
"""simple docstring"""



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


a					:	str							=		logging.get_logger(__name__)

a					:	Dict							=		{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}




class 		a_						( _UpperCAmelCase	):
    a      :				Dict     =				'instructblip_vision_model'







    def __init__(			self			:       Any     ,					__UpperCamelCase			:       Any=14_08     ,					__UpperCamelCase			:       Optional[int]=61_44     ,					__UpperCamelCase			:       List[str]=39     ,					__UpperCamelCase			:       Union[str, Any]=16     ,					__UpperCamelCase			:       int=2_24     ,					__UpperCamelCase			:       int=14     ,					__UpperCamelCase			:       List[Any]="gelu"     ,					__UpperCamelCase			:       Any=1e-6     ,					__UpperCamelCase			:       Tuple=0.0     ,					__UpperCamelCase			:       Union[str, Any]=1e-10     ,					__UpperCamelCase			:       Dict=True     ,					**__UpperCamelCase			:       Union[str, Any]     ,					)		->int:






        '''simple docstring'''






        super().__init__(**__UpperCamelCase     )

        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   intermediate_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   patch_size
        _UpperCAmelCase          =   image_size
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   attention_dropout
        _UpperCAmelCase          =   layer_norm_eps
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   qkv_bias







    @classmethod
    def      _snake_case					(			cls			:       Dict     ,					__UpperCamelCase			:       Union[str, os.PathLike]     ,					**__UpperCamelCase			:       str     )		->"PretrainedConfig":






        '''simple docstring'''






        cls._set_token_in_kwargs(__UpperCamelCase     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   cls.get_config_dict(__UpperCamelCase     ,					**__UpperCamelCase     )

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""     ) == "instructblip":
            _UpperCAmelCase          =   config_dict["""vision_config"""]

        if "model_type" in config_dict and hasattr(cls     ,					"""model_type"""     ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

        return cls.from_dict(__UpperCamelCase     ,					**__UpperCamelCase     )







class 		a_						( _UpperCAmelCase	):
    a      :				List[str]     =				'instructblip_qformer'







    def __init__(			self			:       List[Any]     ,					__UpperCamelCase			:       List[str]=3_05_22     ,					__UpperCamelCase			:       Optional[Any]=7_68     ,					__UpperCamelCase			:       Optional[int]=12     ,					__UpperCamelCase			:       Any=12     ,					__UpperCamelCase			:       Union[str, Any]=30_72     ,					__UpperCamelCase			:       Optional[Any]="gelu"     ,					__UpperCamelCase			:       str=0.1     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       List[Any]=5_12     ,					__UpperCamelCase			:       List[Any]=0.0_2     ,					__UpperCamelCase			:       Tuple=1e-12     ,					__UpperCamelCase			:       Union[str, Any]=0     ,					__UpperCamelCase			:       Optional[int]="absolute"     ,					__UpperCamelCase			:       Dict=2     ,					__UpperCamelCase			:       Any=14_08     ,					**__UpperCamelCase			:       str     ,					)		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase     ,					**__UpperCamelCase     )

        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   intermediate_size
        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   max_position_embeddings
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   layer_norm_eps
        _UpperCAmelCase          =   position_embedding_type
        _UpperCAmelCase          =   cross_attention_frequency
        _UpperCAmelCase          =   encoder_hidden_size







    @classmethod
    def      _snake_case					(			cls			:       Tuple     ,					__UpperCamelCase			:       Union[str, os.PathLike]     ,					**__UpperCamelCase			:       List[Any]     )		->"PretrainedConfig":






        '''simple docstring'''






        cls._set_token_in_kwargs(__UpperCamelCase     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   cls.get_config_dict(__UpperCamelCase     ,					**__UpperCamelCase     )

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""     ) == "instructblip":
            _UpperCAmelCase          =   config_dict["""qformer_config"""]

        if "model_type" in config_dict and hasattr(cls     ,					"""model_type"""     ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

        return cls.from_dict(__UpperCamelCase     ,					**__UpperCamelCase     )







class 		a_						( _UpperCAmelCase	):
    a      :				List[str]     =				'instructblip'
    a      :				Any     =				True







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Optional[Any]=32     ,					**__UpperCamelCase			:       List[str]     )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(**__UpperCamelCase     )

        if vision_config is None:
            _UpperCAmelCase          =   {}
            logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""     )

        if qformer_config is None:
            _UpperCAmelCase          =   {}
            logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""     )

        if text_config is None:
            _UpperCAmelCase          =   {}
            logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""     )

        _UpperCAmelCase          =   InstructBlipVisionConfig(**__UpperCamelCase     )
        _UpperCAmelCase          =   InstructBlipQFormerConfig(**__UpperCamelCase     )
        _UpperCAmelCase          =   text_config["""model_type"""] if """model_type""" in text_config else """opt"""
        _UpperCAmelCase          =   CONFIG_MAPPING[text_model_type](**__UpperCamelCase     )

        _UpperCAmelCase          =   self.text_config.tie_word_embeddings
        _UpperCAmelCase          =   self.text_config.is_encoder_decoder

        _UpperCAmelCase          =   num_query_tokens
        _UpperCAmelCase          =   self.vision_config.hidden_size
        _UpperCAmelCase          =   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        _UpperCAmelCase          =   1.0
        _UpperCAmelCase          =   0.0_2







    @classmethod
    def      _snake_case					(			cls			:       Optional[Any]     ,					__UpperCamelCase			:       InstructBlipVisionConfig     ,					__UpperCamelCase			:       InstructBlipQFormerConfig     ,					__UpperCamelCase			:       PretrainedConfig     ,					**__UpperCamelCase			:       int     ,					)		->Dict:






        '''simple docstring'''






        return cls(
            vision_config=vision_config.to_dict()     ,					qformer_config=qformer_config.to_dict()     ,					text_config=text_config.to_dict()     ,					**__UpperCamelCase     ,					)







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






        '''simple docstring'''






        _UpperCAmelCase          =   copy.deepcopy(self.__dict__     )
        _UpperCAmelCase          =   self.vision_config.to_dict()
        _UpperCAmelCase          =   self.qformer_config.to_dict()
        _UpperCAmelCase          =   self.text_config.to_dict()
        _UpperCAmelCase          =   self.__class__.model_type
        return output

"""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()