





'''simple docstring'''







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,
)

snake_case_     :       Any						=  logging.getLogger(__name__)

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





def        lowerCamelCase_      (  ) ->	Optional[int]:
						UpperCAmelCase_			:      List[str]     						=  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.'''   )

						UpperCAmelCase_			:      Optional[int]     						=  parser.parse_args()

						return args





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any]="cpu"   ) ->	Tuple:
						UpperCAmelCase_			:      Tuple     						=  model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[str]     						=  tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__   )

						if model_name in ["facebook/bart-base"]:
												UpperCAmelCase_			:      Optional[Any]     						=  0
												UpperCAmelCase_			:      Any     						=  None
												UpperCAmelCase_			:      List[Any]     						=  0

						return huggingface_model, tokenizer





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	Any:
						model.eval()

						UpperCAmelCase_			:      Optional[Any]     						=  None
						UpperCAmelCase_			:      Union[str, Any]     						=  torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE__   )   )

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

												UpperCAmelCase_			:      Optional[int]     						=  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__   )   )

												UpperCAmelCase_			:      Union[str, Any]     						=  remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE__   )   )

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

												UpperCAmelCase_			:      Dict     						=  onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[int]     						=  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        lowerCamelCase_      (  ) ->	Optional[Any]:
						UpperCAmelCase_			:      List[Any]     						=  parse_args()
						UpperCAmelCase_			:      List[str]     						=  5
						UpperCAmelCase_			:      Optional[Any]     						=  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()

						UpperCAmelCase_			:      Dict     						=  torch.device(args.device   )

						UpperCAmelCase_						,       UpperCAmelCase_			:      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:
												UpperCAmelCase_			:      Tuple     						=  args.max_length

						if args.num_beams:
												UpperCAmelCase_			:      Optional[int]     						=  args.num_beams

						if args.output_file_path:
												UpperCAmelCase_			:      Optional[int]     						=  args.output_file_path
						else:
												UpperCAmelCase_			:      Dict     						=  '''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()












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[int]]:
						UpperCAmelCase_			:      int     						=  []
						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__   )   ):
												UpperCAmelCase_			:      List[Any]     						=  nums.pop(0   )
												UpperCAmelCase_			:      Optional[Any]     						=  permute(SCREAMING_SNAKE_CASE__   )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__   )
												result.extend(SCREAMING_SNAKE_CASE__   )
												nums.append(SCREAMING_SNAKE_CASE__   )
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Any:

						def backtrack(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												if start == len(SCREAMING_SNAKE_CASE__   ) - 1:
																		output.append(nums[:]   )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__,			len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  nums[i], nums[start]
																								backtrack(start + 1   )
																								UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  nums[i], nums[start]  # backtrack

						UpperCAmelCase_			:      Optional[int]     						=  []
						backtrack(0   )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					snake_case_     :       Tuple						=  permutea([1, 2, 3])
					print(res)
					doctest.testmod()





