





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 :    Optional[int]  							= logging.getLogger(__name__)

_lowerCamelCase :    Any  							= {'''facebook/bart-base''': BartForConditionalGeneration}
_lowerCamelCase :    Union[str, Any]  							= {'''facebook/bart-base''': BartTokenizer}




def _a (  )	->    str:

							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__       :      Dict     =						parser.parse_args()

							return args




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict="cpu"       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__       )

							if model_name in ["facebook/bart-base"]:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
														SCREAMING_SNAKE_CASE__       :      Tuple     =						None
														SCREAMING_SNAKE_CASE__       :      Dict     =						0

							return huggingface_model, tokenizer




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  List[str]       )	->    Dict:

							'''simple docstring'''






							model.eval()

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						None
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE__       )       )

							with torch.no_grad():
														SCREAMING_SNAKE_CASE__       :      List[str]     =						"My friends are cool but they eat too many carbs."
														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer([ARTICLE_TO_SUMMARIZE]			,				max_length=10_24			,				return_tensors="pt"       ).to(model.device       )

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

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

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

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE__       )       )

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

														SCREAMING_SNAKE_CASE__       :      str     =						onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ort_sess.run(
														    SCREAMING_SNAKE_CASE__			,				{
														        "input_ids": inputs["input_ids"].cpu().numpy(),
														        "attention_mask": inputs["attention_mask"].cpu().numpy(),
														        "num_beams": np.array(SCREAMING_SNAKE_CASE__       ),
														        "max_length": np.array(SCREAMING_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 _a (  )	->    List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						parse_args()
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						5
							SCREAMING_SNAKE_CASE__       :      Tuple     =						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__       :      Optional[Any]     =						torch.device(args.device       )

							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						load_model_tokenizer(args.model_name_or_path			,				SCREAMING_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(SCREAMING_SNAKE_CASE__       )

							if args.max_length:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						args.max_length

							if args.num_beams:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						args.num_beams

							if args.output_file_path:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						args.output_file_path
							else:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"BART.onnx"

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


if __name__ == "__main__":
		main()













class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						name
														SCREAMING_SNAKE_CASE__       :      Tuple     =						val






							def __str__( self     :					str						)				->			List[Any]:





														"""simple docstring"""
														return F'''{self.__class__.__name__}({self.name}, {self.val})'''






							def __lt__( self     :					str,			_UpperCAmelCase     :					Dict						)				->			Union[str, Any]:





														"""simple docstring"""
														return self.val < other.val


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					str						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{}
														SCREAMING_SNAKE_CASE__       :      str     =						{}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.build_heap(_UpperCAmelCase						)






							def __getitem__( self     :					Union[str, Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return self.get_value(_UpperCAmelCase						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[int]						)				->			Any:





														"""simple docstring"""
														return (idx - 1) // 2






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return idx * 2 + 1






							def  A_     ( self     :					str,			_UpperCAmelCase     :					List[str]						)				->			Union[str, Any]:





														"""simple docstring"""
														return idx * 2 + 2






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int						)				->			Optional[int]:





														"""simple docstring"""
														return self.heap_dict[key]






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Dict						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						len(_UpperCAmelCase						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_parent_idx(_UpperCAmelCase						)

														for idx, i in enumerate(_UpperCAmelCase						):
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						idx
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						i.val

														for i in range(_UpperCAmelCase,			-1,			-1						):
																					self.sift_down(_UpperCAmelCase,			_UpperCAmelCase						)
														return array






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Tuple						)				->			Tuple:





														"""simple docstring"""
														while True:
																					SCREAMING_SNAKE_CASE__       :      str     =						self.get_left_child_idx(_UpperCAmelCase						)  # noqa: E741
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_right_child_idx(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						idx
																					if l < len(_UpperCAmelCase						) and array[l] < array[idx]:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						l
																					if r < len(_UpperCAmelCase						) and array[r] < array[smallest]:
																												SCREAMING_SNAKE_CASE__       :      int     =						r

																					if smallest != idx:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						array[smallest], array[idx]
																												(
																												    (
																												    SCREAMING_SNAKE_CASE__
																												)   ,(
																												    SCREAMING_SNAKE_CASE__
																												)   ,
																												)       :      Optional[Any]     =						(
																												    self.idx_of_element[array[smallest]],
																												    self.idx_of_element[array[idx]],
																												)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						smallest
																					else:
																												break






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.get_parent_idx(_UpperCAmelCase						)
														while p >= 0 and self.heap[p] > self.heap[idx]:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						self.heap[idx], self.heap[p]
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						(
																					    self.idx_of_element[self.heap[idx]],
																					    self.idx_of_element[self.heap[p]],
																					)
																					SCREAMING_SNAKE_CASE__       :      Dict     =						p
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_parent_idx(_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.heap[0]






							def  A_     ( self     :					Tuple						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.heap[-1], self.heap[0]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						(
														    self.idx_of_element[self.heap[-1]],
														    self.idx_of_element[self.heap[0]],
														)

														SCREAMING_SNAKE_CASE__       :      Any     =						self.heap.pop()
														del self.idx_of_element[x]
														self.sift_down(0,			self.heap						)
														return x






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														self.heap.append(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						len(self.heap						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node.val
														self.sift_up(len(self.heap						) - 1						)






							def  A_     ( self     :					Optional[int]						)				->			int:





														"""simple docstring"""
														return len(self.heap						) == 0






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					str						)				->			Dict:





														"""simple docstring"""
														assert (
														    self.heap[self.idx_of_element[node]].val > new_value
														), "newValue must be less that current value"
														SCREAMING_SNAKE_CASE__       :      Tuple     =						new_value
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_value
														self.sift_up(self.idx_of_element[node]						)


_lowerCamelCase :    Tuple  							= Node('''R''', -1)
_lowerCamelCase :    int  							= Node('''B''', 6)
_lowerCamelCase :    str  							= Node('''A''', 3)
_lowerCamelCase :    Optional[Any]  							= Node('''X''', 1)
_lowerCamelCase :    str  							= Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCamelCase :    int  							= MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
		print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()






