






"""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       :	int             = logging.getLogger(__name__)

__a       :	Dict             = {'facebook/bart-base': BartForConditionalGeneration}
__a       :	Union[str, Any]             = {'facebook/bart-base': BartTokenizer}





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

					a__          =  parser.parse_args()

					return args





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_="cpu"):
					a__          =  model_dict[model_name].from_pretrained(lowerCamelCase_).to(lowerCamelCase_)
					a__          =  tokenizer_dict[model_name].from_pretrained(lowerCamelCase_)

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

					return huggingface_model, tokenizer





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					model.eval()

					a__          =  None
					a__          =  torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_))

					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=lowerCamelCase_ ,     max_length=lowerCamelCase_ ,     early_stopping=lowerCamelCase_ ,     decoder_start_token_id=model.config.decoder_start_token_id ,     )

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

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

										a__          =  remove_dup_initializers(os.path.abspath(lowerCamelCase_))

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

										a__          =  onnxruntime.InferenceSession(lowerCamelCase_)
										a__          =  ort_sess.run(
										    lowerCamelCase_ ,     {
										        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
										        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
										        '''num_beams''': np.array(lowerCamelCase_),
										        '''max_length''': np.array(lowerCamelCase_),
										        '''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        SCREAMING_SNAKE_CASE       (      ):
					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__       ,a__          =  load_model_tokenizer(args.model_name_or_path ,     lowerCamelCase_)

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

					model.to(lowerCamelCase_)

					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(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)


if __name__ == "__main__":
	main()










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	List[str]             = logging.get_logger(__name__)

__a       :	int             = {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='vit_mae'




							def __init__(   self:     str				,		__A:     str=768				,		__A:     Union[str, Any]=12				,		__A:     str=12				,		__A:     List[Any]=3072				,		__A:     List[Any]="gelu"				,		__A:     Union[str, Any]=0.0				,		__A:     Tuple=0.0				,		__A:     str=0.0_2				,		__A:     Tuple=1e-12				,		__A:     Dict=224				,		__A:     List[Any]=16				,		__A:     Tuple=3				,		__A:     str=True				,		__A:     Optional[Any]=16				,		__A:     List[Any]=512				,		__A:     Optional[Any]=8				,		__A:     Dict=2048				,		__A:     str=0.7_5				,		__A:     str=False				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  qkv_bias
												a__          =  decoder_num_attention_heads
												a__          =  decoder_hidden_size
												a__          =  decoder_num_hidden_layers
												a__          =  decoder_intermediate_size
												a__          =  mask_ratio
												a__          =  norm_pix_loss


