


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            =   logging.getLogger(__name__)

__snake_case            =   {"facebook/bart-base": BartForConditionalGeneration}
__snake_case            =   {"facebook/bart-base": BartTokenizer}






def 							_lowercase    (		):





			"""simple docstring"""




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

			UpperCamelCase         =					parser.parse_args()

			return args






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Dict="cpu"	):





			"""simple docstring"""




			UpperCamelCase         =					model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_	)

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

			return huggingface_model, tokenizer






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




			model.eval()

			UpperCamelCase         =					None
			UpperCamelCase         =					torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE_	)	)

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

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

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

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

						UpperCamelCase         =					remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE_	)	)

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

						UpperCamelCase         =					onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					ort_sess.run(
						    SCREAMING_SNAKE_CASE_       ,  {
						        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
						        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
						        """num_beams""": np.array(SCREAMING_SNAKE_CASE_	),
						        """max_length""": np.array(SCREAMING_SNAKE_CASE_	),
						        """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 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					parse_args()
			UpperCamelCase         =					5
			UpperCamelCase         =					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         =					torch.device(args.device	)

			UpperCamelCase  ,  UpperCamelCase         =					load_model_tokenizer(args.model_name_or_path       ,  SCREAMING_SNAKE_CASE_	)

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

			model.to(SCREAMING_SNAKE_CASE_	)

			if args.max_length:
						UpperCamelCase         =					args.max_length

			if args.num_beams:
						UpperCamelCase         =					args.num_beams

			if args.output_file_path:
						UpperCamelCase         =					args.output_file_path
			else:
						UpperCamelCase         =					"""BART.onnx"""

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


if __name__ == "__main__":
			main()








import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""						)
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""						)

						super().__init__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




			def __call__(						self							:		int       ,       __magic_name__							:		Optional[ImageInput] = None       ,       __magic_name__							:		Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = False       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

						if text is not None:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.tokenizer.batch_decode(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return self.tokenizer.decode(*__magic_name__       ,       **__magic_name__						)




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,       __magic_name__       ,       )
						return self.image_processor_class





			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,       __magic_name__       ,       )
						return self.image_processor




