




'''simple docstring'''







# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


A									=get_logger(__name__)


class     _a						:
       __a       :  Tuple            =      """dummy_data"""
       __a       :  List[str]            =      """datasets"""
       __a       :  Tuple            =      False
       def __init__(			self		:			int     ,       lowercase		:			str     ,       lowercase		:			str     ,       lowercase		:			Union[Version, str]     ,       lowercase		:			Optional[str] = None     ,       lowercase		:			bool = False     ,       lowercase		:			bool = True     ,       lowercase		:			Optional[List[Callable]] = None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	0
           UpperCAmelCase     =	dataset_name
           UpperCAmelCase     =	cache_dir
           UpperCAmelCase     =	use_local_dummy_data
           UpperCAmelCase     =	config
           # download_callbacks take a single url as input
           UpperCAmelCase     =	download_callbacks or []
           # if False, it doesn't load existing files and it returns the paths of the dummy files relative
           # to the dummy_data zip file root
           UpperCAmelCase     =	load_existing_dummy_data

           # TODO(PVP, QL) might need to make this more general
           UpperCAmelCase     =	str(lowercase       )
           # to be downloaded
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           if self._dummy_file is None:
               UpperCAmelCase     =	self.download_dummy_data()
           return self._dummy_file
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           if self.config is not None:
               # structure is dummy / config_name / version_name
               return os.path.join('''dummy'''     ,       self.config.name     ,       self.version_name       )
           # structure is dummy / version_name
           return os.path.join('''dummy'''     ,       self.version_name       )
       @property
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           return os.path.join(self.dummy_data_folder     ,       '''dummy_data.zip'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	(
               self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
           )

           UpperCAmelCase     =	cached_path(
               lowercase     ,       cache_dir=self.cache_dir     ,       extract_compressed_file=lowercase     ,       force_extract=lowercase       )

           return os.path.join(lowercase     ,       self.dummy_file_name       )
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return os.path.join(self.datasets_scripts_dir     ,       self.dataset_name     ,       self.dummy_zip_file       )
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           if self._bucket_url is None:
               UpperCAmelCase     =	hf_github_url(self.dataset_name     ,       self.dummy_zip_file.replace(os.sep     ,       '''/'''       )       )
           return self._bucket_url
       @property
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           if os.path.isdir(self.dummy_file       ):
               return self.dummy_file
           # else cut off path to file -> example `xsum`.
           return "/".join(self.dummy_file.replace(os.sep     ,       '''/'''       ).split('''/'''       )[:-1]       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			List[str]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if self.load_existing_dummy_data:
               # dummy data is downloaded and tested
               UpperCAmelCase     =	self.dummy_file
           else:
               # dummy data cannot be downloaded and only the path to dummy file is returned
               UpperCAmelCase     =	self.dummy_file_name

           # special case when data_url is a dict
           if isinstance(lowercase     ,       lowercase       ):
               return self.create_dummy_data_dict(lowercase     ,       lowercase       )
           elif isinstance(lowercase     ,       (list, tuple)       ):
               return self.create_dummy_data_list(lowercase     ,       lowercase       )
           else:
               return self.create_dummy_data_single(lowercase     ,       lowercase       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			List[str]     ,       *lowercase		:			List[Any]       ):



           '''simple docstring'''

           return self.download_and_extract(lowercase       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any       ):



           '''simple docstring'''

           return self.download_and_extract(lowercase       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			str     ,       *lowercase		:			Optional[int]     ,       **lowercase		:			str       ):



           '''simple docstring'''

           return path
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return {}
       def 					A   (			self		:			str     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	{}
           for key, single_urls in data_url.items():
               for download_callback in self.download_callbacks:
                   if isinstance(lowercase     ,       lowercase       ):
                       for single_url in single_urls:
                           download_callback(lowercase       )
                   else:
                       UpperCAmelCase     =	single_urls
                       download_callback(lowercase       )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
               if isinstance(lowercase     ,       lowercase       ):
                   UpperCAmelCase     =	[os.path.join(lowercase     ,       urllib.parse.quote_plus(Path(lowercase       ).name       )       ) for x in single_urls]
               else:
                   UpperCAmelCase     =	single_urls
                   UpperCAmelCase     =	os.path.join(lowercase     ,       urllib.parse.quote_plus(Path(lowercase       ).name       )       )
               UpperCAmelCase     =	value

           # make sure that values are unique
           if all(isinstance(lowercase     ,       lowercase       ) for i in dummy_data_dict.values()       ) and len(set(dummy_data_dict.values()       )       ) < len(
               dummy_data_dict.values()       ):
               # append key to value to make its name unique
               UpperCAmelCase     =	{key: value + key for key, value in dummy_data_dict.items()}

           return dummy_data_dict
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	[]
           # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
           UpperCAmelCase     =	all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''     ,       lowercase       )       ) for url in data_url       )
           UpperCAmelCase     =	all(
               url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''       ) for url in data_url       )
           if data_url and (is_tf_records or is_pubmed_records):
               UpperCAmelCase     =	[data_url[0]] * len(lowercase       )
           for single_url in data_url:
               for download_callback in self.download_callbacks:
                   download_callback(lowercase       )
               # we force the name of each key to be the last file / folder name of the url path
               # if the url has arguments, we need to encode them with urllib.parse.quote_plus
               UpperCAmelCase     =	os.path.join(lowercase     ,       urllib.parse.quote_plus(single_url.split('''/'''       )[-1]       )       )
               dummy_data_list.append(lowercase       )
           return dummy_data_list
       def 					A   (			self		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Any       ):



           '''simple docstring'''

           for download_callback in self.download_callbacks:
               download_callback(lowercase       )
           # we force the name of each key to be the last file / folder name of the url path
           # if the url has arguments, we need to encode them with urllib.parse.quote_plus
           UpperCAmelCase     =	os.path.join(lowercase     ,       urllib.parse.quote_plus(data_url.split('''/'''       )[-1]       )       )
           if os.path.exists(lowercase       ) or not self.load_existing_dummy_data:
               return value
           else:
               # Backward compatibility, maybe deprecate at one point.
               # For many datasets with single url calls to dl_manager.download_and_extract,
               # the dummy_data.zip file is actually the zipped downloaded file
               # while now we expected the dummy_data.zip file to be a directory containing
               # the downloaded file.
               return path_to_dummy_data
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			Dict     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           def _iter_archive_members(lowercase		:			Union[str, Any]       ):
               # this preserves the order of the members inside the ZIP archive
               UpperCAmelCase     =	Path(self.dummy_file       ).parent
               UpperCAmelCase     =	path.relative_to(lowercase       )
               with ZipFile(self.local_path_to_dummy_data       ) as zip_file:
                   UpperCAmelCase     =	zip_file.namelist()
               for member in members:
                   if member.startswith(relative_path.as_posix()       ):
                       yield dummy_parent_path.joinpath(lowercase       )

           UpperCAmelCase     =	Path(lowercase       )
           UpperCAmelCase     =	_iter_archive_members(lowercase       ) if self.use_local_dummy_data else path.rglob('''*'''       )
           for file_path in file_paths:
               if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')       ):
                   yield file_path.relative_to(lowercase       ).as_posix(), file_path.open('''rb'''       )







       def 					A   (			self		:			str     ,       lowercase		:			Any       ):



           '''simple docstring'''

           if not isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	[paths]
           for path in paths:
               if os.path.isfile(lowercase       ):
                   if os.path.basename(lowercase       ).startswith(('''.''', '''__''')       ):
                       return
                   yield path
               else:
                   for dirpath, dirnames, filenames in os.walk(lowercase       ):
                       if os.path.basename(lowercase       ).startswith(('''.''', '''__''')       ):
                           continue
                       dirnames.sort()
                       for filename in sorted(lowercase       ):
                           if filename.startswith(('''.''', '''__''')       ):
                               continue
                           yield os.path.join(lowercase     ,       lowercase       )










'''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									=logging.getLogger(__name__)

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





def 		snake_case_							():
    UpperCAmelCase     =	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     =	parser.parse_args()

    return args





def 		snake_case_							(_a			:							Tuple					,     _a			:							str="cpu"  ):
    UpperCAmelCase     =	model_dict[model_name].from_pretrained(_a  ).to(_a  )
    UpperCAmelCase     =	tokenizer_dict[model_name].from_pretrained(_a  )

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

    return huggingface_model, tokenizer





def 		snake_case_							(_a			:							Optional[int]					,     _a			:							List[str]					,     _a			:							str					,     _a			:							Optional[Any]					,     _a			:							str  ):
    model.eval()

    UpperCAmelCase     =	None
    UpperCAmelCase     =	torch.jit.script(BARTBeamSearchGenerator(_a  )  )

    with torch.no_grad():
        UpperCAmelCase     =	'''My friends are cool but they eat too many carbs.'''
        UpperCAmelCase     =	tokenizer([ARTICLE_TO_SUMMARIZE]					,     max_length=1_0_2_4					,     return_tensors='''pt'''  ).to(model.device  )

        UpperCAmelCase     =	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=1_4					,     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     =	remove_dup_initializers(os.path.abspath(_a  )  )

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

        UpperCAmelCase     =	onnxruntime.InferenceSession(_a  )
        UpperCAmelCase     =	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 		snake_case_							():
    UpperCAmelCase     =	parse_args()
    UpperCAmelCase     =	5
    UpperCAmelCase     =	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     =	torch.device(args.device  )

    UpperCAmelCase ,  UpperCAmelCase     =	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     =	args.max_length

    if args.num_beams:
        UpperCAmelCase     =	args.num_beams

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

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


if __name__ == "__main__":
   main()




