




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,
)

snake_case_ : int    		=       logging.getLogger(__name__)

snake_case_ : Dict    		=       {"facebook/bart-base": BartForConditionalGeneration}
snake_case_ : str    		=       {"facebook/bart-base": BartTokenizer}


def    A						()   ->      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    A						(__A   :    List[str]   ,				__A   :    Optional[Any]="cpu"       )   ->      Dict:







     """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    A						(__A   :    int   ,				__A   :    int   ,				__A   :    Optional[Any]   ,				__A   :    Any   ,				__A   :    List[str]       )   ->      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=1024   ,				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=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       )       )

          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    A						()   ->      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()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






