


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__				:	Dict					       =					logging.getLogger(__name__)

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


def    __lowerCamelCase   (      )     ->    Any:
   lowerCamelCase_						:   List[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."""					)

   lowerCamelCase_						:   str		= parser.parse_args()

   return args


def    __lowerCamelCase   (      A__						: int						,						A__						: List[str]="cpu"					)     ->    Optional[Any]:
   lowerCamelCase_						:   List[str]		= model_dict[model_name].from_pretrained(A__					).to(A__					)
   lowerCamelCase_						:   Any		= tokenizer_dict[model_name].from_pretrained(A__					)

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

   return huggingface_model, tokenizer


def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: List[str]						,						A__						: int						,						A__						: List[str]						,						A__						: List[str]					)     ->    Tuple:
   model.eval()

   lowerCamelCase_						:   Dict		= None
   lowerCamelCase_						:   str		= torch.jit.script(BARTBeamSearchGenerator(A__					)					)

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

      lowerCamelCase_						:   Optional[int]		= 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__					)					)

      lowerCamelCase_						:   Tuple		= remove_dup_initializers(os.path.abspath(A__					)					)

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

      lowerCamelCase_						:   Any		= onnxruntime.InferenceSession(A__					)
      lowerCamelCase_						:   int		= 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    __lowerCamelCase   (      )     ->    Dict:
   lowerCamelCase_						:   Tuple		= parse_args()
   lowerCamelCase_						:   Union[str, Any]		= 5
   lowerCamelCase_						:   Optional[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()

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

   lowerCamelCase_,	lowerCamelCase_						:   List[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:
      lowerCamelCase_						:   List[Any]		= args.max_length

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

   if args.output_file_path:
      lowerCamelCase_						:   Optional[int]		= args.output_file_path
   else:
      lowerCamelCase_						:   int		= """BART.onnx"""

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


if __name__ == "__main__":
      main()









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
      import torch


snake_case__				:	int					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				Optional[Any]=False		,				__a				:				Tuple=False		,				__a				:				Tuple=6.0		,				__a				:				str=None		,				__a				:				List[str]=False		,				__a				:				str=False		,				__a				:				Optional[Any]=None		,				__a				:				Dict="fp4"		,				__a				:				Optional[int]=False		,				**__a				:				Dict		,				)					->str:
          lowerCamelCase_						:   str		= load_in_abit
          lowerCamelCase_						:   Union[str, Any]		= load_in_abit
          lowerCamelCase_						:   Tuple		= llm_inta_threshold
          lowerCamelCase_						:   int		= llm_inta_skip_modules
          lowerCamelCase_						:   List[str]		= llm_inta_enable_fpaa_cpu_offload
          lowerCamelCase_						:   Optional[Any]		= llm_inta_has_fpaa_weight
          lowerCamelCase_						:   Dict		= bnb_abit_quant_type
          lowerCamelCase_						:   int		= bnb_abit_use_double_quant

          if bnb_abit_compute_dtype is None:
             lowerCamelCase_						:   int		= torch.floataa
          elif isinstance(__a		,				__a							):
             lowerCamelCase_						:   Tuple		= getattr(__a		,				__a							)
          elif isinstance(__a		,				torch.dtype							):
             lowerCamelCase_						:   List[str]		= bnb_abit_compute_dtype
          else:
             raise ValueError("""bnb_4bit_compute_dtype must be a string or a torch.dtype"""							)

          self.post_init()



       def 				_lowerCAmelCase							(						self				:				Any							)					->Union[str, Any]:
          if not isinstance(self.llm_inta_threshold		,				__a							):
             raise ValueError("""llm_int8_threshold must be a float"""							)

          if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,				__a							):
             raise ValueError("""llm_int8_skip_modules must be a list of strings"""							)
          if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,				__a							):
             raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean"""							)

          if not isinstance(self.llm_inta_has_fpaa_weight		,				__a							):
             raise ValueError("""llm_int8_has_fp16_weight must be a boolean"""							)

          if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,				torch.dtype							):
             raise ValueError("""bnb_4bit_compute_dtype must be torch.dtype"""							)

          if not isinstance(self.bnb_abit_quant_type		,				__a							):
             raise ValueError("""bnb_4bit_quant_type must be a string"""							)

          if not isinstance(self.bnb_abit_use_double_quant		,				__a							):
             raise ValueError("""bnb_4bit_use_double_quant must be a boolean"""							)

          if self.load_in_abit and not version.parse(importlib.metadata.version("""bitsandbytes"""							)							) >= version.parse(
              """0.39.0"""							):
             raise ValueError(
                 """4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"""							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          return self.load_in_abit or self.load_in_abit



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->List[Any]:
          if self.load_in_abit:
             return "llm_int8"
          elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
             return "fp4"
          elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
             return "nf4"
          else:
             return None



       @classmethod
       def 				_lowerCAmelCase							(						cls				:				int		,				__a				:				Dict		,				__a				:				str		,				**__a				:				Any							)					->Optional[Any]:
          lowerCamelCase_						:   Optional[Any]		= cls(**__a							)

          lowerCamelCase_						:   List[Any]		= []
          for key, value in kwargs.items():
             if hasattr(__a		,				__a							):
                setattr(__a		,				__a		,				__a							)
                to_remove.append(__a							)
          for key in to_remove:
             kwargs.pop(__a		,				__a							)

          if return_unused_kwargs:
             return config, kwargs
          else:
             return config



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Union[str, os.PathLike]							)					->Any:
          with open(__a		,				"""w"""		,				encoding="""utf-8"""							) as writer:
             lowerCamelCase_						:   List[Any]		= self.to_dict()
             lowerCamelCase_						:   Dict		= json.dumps(__a		,				indent=2		,				sort_keys=__a							) + """\n"""

             writer.write(__a							)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Dict[str, Any]:
          lowerCamelCase_						:   Dict		= copy.deepcopy(self.__dict__							)
          lowerCamelCase_						:   Tuple		= str(output["""bnb_4bit_compute_dtype"""]							).split("""."""							)[1]

          return output



       def __repr__(						self				:				List[str]							)					->Any:
          return F'''{self.__class__.__name__} {self.to_json_string()}'''



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				bool = True							)					->str:
          if use_diff is True:
             lowerCamelCase_						:   Dict		= self.to_diff_dict()
          else:
             lowerCamelCase_						:   Any		= self.to_dict()
          return json.dumps(__a		,				indent=2		,				sort_keys=__a							) + "\n"




       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Dict[str, Any]:
          lowerCamelCase_						:   Any		= self.to_dict()

          # get the default config dict
          lowerCamelCase_						:   List[str]		= BitsAndBytesConfig().to_dict()

          lowerCamelCase_						:   str		= {}

          # only serialize values that differ from the default config
          for key, value in config_dict.items():
             if value != default_config_dict[key]:
                lowerCamelCase_						:   List[Any]		= value

          return serializable_config_dict





