
"""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__   :					Dict							=   logging.getLogger(__name__)

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




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''						)
  parser.add_argument(
      '''--validation_file''' ,				type=_UpperCamelCase ,				default=_UpperCamelCase ,				help='''A csv or a json file containing the validation data.'''						)
  parser.add_argument(
      '''--max_length''' ,				type=_UpperCamelCase ,				default=5 ,				help='''The maximum total input sequence length after tokenization.''' ,				)
  parser.add_argument(
      '''--num_beams''' ,				type=_UpperCamelCase ,				default=_UpperCamelCase ,				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=_UpperCamelCase ,				help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,				required=_UpperCamelCase ,				)
  parser.add_argument(
      '''--config_name''' ,				type=_UpperCamelCase ,				default=_UpperCamelCase ,				help='''Pretrained config name or path if not the same as model_name''' ,				)
  parser.add_argument(
      '''--device''' ,				type=_UpperCamelCase ,				default='''cpu''' ,				help='''Device where the model will be run''' ,				)
  parser.add_argument('''--output_file_path''' ,				type=_UpperCamelCase ,				default=_UpperCamelCase ,				help='''Where to store the final ONNX file.'''						)

  _lowercase:			Tuple       =     parser.parse_args()

  return args




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase="cpu"						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     model_dict[model_name].from_pretrained(_UpperCamelCase						).to(_UpperCamelCase						)
  _lowercase:			Union[str, Any]       =     tokenizer_dict[model_name].from_pretrained(_UpperCamelCase						)

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

  return huggingface_model, tokenizer




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  model.eval()

  _lowercase:			Optional[int]       =     None
  _lowercase:			int       =     torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase						)						)

  with torch.no_grad():
    _lowercase:			Optional[Any]       =     '''My friends are cool but they eat too many carbs.'''
    _lowercase:			Optional[int]       =     tokenizer([ARTICLE_TO_SUMMARIZE] ,				max_length=1_024 ,				return_tensors='''pt'''						).to(model.device						)

    _lowercase:			int       =     model.generate(
        inputs['''input_ids'''] ,				attention_mask=inputs['''attention_mask'''] ,				num_beams=_UpperCamelCase ,				max_length=_UpperCamelCase ,				early_stopping=_UpperCamelCase ,				decoder_start_token_id=model.config.decoder_start_token_id ,				)

    torch.onnx.export(
        _UpperCamelCase ,				(
            inputs['''input_ids'''],
            inputs['''attention_mask'''],
            num_beams,
            max_length,
            model.config.decoder_start_token_id,
        ) ,				_UpperCamelCase ,				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=_UpperCamelCase ,				)

    logger.info('''Model exported to {}'''.format(_UpperCamelCase						)						)

    _lowercase:			Optional[int]       =     remove_dup_initializers(os.path.abspath(_UpperCamelCase						)						)

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

    _lowercase:			Tuple       =     onnxruntime.InferenceSession(_UpperCamelCase						)
    _lowercase:			Union[str, Any]       =     ort_sess.run(
        _UpperCamelCase ,				{
            '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
            '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
            '''num_beams''': np.array(_UpperCamelCase						),
            '''max_length''': np.array(_UpperCamelCase						),
            '''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		(		):






  """simple docstring"""



  _lowercase:			Tuple       =     parse_args()
  _lowercase:			Union[str, Any]       =     5
  _lowercase:			List[Any]       =     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()

  _lowercase:			int       =     torch.device(args.device						)

  _lowercase ,				_lowercase:			Optional[Any]       =     load_model_tokenizer(args.model_name_or_path ,				_UpperCamelCase						)

  if model.config.decoder_start_token_id is None:
    raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''						)

  model.to(_UpperCamelCase						)

  if args.max_length:
    _lowercase:			List[Any]       =     args.max_length

  if args.num_beams:
    _lowercase:			int       =     args.num_beams

  if args.output_file_path:
    _lowercase:			Dict       =     args.output_file_path
  else:
    _lowercase:			Tuple       =     '''BART.onnx'''

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


if __name__ == "__main__":
  main()


"""simple docstring"""




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A__   :					List[Any]							=   datasets.utils.logging.get_logger(__name__)


@dataclass
class 			__magic_name__       (       datasets.BuilderConfig ):
       UpperCamelCase_   =		None
       UpperCamelCase_   =		"utf-8"
       UpperCamelCase_   =		None
       UpperCamelCase_   =		None
       UpperCamelCase_   =		True  # deprecated
       UpperCamelCase_   =		None  # deprecated
       UpperCamelCase_   =		10 << 20  # 10MB
       UpperCamelCase_   =		None


class 			__magic_name__       (       datasets.ArrowBasedBuilder ):
       UpperCamelCase_   =		JsonConfig
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self.config.block_size is not None:
           logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''       )
           _lowercase:			List[str]       =     self.config.block_size
         if self.config.use_threads is not True:
           logger.warning(
               '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''       )
         if self.config.newlines_in_values is not None:
           raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''       )
         return datasets.DatasetInfo(features=self.config.features       )
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         if not self.config.data_files:
           raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''       )
         _lowercase:			int       =     dl_manager.download_and_extract(self.config.data_files       )
         if isinstance(A_ ,				(str, list, tuple)       ):
           _lowercase:			Tuple       =     data_files
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			Dict       =     [dl_manager.iter_files(A_       ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={'''files''': files}       )]
         _lowercase:			str       =     []
         for split_name, files in data_files.items():
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			str       =     [dl_manager.iter_files(A_       ) for file in files]
           splits.append(datasets.SplitGenerator(name=A_ ,				gen_kwargs={'''files''': files}       )       )
         return splits
       def 							lowercase_							(     self ,				A_       )     ->	pa.Table:





         """simple docstring"""




         if self.config.features is not None:
           # adding missing columns
           for column_name in set(self.config.features       ) - set(pa_table.column_names       ):
             _lowercase:			Any       =     self.config.features.arrow_schema.field(A_       ).type
             _lowercase:			Optional[Any]       =     pa_table.append_column(A_ ,				pa.array([None] * len(A_       ) ,				type=A_       )       )
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           _lowercase:			Optional[int]       =     table_cast(A_ ,				self.config.features.arrow_schema       )
         return pa_table



       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         for file_idx, file in enumerate(itertools.chain.from_iterable(A_       )       ):
           # If the file is one json object and if we need to look at the list of items in one specific field
           if self.config.field is not None:
             with open(A_ ,				encoding=self.config.encoding ,				errors=self.config.encoding_errors       ) as f:
               _lowercase:			Optional[int]       =     json.load(A_       )

             # We keep only the field we are interested in
             _lowercase:			str       =     dataset[self.config.field]

             # We accept two format: a list of dicts or a dict of lists
             if isinstance(A_ ,				(list, tuple)       ):
               _lowercase:			Dict       =     set().union(*[row.keys() for row in dataset]       )
               _lowercase:			List[str]       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
             else:
               _lowercase:			Dict       =     dataset
             _lowercase:			Union[str, Any]       =     pa.Table.from_pydict(A_       )
             yield file_idx, self._cast_table(A_       )

           # If the file has one json object per line
           else:
             with open(A_ ,				'''rb'''       ) as f:
               _lowercase:			int       =     0
               # Use block_size equal to the chunk size divided by 32 to leverage multithreading
               # Set a default minimum value of 16kB if the chunk size is really small
               _lowercase:			Optional[int]       =     max(self.config.chunksize // 32 ,				16 << 10       )
               _lowercase:			List[Any]       =     (
                   self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
               )
               while True:
                 _lowercase:			Union[str, Any]       =     f.read(self.config.chunksize       )
                 if not batch:
                   break
                 # Finish current line
                 try:
                   batch += f.readline()
                 except (AttributeError, io.UnsupportedOperation):
                   batch += readline(A_       )
                 # PyArrow only accepts utf-8 encoded bytes
                 if self.config.encoding != "utf-8":
                   _lowercase:			Any       =     batch.decode(self.config.encoding ,				errors=A_       ).encode('''utf-8'''       )
                 try:
                   while True:
                     try:
                       _lowercase:			Optional[int]       =     paj.read_json(
                           io.BytesIO(A_       ) ,				read_options=paj.ReadOptions(block_size=A_       )       )
                       break
                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                       if (
                           isinstance(A_ ,				pa.ArrowInvalid       )
                           and "straddling" not in str(A_       )
                           or block_size > len(A_       )
                       ):
                         raise
                       else:
                         # Increase the block size in case it was too small.
                         # The block size will be reset for the next file.
                         logger.debug(
                             f'''Batch of {len(A_       )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''       )
                         block_size *= 2
                 except pa.ArrowInvalid as e:
                   try:
                     with open(
                         A_ ,				encoding=self.config.encoding ,				errors=self.config.encoding_errors       ) as f:
                       _lowercase:			Optional[Any]       =     json.load(A_       )
                   except json.JSONDecodeError:
                     logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                     raise e
                   # If possible, parse the file as a list of json objects and exit the loop
                   if isinstance(A_ ,				A_       ):  # list is the only sequence type supported in JSON
                     try:
                       _lowercase:			Optional[int]       =     set().union(*[row.keys() for row in dataset]       )
                       _lowercase:			Tuple       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
                       _lowercase:			str       =     pa.Table.from_pydict(A_       )
                     except (pa.ArrowInvalid, AttributeError) as e:
                       logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                       raise ValueError(f'''Not able to read records in the JSON file at {file}.'''       ) from None
                     yield file_idx, self._cast_table(A_       )
                     break
                   else:
                     logger.error(f'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
                     raise ValueError(
                         f'''Not able to read records in the JSON file at {file}. '''
                         f'''You should probably indicate the field of the JSON file containing your records. '''
                         f'''This JSON file contain the following fields: {str(list(dataset.keys()       )       )}. '''
                         f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''       ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                 yield (file_idx, batch_idx), self._cast_table(A_       )
                 batch_idx += 1
