





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__:    List[str]  	=	logging.getLogger(__name__)

A__:    Optional[int]  	=	{'''facebook/bart-base''': BartForConditionalGeneration}
A__:    Tuple  	=	{'''facebook/bart-base''': BartTokenizer}



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	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.")

    UpperCamelCase__:	Tuple					=     parser.parse_args()

    return args



def 				lowerCAmelCase_ (							A_    ,A_="cpu"):
    UpperCamelCase__:	Union[str, Any]					=     model_dict[model_name].from_pretrained(A_).to(A_)
    UpperCamelCase__:	List[Any]					=     tokenizer_dict[model_name].from_pretrained(A_)

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

    return huggingface_model, tokenizer



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_    ,A_):
    model.eval()

    UpperCamelCase__:	str					=     None
    UpperCamelCase__:	int					=     torch.jit.script(BARTBeamSearchGenerator(A_))

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

        UpperCamelCase__:	List[str]					=     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_))

        UpperCamelCase__:	Optional[Any]					=     remove_dup_initializers(os.path.abspath(A_))

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

        UpperCamelCase__:	Optional[Any]					=     onnxruntime.InferenceSession(A_)
        UpperCamelCase__:	List[str]					=     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 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Optional[Any]					=     parse_args()
    UpperCamelCase__:	int					=     5
    UpperCamelCase__:	Optional[int]					=     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__:	Optional[Any]					=     torch.device(args.device)

    UpperCamelCase__				, UpperCamelCase__:	str					=     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:
        UpperCamelCase__:	Union[str, Any]					=     args.max_length

    if args.num_beams:
        UpperCamelCase__:	Tuple					=     args.num_beams

    if args.output_file_path:
        UpperCamelCase__:	Optional[int]					=     args.output_file_path
    else:
        UpperCamelCase__:	str					=     "BART.onnx"

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


if __name__ == "__main__":
   main()








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


A__:    Dict  	=	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__:    Any  	=	{
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

A__:    Tuple  	=	{
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

A__:    List[Any]  	=	{
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       VOCAB_FILES_NAMES
      UpperCamelCase__  		=       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__  		=       PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase__  		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__  		=       ElectraTokenizer






      def __init__(		self:			List[str]	,    __lowerCamelCase:			int=None	,    __lowerCamelCase:			List[str]=None	,    __lowerCamelCase:			str=True	,    __lowerCamelCase:			Dict="[UNK]"	,    __lowerCamelCase:			Tuple="[SEP]"	,    __lowerCamelCase:			Optional[int]="[PAD]"	,    __lowerCamelCase:			str="[CLS]"	,    __lowerCamelCase:			List[str]="[MASK]"	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			Optional[Any]=None	,    **__lowerCamelCase:			str	,    ):
          '''simple docstring'''


          super().__init__(
              __lowerCamelCase	,    tokenizer_file=__lowerCamelCase	,    do_lower_case=__lowerCamelCase	,    unk_token=__lowerCamelCase	,    sep_token=__lowerCamelCase	,    pad_token=__lowerCamelCase	,    cls_token=__lowerCamelCase	,    mask_token=__lowerCamelCase	,    tokenize_chinese_chars=__lowerCamelCase	,    strip_accents=__lowerCamelCase	,    **__lowerCamelCase	,    )

          UpperCamelCase__:	Any					=     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
          if (
              normalizer_state.get("lowercase"	,    __lowerCamelCase					) != do_lower_case
              or normalizer_state.get("strip_accents"	,    __lowerCamelCase					) != strip_accents
              or normalizer_state.get("handle_chinese_chars"	,    __lowerCamelCase					) != tokenize_chinese_chars
          ):
              UpperCamelCase__:	Any					=     getattr(__lowerCamelCase	,    normalizer_state.pop("type"					)					)
              UpperCamelCase__:	Union[str, Any]					=     do_lower_case
              UpperCamelCase__:	str					=     strip_accents
              UpperCamelCase__:	Any					=     tokenize_chinese_chars
              UpperCamelCase__:	Optional[Any]					=     normalizer_class(**__lowerCamelCase					)

          UpperCamelCase__:	Union[str, Any]					=     do_lower_case






      def 			UpperCAmelCase_       (		self:			Optional[int]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Tuple=None					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     [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:			Tuple	,    __lowerCamelCase:			List[int]	,    __lowerCamelCase:			Optional[List[int]] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     [self.sep_token_id]
          UpperCamelCase__:	Union[str, Any]					=     [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:			Any	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[str] = None					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     self._tokenizer.model.save(__lowerCamelCase	,    name=__lowerCamelCase					)
          return tuple(__lowerCamelCase					)

