




'''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,
)

UpperCamelCase_						:      Optional[Any]       =						logging.getLogger(__name__)

UpperCamelCase_						:      List[Any]       =						{'''facebook/bart-base''': BartForConditionalGeneration}
UpperCamelCase_						:      Dict       =						{'''facebook/bart-base''': BartTokenizer}





def      __a     (       )						->			Any:

     """simple docstring"""




     _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."   )

     _snake_case							=				parser.parse_args()

     return args





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Tuple="cpu"   )						->			Dict:

     """simple docstring"""




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

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

     return huggingface_model, tokenizer





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   str ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]   )						->			Tuple:

     """simple docstring"""




     model.eval()

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

     with torch.no_grad():
          _snake_case							=				"My friends are cool but they eat too many carbs."
          _snake_case							=				tokenizer([ARTICLE_TO_SUMMARIZE] ,						max_length=1_024 ,						return_tensors="pt"   ).to(model.device   )

          _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   )   )

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

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

          _snake_case							=				onnxruntime.InferenceSession(_UpperCamelCase   )
          _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      __a     (       )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				parse_args()
     _snake_case							=				5
     _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()

     _snake_case							=				torch.device(args.device   )

     _snake_case							, _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:
          _snake_case							=				args.max_length

     if args.num_beams:
          _snake_case							=				args.num_beams

     if args.output_file_path:
          _snake_case							=				args.output_file_path
     else:
          _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 decimal import Decimal, getcontext
from math import ceil, factorial





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("Undefined for non-integers"   )
     elif precision < 1:
          raise ValueError("Undefined for non-natural numbers"   )

     _snake_case							=				precision
     _snake_case							=				ceil(precision / 14   )
     _snake_case							=				426_880 * Decimal(10_005   ).sqrt()
     _snake_case							=				1
     _snake_case							=				13_591_409
     _snake_case							=				Decimal(_UpperCamelCase   )
     for k in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				factorial(6 * k   ) // (factorial(3 * k   ) * factorial(_UpperCamelCase   ) ** 3)
          linear_term += 545_140_134
          exponential_term *= -262_537_412_640_768_000
          partial_sum += Decimal(multinomial_term * linear_term   ) / exponential_term
     return str(constant_term / partial_sum   )[:-1]


if __name__ == "__main__":
   UpperCamelCase_						:      Any       =						50
   print(F'The first {n} digits of pi is: {pi(n)}')


