


import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
		from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{'vocab_file': 'spiece.model'}

lowerCamelCase						=		{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase						=		{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

lowerCamelCase						=		'▁'





class 					_a							(				SCREAMING_SNAKE_CASE_):
	_a  :  Tuple          =							VOCAB_FILES_NAMES
	_a  :  Any          =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  List[Any]          =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Optional[int]          =							["input_ids", "attention_mask"]




	def __init__(					self						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]="</s>"		,					_SCREAMING_SNAKE_CASE						:		int="<unk>"		,					_SCREAMING_SNAKE_CASE						:		Optional[int]="<pad>"		,					_SCREAMING_SNAKE_CASE						:		Dict=100		,					_SCREAMING_SNAKE_CASE						:		Any=None		,					_SCREAMING_SNAKE_CASE						:		Optional[Any] = None		,					_SCREAMING_SNAKE_CASE						:		Optional[int]=True		,					**_SCREAMING_SNAKE_CASE						:		List[Any]		,					)->       Dict:
			# Add extra_ids to the special token list
			if extra_ids > 0 and additional_special_tokens is None:
					lowerCAmelCase__   :			List[Any]         =					[F'<extra_id_{i}>' for i in range(a__		)]
			elif extra_ids > 0 and additional_special_tokens is not None:
					# Check that we have the right number of extra_id special tokens
					lowerCAmelCase__   :			Union[str, Any]         =					len(set(filter(lambda _SCREAMING_SNAKE_CASE		: bool('''extra_id''' in str(a__		)		)		,					a__		)		)		)
					if extra_tokens != extra_ids:
							raise ValueError(
							    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
							    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
							    ''' tokens'''		)
			if legacy:
					logger.warning_once(
					    F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
					    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''		)

			lowerCAmelCase__   :			str         =					legacy
			lowerCAmelCase__   :			List[str]         =					{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    eos_token=a__		,					unk_token=a__		,					pad_token=a__		,					extra_ids=a__		,					additional_special_tokens=a__		,					sp_model_kwargs=self.sp_model_kwargs		,					legacy=a__		,					**a__		,					)

			lowerCAmelCase__   :			Any         =					vocab_file
			lowerCAmelCase__   :			Any         =					extra_ids

			lowerCAmelCase__   :			Dict         =					spm.SentencePieceProcessor(**self.sp_model_kwargs		)
			self.sp_model.Load(a__		)




	@staticmethod
	def 							UpperCAmelCase__(					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->       List[str]:
			if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
					lowerCAmelCase__   :			List[Any]         =					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
					if init_max_model_length is not None and init_max_model_length != max_model_length:
							return init_max_model_length
					elif init_max_model_length is None:
							warnings.warn(
							    '''This tokenizer was incorrectly instantiated with a model max length of'''
							    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
							    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
							    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
							    F' {pretrained_model_name_or_path} automatically truncating your input to'
							    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
							    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
							    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
							    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''		,					a__		,					)

			return max_model_length




	@property
	def 							UpperCAmelCase__(					self						:		Dict		)->       Union[str, Any]:
			return self.sp_model.get_piece_size() + self._extra_ids




	def 							UpperCAmelCase__(					self						:		Any		)->       Optional[int]:
			lowerCAmelCase__   :			Union[str, Any]         =					{self.convert_ids_to_tokens(a__		): i for i in range(self.vocab_size		)}
			vocab.update(self.added_tokens_encoder		)
			return vocab




	def 							UpperCAmelCase__(					self						:		Dict		,					_SCREAMING_SNAKE_CASE						:		Dict		,					_SCREAMING_SNAKE_CASE						:		int = None		,					_SCREAMING_SNAKE_CASE						:		int = False		)->       List[str]:
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=a__		,					token_ids_a=a__		,					already_has_special_tokens=a__		)

			# normal case: some special tokens
			if token_ids_a is None:
					return ([0] * len(a__		)) + [1]
			return ([0] * len(a__		)) + [1] + ([0] * len(a__		)) + [1]




	def 							UpperCAmelCase__(					self						:		Any		)->       int:
			return list(
			    set(filter(lambda _SCREAMING_SNAKE_CASE		: bool(re.search(r'''<extra_id_\d+>'''		,					a__		)		) is not None		,					self.additional_special_tokens		)		)		)




	def 							UpperCAmelCase__(					self						:		Tuple		)->       Union[str, Any]:
			return [self._convert_token_to_id(a__		) for token in self.get_sentinel_tokens()]




	def 							UpperCAmelCase__(					self						:		str		,					_SCREAMING_SNAKE_CASE						:		Any		)->       Tuple:
			if len(a__		) > 0 and token_ids[-1] == self.eos_token_id:
					warnings.warn(
					    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
					    ''' eos tokens being added.'''		)
					return token_ids
			else:
					return token_ids + [self.eos_token_id]




	def 							UpperCAmelCase__(					self						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		List[str] = None		)->       List[Any]:
			lowerCAmelCase__   :			Tuple         =					[self.eos_token_id]

			if token_ids_a is None:
					return len(token_ids_a + eos		) * [0]
			return len(token_ids_a + eos + token_ids_a + eos		) * [0]




	def 							UpperCAmelCase__(					self						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		str		,					_SCREAMING_SNAKE_CASE						:		Optional[int] = None		)->       str:
			lowerCAmelCase__   :			Tuple         =					self._add_eos_if_not_present(a__		)
			if token_ids_a is None:
					return token_ids_a
			else:
					lowerCAmelCase__   :			Optional[Any]         =					self._add_eos_if_not_present(a__		)
					return token_ids_a + token_ids_a




	def __getstate__(					self						:		Optional[int]		)->       Union[str, Any]:
			lowerCAmelCase__   :			Tuple         =					self.__dict__.copy()
			lowerCAmelCase__   :			List[str]         =					None
			return state




	def __setstate__(					self						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		int		)->       List[str]:
			lowerCAmelCase__   :			Optional[int]         =					d

			# for backward compatibility
			if not hasattr(self		,					'''sp_model_kwargs'''		):
					lowerCAmelCase__   :			Any         =					{}

			lowerCAmelCase__   :			Any         =					spm.SentencePieceProcessor(**self.sp_model_kwargs		)
			self.sp_model.Load(self.vocab_file		)




	def 							UpperCAmelCase__(					self						:		int		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					**_SCREAMING_SNAKE_CASE						:		List[Any]		)->       List[Any]:
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
					lowerCAmelCase__   :			Union[str, Any]         =					SPIECE_UNDERLINE + text.replace(a__		,					''' '''		)
			return super().tokenize(a__		,					**a__		)




	def 							UpperCAmelCase__(					self						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					**_SCREAMING_SNAKE_CASE						:		Dict		)->       Optional[Any]:
			if not self.legacy:
					lowerCAmelCase__   :			int         =					text.startswith(a__		)
					if is_first:
							lowerCAmelCase__   :			Tuple         =					text[1:]

			lowerCAmelCase__   :			List[Any]         =					self.sp_model.encode(a__		,					out_type=a__		)

			if not self.legacy and not is_first and not text.startswith(''' '''		) and tokens[0].startswith(a__		):
					lowerCAmelCase__   :			Optional[int]         =					([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
			return tokens




	def 							UpperCAmelCase__(					self						:		Dict		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->       List[Any]:
			if token.startswith('''<extra_id_'''		):
					lowerCAmelCase__   :			Any         =					re.match(r'''<extra_id_(\d+)>'''		,					a__		)
					lowerCAmelCase__   :			Dict         =					int(match.group(1		)		)
					return self.vocab_size - num - 1
			return self.sp_model.piece_to_id(a__		)




	def 							UpperCAmelCase__(					self						:		Any		,					_SCREAMING_SNAKE_CASE						:		Tuple		)->       Union[str, Any]:
			if index < self.sp_model.get_piece_size():
					lowerCAmelCase__   :			Union[str, Any]         =					self.sp_model.IdToPiece(a__		)
			else:
					lowerCAmelCase__   :			Union[str, Any]         =					F'<extra_id_{self.vocab_size - 1 - index}>'
			return token




	def 							UpperCAmelCase__(					self						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		int		)->       List[Any]:
			lowerCAmelCase__   :			Union[str, Any]         =					[]
			lowerCAmelCase__   :			List[str]         =					""""""
			lowerCAmelCase__   :			Tuple         =					False
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special:
									out_string += " "
							out_string += self.sp_model.decode(a__		) + token
							lowerCAmelCase__   :			Optional[int]         =					True
							lowerCAmelCase__   :			Union[str, Any]         =					[]
					else:
							current_sub_tokens.append(a__		)
							lowerCAmelCase__   :			Tuple         =					False
			out_string += self.sp_model.decode(a__		)
			return out_string.strip()




	def 							UpperCAmelCase__(					self						:		Any		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		List[str] = None		)->       List[str]:
			if not os.path.isdir(a__		):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
					return
			lowerCAmelCase__   :			Optional[Any]         =					os.path.join(
			    a__		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

			if os.path.abspath(self.vocab_file		) != os.path.abspath(a__		) and os.path.isfile(self.vocab_file		):
					copyfile(self.vocab_file		,					a__		)
			elif not os.path.isfile(self.vocab_file		):
					with open(a__		,					'''wb'''		) as fi:
							lowerCAmelCase__   :			Optional[Any]         =					self.sp_model.serialized_model_proto()
							fi.write(a__		)

			return (out_vocab_file,)





"""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()




