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,
)

lowerCamelCase_							:						Union[str, Any]					=			logging.getLogger(__name__)

lowerCamelCase_							:						List[str]					=			{"""facebook/bart-base""": BartForConditionalGeneration}
lowerCamelCase_							:						List[Any]					=			{"""facebook/bart-base""": BartTokenizer}
def 				lowerCAmelCase(      ):
					__a						   =  argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'		)
					parser.add_argument(
					    '--validation_file' ,							type=lowercase_ ,							default=lowercase_ ,							help='A csv or a json file containing the validation data.'		)
					parser.add_argument(
					    '--max_length' ,							type=lowercase_ ,							default=5 ,							help='The maximum total input sequence length after tokenization.' ,							)
					parser.add_argument(
					    '--num_beams' ,							type=lowercase_ ,							default=lowercase_ ,							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=lowercase_ ,							help='Path to pretrained model or model identifier from huggingface.co/models.' ,							required=lowercase_ ,							)
					parser.add_argument(
					    '--config_name' ,							type=lowercase_ ,							default=lowercase_ ,							help='Pretrained config name or path if not the same as model_name' ,							)
					parser.add_argument(
					    '--device' ,							type=lowercase_ ,							default='cpu' ,							help='Device where the model will be run' ,							)
					parser.add_argument('--output_file_path' ,							type=lowercase_ ,							default=lowercase_ ,							help='Where to store the final ONNX file.'		)

					__a						   =  parser.parse_args()

					return args
def 				lowerCAmelCase(      __lowerCamelCase ,							__lowerCamelCase="cpu"		):
					__a						   =  model_dict[model_name].from_pretrained(lowercase_		).to(lowercase_		)
					__a						   =  tokenizer_dict[model_name].from_pretrained(lowercase_		)

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

					return huggingface_model, tokenizer
def 				lowerCAmelCase(      __lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase		):
					model.eval()

					__a						   =  None
					__a						   =  torch.jit.script(BARTBeamSearchGenerator(lowercase_		)		)

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

										__a						   =  model.generate(
										    inputs['input_ids'] ,							attention_mask=inputs['attention_mask'] ,							num_beams=lowercase_ ,							max_length=lowercase_ ,							early_stopping=lowercase_ ,							decoder_start_token_id=model.config.decoder_start_token_id ,							)

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

										logger.info('Model exported to {}'.format(lowercase_		)		)

										__a						   =  remove_dup_initializers(os.path.abspath(lowercase_		)		)

										logger.info('Deduplicated and optimized model written to {}'.format(lowercase_		)		)

										__a						   =  onnxruntime.InferenceSession(lowercase_		)
										__a						   =  ort_sess.run(
										    lowercase_ ,							{
										        'input_ids': inputs['input_ids'].cpu().numpy(),
										        'attention_mask': inputs['attention_mask'].cpu().numpy(),
										        'num_beams': np.array(lowercase_		),
										        'max_length': np.array(lowercase_		),
										        '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(      ):
					__a						   =  parse_args()
					__a						   =  5
					__a						   =  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()

					__a						   =  torch.device(args.device		)

					__a						   =  load_model_tokenizer(args.model_name_or_path ,							lowercase_		)

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

					model.to(lowercase_		)

					if args.max_length:
										__a						   =  args.max_length

					if args.num_beams:
										__a						   =  args.num_beams

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

					logger.info('Exporting model to ONNX'		)
					export_and_validate_model(lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_		)


if __name__ == "__main__":
	main()











"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__						= logging.get_logger(__name__)


if is_vision_available():
  import PIL



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       ["pixel_values"]

  def __init__(		self :							Tuple       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							bool = True       , __a :							Union[int, float] = 1 / 255       , __a :							bool = True       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = True       , **__a :							List[Any]       , )		->					None:
        super().__init__(**__a						)
        _UpperCamelCase   :						Optional[int]    =							size if size is not None else {"shortest_edge": 224}
        _UpperCamelCase   :						Optional[int]    =							get_size_dict(__a       , default_to_square=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase   :						Tuple    =							get_size_dict(__a       , default_to_square=__a       , param_name="crop_size"						)

        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						Dict    =							size
        _UpperCamelCase   :						Any    =							resample
        _UpperCamelCase   :						Tuple    =							do_center_crop
        _UpperCamelCase   :						str    =							crop_size
        _UpperCamelCase   :						Any    =							do_rescale
        _UpperCamelCase   :						Dict    =							rescale_factor
        _UpperCamelCase   :						int    =							do_normalize
        _UpperCamelCase   :						Dict    =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCamelCase   :						Tuple    =							image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCamelCase   :						List[Any]    =							do_convert_rgb

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							str       , )		->					np.ndarray:
        _UpperCamelCase   :						Any    =							get_size_dict(__a       , default_to_square=__a						)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
        _UpperCamelCase   :						Dict    =							get_resize_output_image_size(__a       , size=size["shortest_edge"]       , default_to_square=__a						)
        return resize(__a       , size=__a       , resample=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        _UpperCamelCase   :						List[str]    =							get_size_dict(__a						)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
        return center_crop(__a       , size=(size["height"], size["width"])       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							np.ndarray       , __a :							Union[int, float]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Optional[Any]       , )		->					List[str]:
        return rescale(__a       , scale=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							np.ndarray       , __a :							Union[float, List[float]]       , __a :							Union[float, List[float]]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        return normalize(__a       , mean=__a       , std=__a       , data_format=__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							ImageInput       , __a :							bool = None       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = None       , __a :							bool = None       , __a :							int = None       , __a :							bool = None       , __a :							float = None       , __a :							bool = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = None       , __a :							Optional[Union[str, TensorType]] = None       , __a :							Optional[ChannelDimension] = ChannelDimension.FIRST       , **__a :							Union[str, Any]       , )		->					PIL.Image.Image:
        _UpperCamelCase   :						Tuple    =							do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase   :						int    =							size if size is not None else self.size
        _UpperCamelCase   :						int    =							get_size_dict(__a       , param_name="size"       , default_to_square=__a						)
        _UpperCamelCase   :						List[Any]    =							resample if resample is not None else self.resample
        _UpperCamelCase   :						List[Any]    =							do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase   :						List[Any]    =							get_size_dict(__a       , param_name="crop_size"       , default_to_square=__a						)
        _UpperCamelCase   :						Dict    =							do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase   :						Union[str, Any]    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase   :						Union[str, Any]    =							do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase   :						List[Any]    =							image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase   :						List[Any]    =							image_std if image_std is not None else self.image_std
        _UpperCamelCase   :						List[str]    =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase   :						Any    =							make_list_of_images(__a						)

        if not valid_images(__a						):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."						)

        if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              _UpperCamelCase   :						int    =							[convert_to_rgb(__a						) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase   :						List[Any]    =							[to_numpy_array(__a						) for image in images]

        if do_resize:
              _UpperCamelCase   :						Union[str, Any]    =							[self.resize(image=__a       , size=__a       , resample=__a						) for image in images]

        if do_center_crop:
              _UpperCamelCase   :						Optional[Any]    =							[self.center_crop(image=__a       , size=__a						) for image in images]

        if do_rescale:
              _UpperCamelCase   :						Dict    =							[self.rescale(image=__a       , scale=__a						) for image in images]

        if do_normalize:
              _UpperCamelCase   :						List[Any]    =							[self.normalize(image=__a       , mean=__a       , std=__a						) for image in images]

        _UpperCamelCase   :						List[Any]    =							[to_channel_dimension_format(__a       , __a						) for image in images]

        _UpperCamelCase   :						str    =							{"pixel_values": images}
        return BatchFeature(data=__a       , tensor_type=__a						)
