

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_			:					int    			=     logging.getLogger(__name__)

A_			:					Tuple    			=     {'facebook/bart-base': BartForConditionalGeneration}
A_			:					int    			=     {'facebook/bart-base': BartTokenizer}

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

	A__				:       str	  =						parser.parse_args()

	return args

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					Union[str, Any]="cpu"   )	-> Union[str, Any]:
	A__				:       Optional[Any]	  =						model_dict[model_name].from_pretrained(lowercase_   ).to(lowercase_   )
	A__				:       List[Any]	  =						tokenizer_dict[model_name].from_pretrained(lowercase_   )

	if model_name in ["facebook/bart-base"]:
		A__				:       Dict	  =						0
		A__				:       Union[str, Any]	  =						None
		A__				:       Optional[Any]	  =						0

	return huggingface_model, tokenizer

def 		UpperCamelCase   (lowercase_:					Optional[int]       , lowercase_:					Union[str, Any]       , lowercase_:					str       , lowercase_:					Optional[Any]       , lowercase_:					List[str]   )	-> Optional[Any]:
	model.eval()

	A__				:       List[str]	  =						None
	A__				:       Any	  =						torch.jit.script(BARTBeamSearchGenerator(lowercase_   )   )

	with torch.no_grad():
		A__				:       Union[str, Any]	  =						"""My friends are cool but they eat too many carbs."""
		A__				:       Any	  =						tokenizer([ARTICLE_TO_SUMMARIZE]       , max_length=1024       , return_tensors="""pt"""   ).to(model.device   )

		A__				:       List[Any]	  =						model.generate(
		    inputs["""input_ids"""]       , attention_mask=inputs["""attention_mask"""]       , num_beams=lowercase_       , max_length=lowercase_       , early_stopping=lowercase_       , decoder_start_token_id=model.config.decoder_start_token_id       , )

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

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

		A__				:       Any	  =						remove_dup_initializers(os.path.abspath(lowercase_   )   )

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

		A__				:       List[str]	  =						onnxruntime.InferenceSession(lowercase_   )
		A__				:       str	  =						ort_sess.run(
		    lowercase_       , {
		        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
		        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
		        """num_beams""": np.array(lowercase_   ),
		        """max_length""": np.array(lowercase_   ),
		        """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 		UpperCamelCase   ()	-> Tuple:
	A__				:       Any	  =						parse_args()
	A__				:       List[str]	  =						5
	A__				:       List[str]	  =						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()

	A__				:       List[Any]	  =						torch.device(args.device   )

	A__  ,  A__				:       Optional[int]	  =						load_model_tokenizer(args.model_name_or_path       , lowercase_   )

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

	model.to(lowercase_   )

	if args.max_length:
		A__				:       Tuple	  =						args.max_length

	if args.num_beams:
		A__				:       Tuple	  =						args.num_beams

	if args.output_file_path:
		A__				:       Tuple	  =						args.output_file_path
	else:
		A__				:       int	  =						"""BART.onnx"""

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


if __name__ == "__main__":
	main()






A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



