





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	:					int			  =					logging.getLogger(__name__)

__lowerCamelCase	:					Dict			  =					{'facebook/bart-base': BartForConditionalGeneration}
__lowerCamelCase	:					Optional[int]			  =					{'facebook/bart-base': BartTokenizer}



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

 UpperCamelCase     : List[Any]	     =       parser.parse_args()

 return args



def       A_    (					_lowerCAmelCase							,       _lowerCAmelCase="cpu"					)						->			List[str]:
 UpperCamelCase     : Optional[int]	     =       model_dict[model_name].from_pretrained(lowerCamelCase_					).to(lowerCamelCase_					)
 UpperCamelCase     : Dict	     =       tokenizer_dict[model_name].from_pretrained(lowerCamelCase_					)

 if model_name in ["facebook/bart-base"]:
  UpperCamelCase     : Tuple	     =       0
  UpperCamelCase     : Optional[int]	     =       None
  UpperCamelCase     : int	     =       0

 return huggingface_model, tokenizer



def       A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[str]:
 model.eval()

 UpperCamelCase     : Tuple	     =       None
 UpperCamelCase     : Optional[int]	     =       torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_					)					)

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

  UpperCamelCase     : Tuple	     =       model.generate(
      inputs["input_ids"]							,       attention_mask=inputs["attention_mask"]							,       num_beams=lowerCamelCase_							,       max_length=lowerCamelCase_							,       early_stopping=lowerCamelCase_							,       decoder_start_token_id=model.config.decoder_start_token_id							,       )

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

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

  UpperCamelCase     : int	     =       remove_dup_initializers(os.path.abspath(lowerCamelCase_					)					)

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

  UpperCamelCase     : List[str]	     =       onnxruntime.InferenceSession(lowerCamelCase_					)
  UpperCamelCase     : Dict	     =       ort_sess.run(
      lowerCamelCase_							,       {
          "input_ids": inputs["input_ids"].cpu().numpy(),
          "attention_mask": inputs["attention_mask"].cpu().numpy(),
          "num_beams": np.array(lowerCamelCase_					),
          "max_length": np.array(lowerCamelCase_					),
          "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_    (					)						->			int:
 UpperCamelCase     : Any	     =       parse_args()
 UpperCamelCase     : Union[str, Any]	     =       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     : Any	     =       torch.device(args.device					)

 UpperCamelCase     : Dict	     =       load_model_tokenizer(args.model_name_or_path							,       lowerCamelCase_					)

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

 model.to(lowerCamelCase_					)

 if args.max_length:
  UpperCamelCase     : int	     =       args.max_length

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

 if args.output_file_path:
  UpperCamelCase     : Dict	     =       args.output_file_path
 else:
  UpperCamelCase     : Union[str, Any]	     =       '''BART.onnx'''

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


if __name__ == "__main__":
    main()







from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_=None	,							A_=None	,							A_=0       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       1.0 if scale is None else scale
       UpperCamelCase     : Optional[int]	     =       0.0 if loc is None else loc

       super().__init__(A_	,							[AffineTransform(loc=self.loc	,							scale=self.scale	,							event_dim=A_       )]       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.base_dist.mean * self.scale + self.loc





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.base_dist.variance * self.scale**2





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.variance.sqrt()



class  A__       (	nn.Module      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : Union[str, Any]	     =       args_dim
       UpperCamelCase     : str	     =       nn.ModuleList([nn.Linear(A_	,							A_       ) for dim in args_dim.values()]       )
       UpperCamelCase     : Union[str, Any]	     =       domain_map





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       [proj(A_       ) for proj in self.proj]

       return self.domain_map(*A_       )



class  A__       (	nn.Module      ):





      def __init__(			self	,							A_       ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase     : str	     =       function





      def 	__UpperCamelCase(			self	,							A_	,							*A_       ):
       '''simple docstring'''




       return self.function(A_	,							*A_       )



class  A__       :
      _UpperCAmelCase     :type
      _UpperCAmelCase     :int
      _UpperCAmelCase     :Dict[str, int]





      def __init__(			self	,							A_ = 1       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       dim
       UpperCamelCase     : Union[str, Any]	     =       {k: dim * self.args_dim[k] for k in self.args_dim}





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.dim == 1:
        return self.distribution_class(*A_       )
       else:
        return Independent(self.distribution_class(*A_       )	,							1       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None	,							):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self._base_distribution(A_       )
       if loc is None and scale is None:
        return distr
       else:
        return AffineTransformed(A_	,							loc=A_	,							scale=A_	,							event_dim=self.event_dim       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return () if self.dim == 1 else (self.dim,)





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return len(self.event_shape       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 0.0





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return ParameterProjection(
           in_features=A_	,							args_dim=self.args_dim	,							domain_map=LambdaLayer(self.domain_map       )	,							)





      def 	__UpperCamelCase(			self	,							*A_       ):
       '''simple docstring'''




       raise NotImplementedError()





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




       return (x + torch.sqrt(torch.square(A_       ) + 4.0       )) / 2.0



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"df": 1, "loc": 1, "scale": 1}
      _UpperCAmelCase     :type						   =       StudentT





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       cls.squareplus(A_       ).clamp_min(torch.finfo(scale.dtype       ).eps       )
       UpperCamelCase     : int	     =       2.0 + cls.squareplus(A_       )
       return df.squeeze(-1       ), loc.squeeze(-1       ), scale.squeeze(-1       )



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"loc": 1, "scale": 1}
      _UpperCAmelCase     :type						   =       Normal





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       cls.squareplus(A_       ).clamp_min(torch.finfo(scale.dtype       ).eps       )
       return loc.squeeze(-1       ), scale.squeeze(-1       )



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"total_count": 1, "logits": 1}
      _UpperCAmelCase     :type						   =       NegativeBinomial





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       cls.squareplus(A_       )
       return total_count.squeeze(-1       ), logits.squeeze(-1       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       distr_args
       if self.dim == 1:
        return self.distribution_class(total_count=A_	,							logits=A_       )
       else:
        return Independent(self.distribution_class(total_count=A_	,							logits=A_       )	,							1       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Any	     =       distr_args

       if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

       return self._base_distribution((total_count, logits)       )
