






"""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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a       :	str             = logging.get_logger(__name__)

__a       :	Dict             = {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='xlm'
							_SCREAMING_SNAKE_CASE    ={
							    'hidden_size': 'emb_dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							    'n_words': 'vocab_size',  # For backward compatibility
							}




							def __init__(   self:     Optional[Any]				,		__A:     str=30145				,		__A:     Union[str, Any]=2048				,		__A:     Union[str, Any]=12				,		__A:     List[Any]=16				,		__A:     List[Any]=0.1				,		__A:     int=0.1				,		__A:     str=True				,		__A:     int=False				,		__A:     Optional[int]=False				,		__A:     Union[str, Any]=False				,		__A:     List[Any]=1				,		__A:     Dict=True				,		__A:     List[Any]=512				,		__A:     Optional[int]=2048**-0.5				,		__A:     Tuple=1e-12				,		__A:     Optional[int]=0.0_2				,		__A:     Optional[int]=0				,		__A:     Union[str, Any]=1				,		__A:     List[Any]=2				,		__A:     Union[str, Any]=3				,		__A:     Tuple=5				,		__A:     Tuple=True				,		__A:     Dict="first"				,		__A:     Optional[int]=True				,		__A:     int=None				,		__A:     Optional[Any]=True				,		__A:     Dict=0.1				,		__A:     Tuple=5				,		__A:     Union[str, Any]=5				,		__A:     Tuple=0				,		__A:     List[str]=0				,		__A:     int=2				,		__A:     Optional[int]=0				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  vocab_size
												a__          =  emb_dim
												a__          =  n_layers
												a__          =  n_heads
												a__          =  dropout
												a__          =  attention_dropout
												a__          =  gelu_activation
												a__          =  sinusoidal_embeddings
												a__          =  causal
												a__          =  asm
												a__          =  n_langs
												a__          =  use_lang_emb
												a__          =  layer_norm_eps
												a__          =  bos_index
												a__          =  eos_index
												a__          =  pad_index
												a__          =  unk_index
												a__          =  mask_index
												a__          =  is_encoder
												a__          =  max_position_embeddings
												a__          =  embed_init_std
												a__          =  init_std
												a__          =  summary_type
												a__          =  summary_use_proj
												a__          =  summary_activation
												a__          =  summary_proj_to_labels
												a__          =  summary_first_dropout
												a__          =  start_n_top
												a__          =  end_n_top
												a__          =  mask_token_id
												a__          =  lang_id

												if "n_words" in kwargs:
																	a__          =  kwargs['''n_words''']

												super().__init__(pad_token_id=__A				,		bos_token_id=__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							@property
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												if self.task == "multiple-choice":
																	a__          =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	a__          =  {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]    )


