'''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__  :						Any					=      logging.getLogger(__name__)

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







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

   UpperCAmelCase__				:					int 					=	parser.parse_args()

   return args







def __UpperCamelCase( _A       :					Optional[Any]	,      _A       :					List[str]="cpu"     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	model_dict[model_name].from_pretrained(_A     ).to(_A     )
   UpperCAmelCase__				:					int 					=	tokenizer_dict[model_name].from_pretrained(_A     )

   if model_name in ["facebook/bart-base"]:
      UpperCAmelCase__				:					str 					=	0
      UpperCAmelCase__				:					Tuple 					=	None
      UpperCAmelCase__				:					str 					=	0

   return huggingface_model, tokenizer







def __UpperCamelCase( _A       :					Any	,      _A       :					str	,      _A       :					str	,      _A       :					str	,      _A       :					Tuple     ):
   '''simple docstring'''
   model.eval()

   UpperCAmelCase__				:					Optional[int] 					=	None
   UpperCAmelCase__				:					int 					=	torch.jit.script(BARTBeamSearchGenerator(_A     )     )

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

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

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

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

      UpperCAmelCase__				:					Any 					=	remove_dup_initializers(os.path.abspath(_A     )     )

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

      UpperCAmelCase__				:					str 					=	onnxruntime.InferenceSession(_A     )
      UpperCAmelCase__				:					Union[str, Any] 					=	ort_sess.run(
          _A	,      {
              '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
              '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
              '''num_beams''': np.array(_A     ),
              '''max_length''': np.array(_A     ),
              '''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'''
   UpperCAmelCase__				:					int 					=	parse_args()
   UpperCAmelCase__				:					str 					=	5
   UpperCAmelCase__				:					int 					=	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__				:					List[str] 					=	torch.device(args.device     )

   UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	load_model_tokenizer(args.model_name_or_path	,      _A     )

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

   model.to(_A     )

   if args.max_length:
      UpperCAmelCase__				:					List[str] 					=	args.max_length

   if args.num_beams:
      UpperCAmelCase__				:					Optional[Any] 					=	args.num_beams

   if args.output_file_path:
      UpperCAmelCase__				:					int 					=	args.output_file_path
   else:
      UpperCAmelCase__				:					Optional[int] 					=	'''BART.onnx'''

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


if __name__ == "__main__":
  main()





'''simple docstring'''

from math import factorial, radians







def __UpperCamelCase( _A       :					float	,      _A       :					int = 18	,      _A       :					int = 10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

   # Converting from degrees to radians
   UpperCAmelCase__				:					Dict 					=	radians(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	angle_in_radians
   UpperCAmelCase__				:					List[str] 					=	3
   UpperCAmelCase__				:					int 					=	-1

   for _ in range(_A     ):
      result += (b * (angle_in_radians**a)) / factorial(_A     )

      UpperCAmelCase__				:					List[str] 					=	-b  # One positive term and the next will be negative and so on...
      a += 2  # Increased by 2 for every term.

   return round(_A	,      _A     )


if __name__ == "__main__":
  __import__('doctest').testmod()




