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_:   Optional[int]   =      logging.getLogger(__name__)

A_:   Tuple   =      {'facebook/bart-base': BartForConditionalGeneration}
A_:   Any   =      {'facebook/bart-base': BartTokenizer}


def        __lowerCAmelCase      (	):



							"""simple docstring"""


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

							_lowercase					=						parser.parse_args()

							return args


def        __lowerCAmelCase      (	_A			,_A="cpu"       ):



							"""simple docstring"""


							_lowercase					=						model_dict[model_name].from_pretrained(__UpperCAmelCase       ).to(__UpperCAmelCase       )
							_lowercase					=						tokenizer_dict[model_name].from_pretrained(__UpperCAmelCase       )

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

							return huggingface_model, tokenizer


def        __lowerCAmelCase      (	_A			,_A			,_A			,_A			,_A       ):



							"""simple docstring"""


							model.eval()

							_lowercase					=						None
							_lowercase					=						torch.jit.script(BARTBeamSearchGenerator(__UpperCAmelCase       )       )

							with torch.no_grad():
														_lowercase					=						"""My friends are cool but they eat too many carbs."""
														_lowercase					=						tokenizer([ARTICLE_TO_SUMMARIZE]			,max_length=1_024			,return_tensors="""pt"""       ).to(model.device       )

														_lowercase					=						model.generate(
														    inputs["""input_ids"""]			,attention_mask=inputs["""attention_mask"""]			,num_beams=__UpperCAmelCase			,max_length=__UpperCAmelCase			,early_stopping=__UpperCAmelCase			,decoder_start_token_id=model.config.decoder_start_token_id			,)

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

														logger.info("""Model exported to {}""".format(__UpperCAmelCase       )       )

														_lowercase					=						remove_dup_initializers(os.path.abspath(__UpperCAmelCase       )       )

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

														_lowercase					=						onnxruntime.InferenceSession(__UpperCAmelCase       )
														_lowercase					=						ort_sess.run(
														    __UpperCAmelCase			,{
														        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
														        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
														        """num_beams""": np.array(__UpperCAmelCase       ),
														        """max_length""": np.array(__UpperCAmelCase       ),
														        """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        __lowerCAmelCase      (	):



							"""simple docstring"""


							_lowercase					=						parse_args()
							_lowercase					=						5
							_lowercase					=						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()

							_lowercase					=						torch.device(args.device       )

							_lowercase					,							_lowercase					=						load_model_tokenizer(args.model_name_or_path			,__UpperCAmelCase       )

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

							model.to(__UpperCAmelCase       )

							if args.max_length:
														_lowercase					=						args.max_length

							if args.num_beams:
														_lowercase					=						args.num_beams

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

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


if __name__ == "__main__":
				main()








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a    :							Tuple										=    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							a    :							List[Any]										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

