

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,
)

_lowerCAmelCase					:			List[Any]  				=			logging.getLogger(__name__)

_lowerCAmelCase					:			Any  				=			{"facebook/bart-base": BartForConditionalGeneration}
_lowerCAmelCase					:			Optional[Any]  				=			{"facebook/bart-base": BartTokenizer}






def        UpperCamelCase_(							):




			"""simple docstring"""






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

			__a        =parser.parse_args()

			return args






def        UpperCamelCase_(							_snake_case   :  List[Any]       ,     _snake_case   :  Dict="cpu"	):




			"""simple docstring"""






			__a        =model_dict[model_name].from_pretrained(_snake_case	).to(_snake_case	)
			__a        =tokenizer_dict[model_name].from_pretrained(_snake_case	)

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

			return huggingface_model, tokenizer






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  Tuple       ,     _snake_case   :  List[Any]	):




			"""simple docstring"""






			model.eval()

			__a        =None
			__a        =torch.jit.script(BARTBeamSearchGenerator(_snake_case	)	)

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

						__a        =model.generate(
						    inputs['input_ids']       ,     attention_mask=inputs['attention_mask']       ,     num_beams=_snake_case       ,     max_length=_snake_case       ,     early_stopping=_snake_case       ,     decoder_start_token_id=model.config.decoder_start_token_id       ,     )

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

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

						__a        =remove_dup_initializers(os.path.abspath(_snake_case	)	)

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

						__a        =onnxruntime.InferenceSession(_snake_case	)
						__a        =ort_sess.run(
						    _snake_case       ,     {
						        'input_ids': inputs['input_ids'].cpu().numpy(),
						        'attention_mask': inputs['attention_mask'].cpu().numpy(),
						        'num_beams': np.array(_snake_case	),
						        'max_length': np.array(_snake_case	),
						        '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_(							):




			"""simple docstring"""






			__a        =parse_args()
			__a        =5
			__a        =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        =torch.device(args.device	)

			__a      ,    __a        =load_model_tokenizer(args.model_name_or_path       ,     _snake_case	)

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

			model.to(_snake_case	)

			if args.max_length:
						__a        =args.max_length

			if args.num_beams:
						__a        =args.num_beams

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

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


if __name__ == "__main__":
						main()









from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






