






'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		__lowerCAmelCase									=   None


__lowerCAmelCase									=   logging.get_logger(__name__)


__lowerCAmelCase									=   '▁'

__lowerCAmelCase									=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase									=   {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__lowerCAmelCase									=   {
    'google/pegasus-xsum': 512,
}





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					VOCAB_FILES_NAMES
			snake_case__	=					PRETRAINED_VOCAB_FILES_MAP
			snake_case__	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__	=					PegasusTokenizer
			snake_case__	=					["input_ids", "attention_mask"]






			def __init__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Any=None	,    __SCREAMING_SNAKE_CASE   :      List[Any]=None	,    __SCREAMING_SNAKE_CASE   :      List[str]="<pad>"	,    __SCREAMING_SNAKE_CASE   :      List[Any]="</s>"	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]="<unk>"	,    __SCREAMING_SNAKE_CASE   :      List[Any]="<mask_2>"	,    __SCREAMING_SNAKE_CASE   :      str="<mask_1>"	,    __SCREAMING_SNAKE_CASE   :      int=None	,    __SCREAMING_SNAKE_CASE   :      Tuple=103	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    )      ->      Tuple:
							a_		:	Dict									=   offset

							if additional_special_tokens is not None:
											if not isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															raise TypeError(
															    f'additional_special_tokens should be of type {type(__SCREAMING_SNAKE_CASE       )}, but is'
															    f' {type(__SCREAMING_SNAKE_CASE       )}'       )

											a_		:	Optional[Any]									=   (
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'<unk_{i}>' for i in range(len(__SCREAMING_SNAKE_CASE       )	,    self.offset - 1       )
											]

											if len(set(__SCREAMING_SNAKE_CASE       )       ) != len(__SCREAMING_SNAKE_CASE       ):
															raise ValueError(
															    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
															    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'       )
											a_		:	int									=   additional_special_tokens_extended
							else:
											a_		:	Union[str, Any]									=   [mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'<unk_{i}>' for i in range(2	,    self.offset       )]

							super().__init__(
							    __SCREAMING_SNAKE_CASE	,    tokenizer_file=__SCREAMING_SNAKE_CASE	,    pad_token=__SCREAMING_SNAKE_CASE	,    eos_token=__SCREAMING_SNAKE_CASE	,    unk_token=__SCREAMING_SNAKE_CASE	,    mask_token=__SCREAMING_SNAKE_CASE	,    mask_token_sent=__SCREAMING_SNAKE_CASE	,    offset=__SCREAMING_SNAKE_CASE	,    additional_special_tokens=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
							a_		:	List[str]									=   vocab_file
							a_		:	Dict									=   False if not self.vocab_file else True






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      Dict:
							a_		:	Union[str, Any]									=   set(self.all_special_ids       )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

							if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
											raise ValueError(
											    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
											    f' {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}'       )

							return [1 if x in all_special_ids else 0 for x in seq]






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List	,    __SCREAMING_SNAKE_CASE   :      Optional[List] = None	,    __SCREAMING_SNAKE_CASE   :      bool = False       )      ->      List[int]:
							if already_has_special_tokens:
											return self._special_token_mask(__SCREAMING_SNAKE_CASE       )
							elif token_ids_a is None:
											return self._special_token_mask(__SCREAMING_SNAKE_CASE       ) + [1]
							else:
											return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=None       )      ->      List[int]:
							if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[str] = None       )      ->      Tuple[str]:
							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''       )

							if not os.path.isdir(__SCREAMING_SNAKE_CASE       ):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
											return
							a_		:	List[Any]									=   os.path.join(
							    __SCREAMING_SNAKE_CASE	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(__SCREAMING_SNAKE_CASE       ):
											copyfile(self.vocab_file	,    __SCREAMING_SNAKE_CASE       )

							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,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

__lowerCAmelCase									=   {'facebook/bart-base': BartForConditionalGeneration}
__lowerCAmelCase									=   {'facebook/bart-base': BartTokenizer}





def 		_UpperCAmelCase (				):
				a_		:	int									=   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.'''   )

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

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

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

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

				with torch.no_grad():
								a_		:	Any									=   '''My friends are cool but they eat too many carbs.'''
								a_		:	Dict									=   tokenizer([ARTICLE_TO_SUMMARIZE]    ,		max_length=10_24    ,		return_tensors='''pt'''   ).to(model.device   )

								a_		:	Dict									=   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   )   )

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[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 		_UpperCAmelCase (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[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()

				a_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   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:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

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


if __name__ == "__main__":
		main()




