






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_       :Any       =						logging.getLogger(__name__)

A_       :int       =						{'''facebook/bart-base''': BartForConditionalGeneration}
A_       :Tuple       =						{'''facebook/bart-base''': BartTokenizer}






def 							A     (       )     ->						Tuple:
					__UpperCamelCase      :					Any       		=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      :					str       		=parser.parse_args()

					return args






def 							A     (       a_							,a_="cpu"					)     ->						Union[str, Any]:
					__UpperCamelCase      :					Tuple       		=model_dict[model_name].from_pretrained(a_					).to(a_					)
					__UpperCamelCase      :					Dict       		=tokenizer_dict[model_name].from_pretrained(a_					)

					if model_name in ["facebook/bart-base"]:
										__UpperCamelCase      :					Optional[Any]       		=0
										__UpperCamelCase      :					Optional[Any]       		=None
										__UpperCamelCase      :					int       		=0

					return huggingface_model, tokenizer






def 							A     (       a_							,a_							,a_							,a_							,a_					)     ->						Optional[int]:
					model.eval()

					__UpperCamelCase      :					Union[str, Any]       		=None
					__UpperCamelCase      :					Optional[Any]       		=torch.jit.script(BARTBeamSearchGenerator(a_					)					)

					with torch.no_grad():
										__UpperCamelCase      :					Optional[int]       		='My friends are cool but they eat too many carbs.'
										__UpperCamelCase      :					Tuple       		=tokenizer([ARTICLE_TO_SUMMARIZE]							,max_length=1_024							,return_tensors='pt'					).to(model.device					)

										__UpperCamelCase      :					str       		=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_					)					)

										__UpperCamelCase      :					int       		=remove_dup_initializers(os.path.abspath(a_					)					)

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

										__UpperCamelCase      :					List[str]       		=onnxruntime.InferenceSession(a_					)
										__UpperCamelCase      :					Any       		=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 							A     (       )     ->						Any:
					__UpperCamelCase      :					Optional[Any]       		=parse_args()
					__UpperCamelCase      :					Optional[int]       		=5
					__UpperCamelCase      :					int       		=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      :					int       		=torch.device(args.device					)

					__UpperCamelCase		,				__UpperCamelCase      :					List[str]       		=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      :					List[Any]       		=args.max_length

					if args.num_beams:
										__UpperCamelCase      :					Any       		=args.num_beams

					if args.output_file_path:
										__UpperCamelCase      :					Tuple       		=args.output_file_path
					else:
										__UpperCamelCase      :					str       		='BART.onnx'

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


if __name__ == "__main__":
							main()








from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


