
import os
from collections import deque

import torch
from torch.utils.data import Dataset



class    a_ (			__UpperCamelCase    ):






			def __init__(      self				,			__UpperCamelCase=""				,			__UpperCamelCase="train"  ):
									assert os.path.isdir(__UpperCamelCase  )

									_lowercase					       =					[]
									_lowercase					       =					os.listdir(__UpperCamelCase  )
									for story_filename in story_filenames_list:
															if "summary" in story_filename:
																					continue
															_lowercase					       =					os.path.join(__UpperCamelCase				,			__UpperCamelCase  )
															if not os.path.isfile(__UpperCamelCase  ):
																					continue
															self.documents.append(__UpperCamelCase  )






			def __len__(      self  ):
									return len(self.documents  )






			def __getitem__(      self				,			__UpperCamelCase  ):
									_lowercase					       =					self.documents[idx]
									_lowercase					       =					document_path.split("""/"""  )[-1]
									with open(__UpperCamelCase				,			encoding="""utf-8"""  ) as source:
															_lowercase					       =					source.read()
															_lowercase       ,						_lowercase					       =					process_story(__UpperCamelCase  )
									return document_name, story_lines, summary_lines







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Dict:
						_lowercase					       =					list(filter(lambda SCREAMING_SNAKE_CASE_       : len(__lowerCAmelCase       ) != 0		,   [line.strip() for line in raw_story.split("""\n"""       )]       )       )

						# for some unknown reason some lines miss a period, add it
						_lowercase					       =					[_add_missing_period(__lowerCAmelCase       ) for line in nonempty_lines]

						# gather article lines
						_lowercase					       =					[]
						_lowercase					       =					deque(__lowerCAmelCase       )
						while True:
												try:
																		_lowercase					       =					lines.popleft()
																		if element.startswith("""@highlight"""       ):
																								break
																		story_lines.append(__lowerCAmelCase       )
												except IndexError:
																		# if "@highlight" is absent from the file we pop
																		# all elements until there is None, raising an exception.
																		return story_lines, []

    # gather summary lines
						_lowercase					       =					list(filter(lambda SCREAMING_SNAKE_CASE_       : not t.startswith("""@highlight"""       )		,   __lowerCAmelCase       )       )

						return story_lines, summary_lines




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     Optional[int]:
						_lowercase					       =					[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
						if line.startswith("""@highlight"""       ):
												return line
						if line[-1] in END_TOKENS:
												return line
						return line + "."




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     Optional[int]:
						if len(__lowerCAmelCase       ) > block_size:
												return sequence[:block_size]
						else:
												sequence.extend([pad_token_id] * (block_size - len(__lowerCAmelCase       ))       )
												return sequence




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase					       =					torch.ones_like(__lowerCAmelCase       )
						_lowercase					       =					sequence == pad_token_id
						_lowercase					       =					0
						return mask




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     Optional[Any]:
						_lowercase					       =					[tokenizer.encode(__lowerCAmelCase       ) for line in story_lines]
						_lowercase					       =					[token for sentence in story_lines_token_ids for token in sentence]
						_lowercase					       =					[tokenizer.encode(__lowerCAmelCase       ) for line in summary_lines]
						_lowercase					       =					[token for sentence in summary_lines_token_ids for token in sentence]

						return story_token_ids, summary_token_ids




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     List[Any]:
						_lowercase					       =					[]
						for sequence in batch:
												_lowercase					       =					-1
												_lowercase					       =					[]
												for s in sequence:
																		if s == separator_token_id:
																								sentence_num += 1
																		embeddings.append(sentence_num % 2       )
												batch_embeddings.append(__lowerCAmelCase       )
						return torch.tensor(__lowerCAmelCase       )

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,
)

__magic_name__  							=	logging.getLogger(__name__)

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





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

    snake_case__    =					parser.parse_args()

    return args





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase="cpu"				):
    snake_case__    =					model_dict[model_name].from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
    snake_case__    =					tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase				)

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

    return huggingface_model, tokenizer





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    model.eval()

    snake_case__    =					None
    snake_case__    =					torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase				)				)

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

        snake_case__    =					model.generate(
            inputs["input_ids"]     ,		attention_mask=inputs["attention_mask"]     ,		num_beams=__lowerCAmelCase     ,		max_length=__lowerCAmelCase     ,		early_stopping=__lowerCAmelCase     ,		decoder_start_token_id=model.config.decoder_start_token_id     ,		)

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

        logger.info("Model exported to {}".format(__lowerCAmelCase				)				)

        snake_case__    =					remove_dup_initializers(os.path.abspath(__lowerCAmelCase				)				)

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

        snake_case__    =					onnxruntime.InferenceSession(__lowerCAmelCase				)
        snake_case__    =					ort_sess.run(
            __lowerCAmelCase     ,		{
                "input_ids": inputs["input_ids"].cpu().numpy(),
                "attention_mask": inputs["attention_mask"].cpu().numpy(),
                "num_beams": np.array(__lowerCAmelCase				),
                "max_length": np.array(__lowerCAmelCase				),
                "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__     (   ):
    snake_case__    =					parse_args()
    snake_case__    =					5
    snake_case__    =					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()

    snake_case__    =					torch.device(args.device				)

    snake_case__    ,       snake_case__    =					load_model_tokenizer(args.model_name_or_path     ,		__lowerCAmelCase				)

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

    model.to(__lowerCAmelCase				)

    if args.max_length:
        snake_case__    =					args.max_length

    if args.num_beams:
        snake_case__    =					args.num_beams

    if args.output_file_path:
        snake_case__    =					args.output_file_path
    else:
        snake_case__    =					"BART.onnx"

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


if __name__ == "__main__":
     main()



