
            _UpperCAmelCase
        )    ,(
            _UpperCAmelCase
        )    ,(
            _UpperCAmelCase
        )    ,(
            _UpperCAmelCase
        )    ,(
            _UpperCAmelCase
        )    ,(
            _UpperCAmelCase
        )    ,
        )          =   config_and_inputs
        _UpperCAmelCase          =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Optional[int]     =				(
        (
            MobileBertModel,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a      :				Any     =				(
        {
            'feature-extraction': MobileBertModel,
            'fill-mask': MobileBertForMaskedLM,
            'question-answering': MobileBertForQuestionAnswering,
            'text-classification': MobileBertForSequenceClassification,
            'token-classification': MobileBertForTokenClassification,
            'zero-shot': MobileBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a      :				Optional[int]     =				True







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Union[str, Any]=False     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   super()._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     ,					return_labels=__UpperCamelCase     )

        if return_labels:
            if model_class in get_values(__UpperCamelCase     ):
                _UpperCAmelCase          =   torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length)     ,					dtype=torch.long     ,					device=__UpperCamelCase     )
                _UpperCAmelCase          =   torch.zeros(
                    self.model_tester.batch_size     ,					dtype=torch.long     ,					device=__UpperCamelCase     )
        return inputs_dict







    def      _snake_case					(			self			:       Tuple     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					hidden_size=37     )







    def      _snake_case					(			self			:       List[str]     )		->Union[str, Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def      _snake_case					(			self			:       Dict     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_model(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_masked_lm(*__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_pretraining(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_question_answering(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_token_classification(*__UpperCamelCase     )







def  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    return torch.tensor(
        _A					,      dtype=torch.long					,      device=_A					,      )


a					:	str							=		1E-3







@require_torch
@require_sentencepiece
@require_tokenizers
class 		a_						( unittest.TestCase	):







    @slow
    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   MobileBertModel.from_pretrained("""google/mobilebert-uncased"""     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]     )
        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     )[0]
        _UpperCAmelCase          =   torch.Size((1, 9, 5_12)     )
        self.assertEqual(output.shape     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor(
            [
                [
                    [-2.4736526e07, 8.2691656e04, 1.6521838e05],
                    [-5.7541704e-01, 3.9056022e00, 4.4011507e00],
                    [2.6047359e00, 1.5677652e00, -1.7324188e-01],
                ]
            ]     ,					device=__UpperCamelCase     ,					)

        # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
        # ~1 difference, it's therefore not a good idea to measure using addition.
        # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
        # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
        _UpperCAmelCase          =   torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE     )
        _UpperCAmelCase          =   torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE     )

        self.assertTrue(lower_bound and upper_bound     )

"""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,
)

a					:	List[str]							=		logging.getLogger(__name__)

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   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."""      )

    _UpperCAmelCase          =   parser.parse_args()

    return args


def  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   model_dict[model_name].from_pretrained(_A      ).to(_A      )
    _UpperCAmelCase          =   tokenizer_dict[model_name].from_pretrained(_A      )

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

    return huggingface_model, tokenizer


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    model.eval()

    _UpperCAmelCase          =   None
    _UpperCAmelCase          =   torch.jit.script(BARTBeamSearchGenerator(_A      )      )

    with torch.no_grad():
        _UpperCAmelCase          =   """My friends are cool but they eat too many carbs."""
        _UpperCAmelCase          =   tokenizer([ARTICLE_TO_SUMMARIZE]					,      max_length=1_0_2_4					,      return_tensors="""pt"""      ).to(model.device      )

        _UpperCAmelCase          =   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=1_4					,      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      )      )

        _UpperCAmelCase          =   remove_dup_initializers(os.path.abspath(_A      )      )

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

        _UpperCAmelCase          =   onnxruntime.InferenceSession(_A      )
        _UpperCAmelCase          =   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	(						)    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   parse_args()
    _UpperCAmelCase          =   5
    _UpperCAmelCase          =   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()

    _UpperCAmelCase          =   torch.device(args.device      )

    _UpperCAmelCase    ,_UpperCAmelCase          =   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:
        _UpperCAmelCase          =   args.max_length

    if args.num_beams:
        _UpperCAmelCase          =   args.num_beams

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

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


if __name__ == "__main__":
 main()