'''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,
)

__lowerCAmelCase						:							Optional[Any]    							=						logging.getLogger(__name__)

__lowerCAmelCase						:							Optional[Any]    							=						{"facebook/bart-base": BartForConditionalGeneration}
__lowerCAmelCase						:							int    							=						{"facebook/bart-base": BartTokenizer}





def    lowerCAmelCase       (		):


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

	__UpperCAmelCase      					=      parser.parse_args()

	return args





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: Union[str, Any]="cpu"							):


	"""simple docstring"""
	__UpperCAmelCase      					=      model_dict[model_name].from_pretrained(UpperCamelCase__							).to(UpperCamelCase__							)
	__UpperCAmelCase      					=      tokenizer_dict[model_name].from_pretrained(UpperCamelCase__							)

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

	return huggingface_model, tokenizer





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: List[str]     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: Dict							):


	"""simple docstring"""
	model.eval()

	__UpperCAmelCase      					=      None
	__UpperCAmelCase      					=      torch.jit.script(BARTBeamSearchGenerator(UpperCamelCase__							)							)

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

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

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

		__UpperCAmelCase      					=      remove_dup_initializers(os.path.abspath(UpperCamelCase__							)							)

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

		__UpperCAmelCase      					=      onnxruntime.InferenceSession(UpperCamelCase__							)
		__UpperCAmelCase      					=      ort_sess.run(
		    UpperCamelCase__     ,       {
		        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
		        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
		        '''num_beams''': np.array(UpperCamelCase__							),
		        '''max_length''': np.array(UpperCamelCase__							),
		        '''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       (		):


	"""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     ,       UpperCamelCase__							)

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

	model.to(UpperCamelCase__							)

	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(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)


if __name__ == "__main__":
					main()




'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__UpperCAmelCase       							=				TypeVar('''KEY''')
__UpperCAmelCase       							=				TypeVar('''VAL''')




@dataclass(frozen=a__							,							slots=a__    )
class 						a__							(				Generic[KEY, VAL]    ):





							'''simple docstring'''






							lowercase__     :  KEY
							lowercase__     :  VAL




class 						a__							(				_Item    ):





							'''simple docstring'''






							def __init__(						self  )			->				None:
														super().__init__(lowerCamelCase_   ,       lowerCamelCase_  )







							def __bool__(						self  )			->				bool:
														return False

__UpperCAmelCase       							=				_DeletedItem()




class 						a__							(				MutableMapping[KEY, VAL]    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_ = 8   ,       lowerCamelCase_ = 0.75  )			->				None:
														lowerCAmelCase__			  =      initial_block_size
														lowerCAmelCase__			  =      [None] * initial_block_size
														assert 0.0 < capacity_factor < 1.0
														lowerCAmelCase__			  =      capacity_factor
														lowerCAmelCase__			  =      0







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return hash(lowerCamelCase_  ) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return (ind + 1) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				bool:
														lowerCAmelCase__			  =      self._buckets[ind]
														if not stored:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					self._len += 1
																					return True
														elif stored.key == key:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					return True
														else:
																					return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor
														return len(self  ) >= int(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														if len(self._buckets  ) <= self._initial_block_size:
																					return False
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor / 2
														return len(self  ) < limit







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				None:
														lowerCAmelCase__			  =      self._buckets
														lowerCAmelCase__			  =      [None] * new_size
														lowerCAmelCase__			  =      0
														for item in old_buckets:
																					if item:
																												self._add_item(item.key   ,       item.val  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) * 2  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) // 2  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Iterator[int]:
														lowerCAmelCase__			  =      self._get_bucket_index(lowerCamelCase_  )
														for _ in range(len(self._buckets  )  ):
																					yield ind
																					lowerCAmelCase__			  =      self._get_next_ind(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					if self._try_set(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																												break







							def __setitem__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														if self._is_full():
																					self._size_up()

														self._add_item(lowerCamelCase_   ,       lowerCamelCase_  )







							def __delitem__(						self   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												raise KeyError(lowerCamelCase_  )
																					if item is _deleted:
																												continue
																					if item.key == key:
																												lowerCAmelCase__			  =      _deleted
																												self._len -= 1
																												break
														if self._is_sparse():
																					self._size_down()







							def __getitem__(						self   ,       lowerCamelCase_  )			->				VAL:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												break
																					if item is _deleted:
																												continue
																					if item.key == key:
																												return item.val
														raise KeyError(lowerCamelCase_  )







							def __len__(						self  )			->				int:
														return self._len







							def __iter__(						self  )			->				Iterator[KEY]:
														yield from (item.key for item in self._buckets if item)







							def __repr__(						self  )			->				str:
														lowerCAmelCase__			  =      ''' ,'''.join(
														    F"""{item.key}: {item.val}""" for item in self._buckets if item  )
														return F"""HashMap({val_string})"""