


import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowerCamelCase						=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 					_a							:
	_a  :  str          =							field(
	    default=SCREAMING_SNAKE_CASE_     ,						metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(SCREAMING_SNAKE_CASE_)})
	_a  :  str          =							field(
	    default=SCREAMING_SNAKE_CASE_     ,						metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''})
	_a  :  int          =							field(
	    default=128     ,						metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }     ,						)
	_a  :  int          =							field(
	    default=128     ,						metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}     ,						)
	_a  :  int          =							field(
	    default=64     ,						metadata={
	        '''help''': (
	            '''The maximum number of tokens for the question. Questions longer than this will '''
	            '''be truncated to this length.'''
	        )
	    }     ,						)
	_a  :  int          =							field(
	    default=30     ,						metadata={
	        '''help''': (
	            '''The maximum length of an answer that can be generated. This is needed because the start '''
	            '''and end predictions are not conditioned on one another.'''
	        )
	    }     ,						)
	_a  :  bool          =							field(
	    default=SCREAMING_SNAKE_CASE_     ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})
	_a  :  bool          =							field(
	    default=SCREAMING_SNAKE_CASE_     ,						metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''})
	_a  :  float          =							field(
	    default=0.0     ,						metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''})
	_a  :  int          =							field(
	    default=20     ,						metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''})
	_a  :  int          =							field(
	    default=0     ,						metadata={
	        '''help''': (
	            '''language id of input for language-specific xlm models (see'''
	            ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
	        )
	    }     ,						)
	_a  :  int          =							field(default=1     ,						metadata={'''help''': '''multiple threads for converting example to features'''})





class 					_a							(				SCREAMING_SNAKE_CASE_):
	_a  :  Union[str, Any]          =							"train"
	_a  :  List[str]          =							"dev"





class 					_a							(				SCREAMING_SNAKE_CASE_):
	_a  :  SquadDataTrainingArguments
	_a  :  List[SquadFeatures]
	_a  :  Split
	_a  :  bool




	def __init__(					self						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any] = None		,					_SCREAMING_SNAKE_CASE						:		Any = Split.train		,					_SCREAMING_SNAKE_CASE						:		Dict = False		,					_SCREAMING_SNAKE_CASE						:		Optional[Any] = None		,					_SCREAMING_SNAKE_CASE						:		List[Any] = "pt"		,					)->       Any:
			lowerCAmelCase__   :			List[Any]         =					args
			lowerCAmelCase__   :			str         =					is_language_sensitive
			lowerCAmelCase__   :			Tuple         =					SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
			if isinstance(a__		,					a__		):
					try:
							lowerCAmelCase__   :			Any         =					Split[mode]
					except KeyError:
							raise KeyError('''mode is not a valid split name'''		)
			lowerCAmelCase__   :			Union[str, Any]         =					mode
			# Load data features from cache or dataset file
			lowerCAmelCase__   :			Optional[int]         =					"""v2""" if args.version_2_with_negative else """v1"""
			lowerCAmelCase__   :			Dict         =					os.path.join(
			    cache_dir if cache_dir is not None else args.data_dir		,					F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'		,					)

			# Make sure only the first process in distributed training processes the dataset,
			# and the others will use the cache.
			lowerCAmelCase__   :			int         =					cached_features_file + """.lock"""
			with FileLock(a__		):
					if os.path.exists(a__		) and not args.overwrite_cache:
							lowerCAmelCase__   :			Dict         =					time.time()
							lowerCAmelCase__   :			Optional[Any]         =					torch.load(a__		)

							# Legacy cache files have only features, while new cache files
							# will have dataset and examples also.
							lowerCAmelCase__   :			Optional[int]         =					self.old_features["""features"""]
							lowerCAmelCase__   :			Optional[int]         =					self.old_features.get('''dataset'''		,					a__		)
							lowerCAmelCase__   :			Optional[int]         =					self.old_features.get('''examples'''		,					a__		)
							logger.info(
							    F'Loading features from cached file {cached_features_file} [took %.3f s]'		,					time.time() - start		)

							if self.dataset is None or self.examples is None:
									logger.warning(
									    F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
									    ''' future run'''		)
					else:
							if mode == Split.dev:
									lowerCAmelCase__   :			Dict         =					self.processor.get_dev_examples(args.data_dir		)
							else:
									lowerCAmelCase__   :			int         =					self.processor.get_train_examples(args.data_dir		)

							lowerCAmelCase__   :			Optional[Any]         =					squad_convert_examples_to_features(
							    examples=self.examples		,					tokenizer=a__		,					max_seq_length=args.max_seq_length		,					doc_stride=args.doc_stride		,					max_query_length=args.max_query_length		,					is_training=mode == Split.train		,					threads=args.threads		,					return_dataset=a__		,					)

							lowerCAmelCase__   :			Optional[Any]         =					time.time()
							torch.save(
							    {'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples}		,					a__		,					)
							# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
							logger.info(
							    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'		)




	def __len__(					self						:		Optional[int]		)->       Any:
			return len(self.features		)




	def __getitem__(					self						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Tuple		)->       Tuple:
			# Convert to Tensors and build dataset
			lowerCAmelCase__   :			Union[str, Any]         =					self.features[i]

			lowerCAmelCase__   :			Optional[Any]         =					torch.tensor(feature.input_ids		,					dtype=torch.long		)
			lowerCAmelCase__   :			List[Any]         =					torch.tensor(feature.attention_mask		,					dtype=torch.long		)
			lowerCAmelCase__   :			Tuple         =					torch.tensor(feature.token_type_ids		,					dtype=torch.long		)
			lowerCAmelCase__   :			Union[str, Any]         =					torch.tensor(feature.cls_index		,					dtype=torch.long		)
			lowerCAmelCase__   :			str         =					torch.tensor(feature.p_mask		,					dtype=torch.float		)
			lowerCAmelCase__   :			List[Any]         =					torch.tensor(feature.is_impossible		,					dtype=torch.float		)

			lowerCAmelCase__   :			Tuple         =					{
			    """input_ids""": input_ids,
			    """attention_mask""": attention_mask,
			    """token_type_ids""": token_type_ids,
			}

			if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
					del inputs["token_type_ids"]

			if self.args.model_type in ["xlnet", "xlm"]:
					inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask}		)
					if self.args.version_2_with_negative:
							inputs.update({'''is_impossible''': is_impossible}		)
					if self.is_language_sensitive:
							inputs.update({'''langs''': (torch.ones(input_ids.shape		,					dtype=torch.intaa		) * self.args.lang_id)}		)

			if self.mode == Split.train:
					lowerCAmelCase__   :			Any         =					torch.tensor(feature.start_position		,					dtype=torch.long		)
					lowerCAmelCase__   :			Any         =					torch.tensor(feature.end_position		,					dtype=torch.long		)
					inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions}		)

			return inputs





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

_a      :		Any           =   {'facebook/bart-base': BartForConditionalGeneration}
_a      :		List[str]           =   {'facebook/bart-base': BartTokenizer}





def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  _lowerCAmelCase						:       int  			=     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."""	)

  _lowerCAmelCase						:       Optional[Any]  			=     parser.parse_args()

  return args





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Union[str, Any]="cpu"	)       ->      str:
  _lowerCAmelCase						:       List[str]  			=     model_dict[model_name].from_pretrained(_lowerCamelCase	).to(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[Any]  			=     tokenizer_dict[model_name].from_pretrained(_lowerCamelCase	)

  if model_name in ["facebook/bart-base"]:
    _lowerCAmelCase						:       List[Any]  			=     0
    _lowerCAmelCase						:       str  			=     None
    _lowerCAmelCase						:       List[str]  			=     0

  return huggingface_model, tokenizer





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       List[str]	)       ->      Tuple:
  model.eval()

  _lowerCAmelCase						:       str  			=     None
  _lowerCAmelCase						:       int  			=     torch.jit.script(BARTBeamSearchGenerator(_lowerCamelCase	)	)

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

    _lowerCAmelCase						:       Any  			=     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	)	)

    _lowerCAmelCase						:       List[str]  			=     remove_dup_initializers(os.path.abspath(_lowerCamelCase	)	)

    logger.info("""Deduplicated and optimized model written to {}""".format(_lowerCamelCase	)	)

    _lowerCAmelCase						:       str  			=     onnxruntime.InferenceSession(_lowerCamelCase	)
    _lowerCAmelCase						:       Union[str, Any]  			=     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     (			)       ->      Any:
  _lowerCAmelCase						:       Any  			=     parse_args()
  _lowerCAmelCase						:       List[Any]  			=     5
  _lowerCAmelCase						:       str  			=     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()

  _lowerCAmelCase						:       Optional[Any]  			=     torch.device(args.device	)

  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     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:
    _lowerCAmelCase						:       Dict  			=     args.max_length

  if args.num_beams:
    _lowerCAmelCase						:       Dict  			=     args.num_beams

  if args.output_file_path:
    _lowerCAmelCase						:       Any  			=     args.output_file_path
  else:
    _lowerCAmelCase						:       Union[str, Any]  			=     """BART.onnx"""

  logger.info("""Exporting model to ONNX"""	)
  export_and_validate_model(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)


if __name__ == "__main__":
     main()




