



"""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,
)

A:   List[Any]								=		logging.getLogger(__name__)

A:   Any								=		{"facebook/bart-base": BartForConditionalGeneration}
A:   List[str]								=		{"facebook/bart-base": BartTokenizer}

def       _snake_case						(						):
 UpperCAmelCase	:					List[Any]	=	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."""						)

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

 return args

def       _snake_case						(						UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any="cpu"						):
 UpperCAmelCase	:					Optional[int]	=	model_dict[model_name].from_pretrained(UpperCamelCase						).to(UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	tokenizer_dict[model_name].from_pretrained(UpperCamelCase						)

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

 return huggingface_model, tokenizer

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Any						):
 model.eval()

 UpperCAmelCase	:					int	=	None
 UpperCAmelCase	:					Tuple	=	torch.jit.script(BARTBeamSearchGenerator(UpperCamelCase						)						)

 with torch.no_grad():
  UpperCAmelCase	:					str	=	"""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=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						)						)

  UpperCAmelCase	:					Optional[int]	=	remove_dup_initializers(os.path.abspath(UpperCamelCase						)						)

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

  UpperCAmelCase	:					List[Any]	=	onnxruntime.InferenceSession(UpperCamelCase						)
  UpperCAmelCase	:					Optional[Any]	=	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       _snake_case						(						):
 UpperCAmelCase	:					List[Any]	=	parse_args()
 UpperCAmelCase	:					Dict	=	5
 UpperCAmelCase	:					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	:					Optional[Any]	=	torch.device(args.device						)

 UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	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:
  UpperCAmelCase	:					Any	=	args.max_length

 if args.num_beams:
  UpperCAmelCase	:					int	=	args.num_beams

 if args.output_file_path:
  UpperCAmelCase	:					Tuple	=	args.output_file_path
 else:
  UpperCAmelCase	:					Union[str, Any]	=	"""BART.onnx"""

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


if __name__ == "__main__":
      main()





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
