






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

a_		:    Tuple   							=     {'facebook/bart-base': BartForConditionalGeneration}
a_		:    Dict   							=     {'facebook/bart-base': BartTokenizer}






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

			SCREAMING_SNAKE_CASE							=			parser.parse_args()

			return args






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase="cpu"):
			SCREAMING_SNAKE_CASE							=			model_dict[model_name].from_pretrained(_UpperCAmelCase).to(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase)

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

			return huggingface_model, tokenizer






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			model.eval()

			SCREAMING_SNAKE_CASE							=			None
			SCREAMING_SNAKE_CASE							=			torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase))

			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=1024				,							return_tensors='pt').to(model.device)

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

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

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

						SCREAMING_SNAKE_CASE							=			remove_dup_initializers(os.path.abspath(_UpperCAmelCase))

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

						SCREAMING_SNAKE_CASE							=			onnxruntime.InferenceSession(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			ort_sess.run(
						    _UpperCAmelCase				,							{
						        'input_ids': inputs['input_ids'].cpu().numpy(),
						        'attention_mask': inputs['attention_mask'].cpu().numpy(),
						        'num_beams': np.array(_UpperCAmelCase),
						        'max_length': np.array(_UpperCAmelCase),
						        '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 					lowerCamelCase__					():
			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				,							_UpperCAmelCase)

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

			model.to(_UpperCAmelCase)

			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(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)


if __name__ == "__main__":
				main()











'''simple docstring'''

from __future__ import annotations

import math





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   float			):

    if depth < 0:
        raise ValueError("Depth cannot be less than 0"			)

    if len(_lowerCamelCase			) == 0:
        raise ValueError("Scores cannot be empty"			)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)

    return min(
        minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)





def 		UpperCamelCase							(				):
    A__							=     [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    A__							=     math.log(len(_lowerCamelCase			)		,			2			)
    print("Optimal value : "		,			end=""			)
    print(minimax(0		,			0		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



