
"""simple docstring"""



import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
           VideoMAEForPreTraining,
           VideoMAEForVideoClassification,
           VideoMAEModel,
       )
       from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VideoMAEImageProcessor




class 		a_						:







    def __init__(			self			:       str     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any=13     ,					__UpperCamelCase			:       int=10     ,					__UpperCamelCase			:       int=3     ,					__UpperCamelCase			:       List[str]=2     ,					__UpperCamelCase			:       Tuple=2     ,					__UpperCamelCase			:       Optional[Any]=2     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Tuple=32     ,					__UpperCamelCase			:       Optional[int]=5     ,					__UpperCamelCase			:       Any=4     ,					__UpperCamelCase			:       Union[str, Any]=37     ,					__UpperCamelCase			:       Dict="gelu"     ,					__UpperCamelCase			:       List[str]=0.1     ,					__UpperCamelCase			:       Optional[Any]=0.1     ,					__UpperCamelCase			:       Union[str, Any]=10     ,					__UpperCamelCase			:       Dict=0.0_2     ,					__UpperCamelCase			:       Optional[Any]=0.9     ,					__UpperCamelCase			:       int=None     ,					)		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   image_size
        _UpperCAmelCase          =   num_channels
        _UpperCAmelCase          =   patch_size
        _UpperCAmelCase          =   tubelet_size
        _UpperCAmelCase          =   num_frames
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   hidden_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          =   type_sequence_label_size
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   mask_ratio
        _UpperCAmelCase          =   scope

        # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
        _UpperCAmelCase          =   (image_size // patch_size) ** 2
        _UpperCAmelCase          =   (num_frames // tubelet_size) * self.num_patches_per_frame

        # use this variable to define bool_masked_pos
        _UpperCAmelCase          =   int(mask_ratio * self.seq_length     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

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

        _UpperCAmelCase          =   self.get_config()

        return config, pixel_values, labels







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






        '''simple docstring'''






        return VideoMAEConfig(
            image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					num_frames=self.num_frames     ,					tubelet_size=self.tubelet_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     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					is_decoder=A__     ,					initializer_range=self.initializer_range     ,					)







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






        '''simple docstring'''






        _UpperCAmelCase          =   VideoMAEModel(config=A__     )
        model.to(A__     )
        model.eval()
        _UpperCAmelCase          =   model(A__     )
        self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   VideoMAEForPreTraining(A__     )
        model.to(A__     )
        model.eval()
        # important: each video needs to have the same number of masked patches
        # hence we define a single mask, which we then repeat for each example in the batch
        _UpperCAmelCase          =   torch.ones((self.num_masks,)     )
        _UpperCAmelCase          =   torch.cat([mask, torch.zeros(self.seq_length - mask.size(0     )     )]     )
        _UpperCAmelCase          =   mask.expand(self.batch_size     ,					-1     ).bool()

        _UpperCAmelCase          =   model(A__     ,					A__     )
        # model only returns predictions for masked patches
        _UpperCAmelCase          =   mask.sum().item()
        _UpperCAmelCase          =   3 * self.tubelet_size * self.patch_size**2
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, num_masked_patches, decoder_num_labels)     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.prepare_config_and_inputs()
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   config_and_inputs
        _UpperCAmelCase          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_						( _lowerCamelCase     ,						_lowerCamelCase     ,						unittest.TestCase	):
    a      :				Optional[Any]     =				(
        (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
    )
    a      :				Optional[int]     =				(
        {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
        if is_torch_available()
        else {}
    )

    a      :				Any     =				False
    a      :				Dict     =				False
    a      :				Optional[int]     =				False
    a      :				Any     =				False







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






        '''simple docstring'''






        _UpperCAmelCase          =   VideoMAEModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=A__     ,					has_text_modality=A__     ,					hidden_size=37     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   copy.deepcopy(A__     )

        if model_class == VideoMAEForPreTraining:
            # important: each video needs to have the same number of masked patches
            # hence we define a single mask, which we then repeat for each example in the batch
            _UpperCAmelCase          =   torch.ones((self.model_tester.num_masks,)     )
            _UpperCAmelCase          =   torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0     )     )]     )
            _UpperCAmelCase          =   mask.expand(self.model_tester.batch_size     ,					-1     ).bool()
            _UpperCAmelCase          =   bool_masked_pos.to(A__     )

        if return_labels:
            if model_class in [
                *get_values(A__     ),
            ]:
                _UpperCAmelCase          =   torch.zeros(
                    self.model_tester.batch_size     ,					dtype=torch.long     ,					device=A__     )

        return inputs_dict







    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    @unittest.skip(reason="""VideoMAE does not use inputs_embeds"""     )
    def      _snake_case					(			self			:       List[str]     )		->int:






        '''simple docstring'''






        pass







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase          =   model_class(A__     )
            self.assertIsInstance(model.get_input_embeddings()     ,					(nn.Module)     )
            _UpperCAmelCase          =   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A__     ,					nn.Linear     )     )







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase          =   model_class(A__     )
            _UpperCAmelCase          =   inspect.signature(model.forward     )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _UpperCAmelCase          =   [*signature.parameters.keys()]

            _UpperCAmelCase          =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     ,					A__     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A__     )







    def      _snake_case					(			self			:       str     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*A__     )







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






        '''simple docstring'''






        for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _UpperCAmelCase          =   VideoMAEModel.from_pretrained(A__     )
            self.assertIsNotNone(A__     )







    def      _snake_case					(			self			:       Tuple     )		->int:






        '''simple docstring'''






        if not self.has_attentions:
            pass

        else:
            _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase          =   True

            for model_class in self.all_model_classes:
                _UpperCAmelCase          =   self.model_tester.seq_length - self.model_tester.num_masks
                _UpperCAmelCase          =   (
                    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                )

                _UpperCAmelCase          =   True
                _UpperCAmelCase          =   False
                _UpperCAmelCase          =   True
                _UpperCAmelCase          =   model_class(A__     )
                model.to(A__     )
                model.eval()
                with torch.no_grad():
                    _UpperCAmelCase          =   model(**self._prepare_for_class(A__     ,					A__     )     )
                _UpperCAmelCase          =   outputs.attentions
                self.assertEqual(len(A__     )     ,					self.model_tester.num_hidden_layers     )

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                _UpperCAmelCase          =   True
                _UpperCAmelCase          =   model_class(A__     )
                model.to(A__     )
                model.eval()
                with torch.no_grad():
                    _UpperCAmelCase          =   model(**self._prepare_for_class(A__     ,					A__     )     )
                _UpperCAmelCase          =   outputs.attentions
                self.assertEqual(len(A__     )     ,					self.model_tester.num_hidden_layers     )

                self.assertListEqual(
                    list(attentions[0].shape[-3:]     )     ,					[self.model_tester.num_attention_heads, seq_len, seq_len]     ,					)
                _UpperCAmelCase          =   len(A__     )

                # Check attention is always last and order is fine
                _UpperCAmelCase          =   True
                _UpperCAmelCase          =   True
                _UpperCAmelCase          =   model_class(A__     )
                model.to(A__     )
                model.eval()
                with torch.no_grad():
                    _UpperCAmelCase          =   model(**self._prepare_for_class(A__     ,					A__     )     )

                self.assertEqual(out_len + 1     ,					len(A__     )     )

                _UpperCAmelCase          =   outputs.attentions

                self.assertEqual(len(A__     )     ,					self.model_tester.num_hidden_layers     )
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]     )     ,					[self.model_tester.num_attention_heads, seq_len, seq_len]     ,					)







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        def check_hidden_states_output(__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     ):
            _UpperCAmelCase          =   model_class(A__     )
            model.to(A__     )
            model.eval()

            with torch.no_grad():
                _UpperCAmelCase          =   model(**self._prepare_for_class(A__     ,					A__     )     )

            _UpperCAmelCase          =   outputs.hidden_states
            _UpperCAmelCase          =   self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(A__     )     ,					A__     )

            _UpperCAmelCase          =   self.model_tester.seq_length - self.model_tester.num_masks
            _UpperCAmelCase          =   num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]     )     ,					[seq_length, self.model_tester.hidden_size]     ,					)

        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase          =   True
            check_hidden_states_output(A__     ,					A__     ,					A__     )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _UpperCAmelCase          =   True

            check_hidden_states_output(A__     ,					A__     ,					A__     )







    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
    def      _snake_case					(			self			:       Any     )		->List[Any]:






        '''simple docstring'''






        pass







def  _UpperCamelCase	(						)    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   hf_hub_download(
        repo_id="""hf-internal-testing/spaghetti-video"""					,      filename="""eating_spaghetti.npy"""					,      repo_type="""dataset"""      )
    _UpperCAmelCase          =   np.load(SCREAMING_SNAKE_CASE_      )
    return list(SCREAMING_SNAKE_CASE_      )







@require_torch
@require_vision
class 		a_						( unittest.TestCase	):







    @cached_property
    def      _snake_case					(			self			:       List[Any]     )		->str:






        '''simple docstring'''






        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,					image_std=[0.5, 0.5, 0.5]     )
            if is_vision_available()
            else None
        )







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






        '''simple docstring'''






        _UpperCAmelCase          =   VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics"""     ).to(
            A__     )

        _UpperCAmelCase          =   self.default_image_processor
        _UpperCAmelCase          =   prepare_video()
        _UpperCAmelCase          =   image_processor(A__     ,					return_tensors="""pt"""     ).to(A__     )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase          =   model(**A__     )

        # verify the logits
        _UpperCAmelCase          =   torch.Size((1, 4_00)     )
        self.assertEqual(outputs.logits.shape     ,					A__     )

        _UpperCAmelCase          =   torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]     ).to(A__     )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					A__     ,					atol=1e-4     )     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""     ).to(A__     )

        _UpperCAmelCase          =   self.default_image_processor
        _UpperCAmelCase          =   prepare_video()
        _UpperCAmelCase          =   image_processor(A__     ,					return_tensors="""pt"""     ).to(A__     )

        # add boolean mask, indicating which patches to mask
        _UpperCAmelCase          =   hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""     ,					filename="""bool_masked_pos.pt"""     )
        _UpperCAmelCase          =   torch.load(A__     )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase          =   model(**A__     )

        # verify the logits
        _UpperCAmelCase          =   torch.Size([1, 14_08, 15_36]     )
        _UpperCAmelCase          =   torch.tensor(
            [[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]     ,					device=A__     )
        self.assertEqual(outputs.logits.shape     ,					A__     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,					A__     ,					atol=1e-4     )     )

        # verify the loss (`config.norm_pix_loss` = `True`)
        _UpperCAmelCase          =   torch.tensor([0.5_1_4_2]     ,					device=A__     )
        self.assertTrue(torch.allclose(outputs.loss     ,					A__     ,					atol=1e-4     )     )

        # verify the loss (`config.norm_pix_loss` = `False`)
        _UpperCAmelCase          =   VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""     ,					norm_pix_loss=A__     ).to(
            A__     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(**A__     )

        _UpperCAmelCase          =   torch.tensor(torch.tensor([0.6_4_6_9]     )     ,					device=A__     )
        self.assertTrue(torch.allclose(outputs.loss     ,					A__     ,					atol=1e-4     )     )

"""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()