




'''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,
)

UpperCAmelCase_ 		=      logging.getLogger(__name__)

UpperCAmelCase_ 		=      {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase_ 		=      {"facebook/bart-base": BartTokenizer}


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					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			=					parser.parse_args()

  return args


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]				,					a_	:							List[str]="cpu"			):
  __a			=					model_dict[model_name].from_pretrained(a_			).to(a_			)
  __a			=					tokenizer_dict[model_name].from_pretrained(a_			)

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

  return huggingface_model, tokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							Tuple				,					a_	:							int				,					a_	:							List[str]			):
  model.eval()

  __a			=					None
  __a			=					torch.jit.script(BARTBeamSearchGenerator(a_			)			)

  with torch.no_grad():
    __a			=					'My friends are cool but they eat too many carbs.'
    __a			=					tokenizer([ARTICLE_TO_SUMMARIZE]				,					max_length=1024				,					return_tensors='pt'			).to(model.device			)

    __a			=					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			=					remove_dup_initializers(os.path.abspath(a_			)			)

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

    __a			=					onnxruntime.InferenceSession(a_			)
    __a			=					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 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					parse_args()
  __a			=					5
  __a			=					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			=					torch.device(args.device			)

  __a	,       __a			=					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			=					args.max_length

  if args.num_beams:
    __a			=					args.num_beams

  if args.output_file_path:
    __a			=					args.output_file_path
  else:
    __a			=					'BART.onnx'

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


if __name__ == "__main__":
  main()









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      RoFormerTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         pre_tok_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or pre_tok_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
     ):
       __a			=					getattr(UpperCamelCase      ,							pre_tok_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					pre_tok_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def __getstate__(	self      )				->    List[Any]:
     __a			=					self.__dict__.copy()
     __a			=					BertPreTokenizer()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					d
     __a			=					self.__dict__['_tokenizer'].get_vocab()
     __a			=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    int:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    List[Any]:
     __a			=					BertPreTokenizer()
     return super().save_pretrained(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )



