

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,
)

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

__lowercase     : Optional[Any]       =						{'''facebook/bart-base''': BartForConditionalGeneration}
__lowercase     : str       =						{'''facebook/bart-base''': BartTokenizer}







def       lowercase					(	)				->			int:





    '''simple docstring'''




    snake_case							:		List[str]        =		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."""		)

    snake_case							:		str        =		parser.parse_args()

    return args







def       lowercase					(	__A :					int ,    __A :					Union[str, Any]="cpu"		)				->			Tuple:





    '''simple docstring'''




    snake_case							:		Any        =		model_dict[model_name].from_pretrained(__A		).to(__A		)
    snake_case							:		Any        =		tokenizer_dict[model_name].from_pretrained(__A		)

    if model_name in ["facebook/bart-base"]:
        snake_case							:		Tuple        =		0
        snake_case							:		List[str]        =		None
        snake_case							:		List[Any]        =		0

    return huggingface_model, tokenizer







def       lowercase					(	__A :					Optional[int] ,    __A :					List[Any] ,    __A :					List[Any] ,    __A :					Union[str, Any] ,    __A :					Tuple		)				->			Tuple:





    '''simple docstring'''




    model.eval()

    snake_case							:		Tuple        =		None
    snake_case							:		Any        =		torch.jit.script(BARTBeamSearchGenerator(__A		)		)

    with torch.no_grad():
        snake_case							:		str        =		"""My friends are cool but they eat too many carbs."""
        snake_case							:		Optional[Any]        =		tokenizer([ARTICLE_TO_SUMMARIZE] ,    max_length=1024 ,    return_tensors="""pt"""		).to(model.device		)

        snake_case							:		Optional[int]        =		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		)		)

        snake_case							:		Any        =		remove_dup_initializers(os.path.abspath(__A		)		)

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

        snake_case							:		Union[str, Any]        =		onnxruntime.InferenceSession(__A		)
        snake_case							:		Optional[int]        =		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       lowercase					(	)				->			Optional[Any]:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		parse_args()
    snake_case							:		str        =		5
    snake_case							:		Union[str, Any]        =		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()

    snake_case							:		Union[str, Any]        =		torch.device(args.device		)

    snake_case      ,		snake_case							:		Dict        =		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:
        snake_case							:		Union[str, Any]        =		args.max_length

    if args.num_beams:
        snake_case							:		Any        =		args.num_beams

    if args.output_file_path:
        snake_case							:		Optional[int]        =		args.output_file_path
    else:
        snake_case							:		str        =		"""BART.onnx"""

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


if __name__ == "__main__":
     main()







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						lowerCAmelCase__ = 0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if images is None and text is None:
													raise ValueError('You have to specify either images or text.'       )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
													return text_encoding

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					text_encoding.pop('attention_mask'       )
													if "input_ids" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					text_encoding.pop('input_ids'       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

										if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__       )

										return encoding_image_processor






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





