




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,
)

__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)

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

def 			a   ():
							SCREAMING_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.'''     )

							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()

							return args

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase="cpu"     ):
							SCREAMING_SNAKE_CASE_   	=    model_dict[model_name].from_pretrained(_lowerCAmelCase     ).to(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    tokenizer_dict[model_name].from_pretrained(_lowerCAmelCase     )

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

							return huggingface_model, tokenizer

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							model.eval()

							SCREAMING_SNAKE_CASE_   	=    None
							SCREAMING_SNAKE_CASE_   	=    torch.jit.script(BARTBeamSearchGenerator(_lowerCAmelCase     )     )

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

														SCREAMING_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=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=_lowerCAmelCase  ,    )

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

														SCREAMING_SNAKE_CASE_   	=    remove_dup_initializers(os.path.abspath(_lowerCAmelCase     )     )

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

														SCREAMING_SNAKE_CASE_   	=    onnxruntime.InferenceSession(_lowerCAmelCase     )
														SCREAMING_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 			a   ():
							SCREAMING_SNAKE_CASE_   	=    parse_args()
							SCREAMING_SNAKE_CASE_   	=    5
							SCREAMING_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()

							SCREAMING_SNAKE_CASE_   	=    torch.device(args.device     )

							SCREAMING_SNAKE_CASE_	,	SCREAMING_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:
														SCREAMING_SNAKE_CASE_   	=    args.max_length

							if args.num_beams:
														SCREAMING_SNAKE_CASE_   	=    args.num_beams

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

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


if __name__ == "__main__":
			main()








import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE 						="""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

__SCREAMING_SNAKE_CASE 						="""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

__SCREAMING_SNAKE_CASE 						="""
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class  __magic_name__						(  datasets.Metric):
				'''simple docstring'''







				def     _A							(							self:   Optional[int]		):
											return datasets.MetricInfo(
											    description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
											        {
											            '''predictions''': datasets.Value('''string'''		,					id='''sequence'''		),
											            '''references''': datasets.Value('''string'''		,					id='''sequence'''		),
											        }		)		,					reference_urls=[]		,					)





				def     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   List[str]=False		,					):
											if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in predictions]		)
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in references]		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)

											if ignore_case:
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)

											if ignore_punctuation:
																		SCREAMING_SNAKE_CASE_   	=    string.punctuation.maketrans(''''''		,					''''''		,					string.punctuation		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											if ignore_numbers:
																		SCREAMING_SNAKE_CASE_   	=    string.digits.maketrans(''''''		,					''''''		,					string.digits		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    predictions == references

											return {"exact_match": np.mean(_lowerCamelCase		) * 1_00}


