"""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,
)

__lowerCAmelCase     :				Dict    				=    logging.getLogger(__name__)

__lowerCAmelCase     :				Optional[int]    				=    {"facebook/bart-base": BartForConditionalGeneration}
__lowerCAmelCase     :				Any    				=    {"facebook/bart-base": BartTokenizer}







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = 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."""  )

		lowerCAmelCase__   = parser.parse_args()

		return args







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__="cpu"  ):





		"""simple docstring"""




		lowerCAmelCase__   = model_dict[model_name].from_pretrained(lowerCamelCase__  ).to(lowerCamelCase__  )
		lowerCAmelCase__   = tokenizer_dict[model_name].from_pretrained(lowerCamelCase__  )

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

		return huggingface_model, tokenizer







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		model.eval()

		lowerCAmelCase__   = None
		lowerCAmelCase__   = torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase__  )  )

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

				lowerCAmelCase__   = 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__  )  )

				lowerCAmelCase__   = remove_dup_initializers(os.path.abspath(lowerCamelCase__  )  )

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

				lowerCAmelCase__   = onnxruntime.InferenceSession(lowerCamelCase__  )
				lowerCAmelCase__   = 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 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = parse_args()
		lowerCAmelCase__   = 5
		lowerCAmelCase__   = 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__   = torch.device(args.device  )

		lowerCAmelCase__ ,       lowerCAmelCase__   = 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:
				lowerCAmelCase__   = args.max_length

		if args.num_beams:
				lowerCAmelCase__   = args.num_beams

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

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


if __name__ == "__main__":
			main()





"""simple docstring"""




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return getitem, k







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return setitem, k, v







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return delitem, k







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  ):





		"""simple docstring"""




		try:
				return fun(lowerCamelCase__      ,	*lowerCamelCase__  ), None
		except Exception as e:
				return None, e


__lowerCAmelCase     :				Optional[Any]    				=    (
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

__lowerCAmelCase     :				Optional[int]    				=    [
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

__lowerCAmelCase     :				List[Any]    				=    [
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

__lowerCAmelCase     :				int    				=    [
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

__lowerCAmelCase     :				Optional[Any]    				=    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__lowerCAmelCase     :				Any    				=    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]







@pytest.mark.parametrize(
    """operations"""      ,	(
        pytest.param(_add_items      ,	id="""add items"""  ),
        pytest.param(_overwrite_items      ,	id="""overwrite items"""  ),
        pytest.param(_delete_items      ,	id="""delete items"""  ),
        pytest.param(_access_absent_items      ,	id="""access absent items"""  ),
        pytest.param(_add_with_resize_up      ,	id="""add with resize up"""  ),
        pytest.param(_add_with_resize_down      ,	id="""add with resize down"""  ),
    )      ,	)
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = HashMap(initial_block_size=4  )
		lowerCAmelCase__   = {}
		for _, (fun, *args) in enumerate(lowerCamelCase__  ):
				lowerCAmelCase__ ,       lowerCAmelCase__   = _run_operation(lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  )
				lowerCAmelCase__ ,       lowerCAmelCase__   = _run_operation(lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  )
				assert my_res == py_res
				assert str(lowerCamelCase__  ) == str(lowerCamelCase__  )
				assert set(lowerCamelCase__  ) == set(lowerCamelCase__  )
				assert len(lowerCamelCase__  ) == len(lowerCamelCase__  )
				assert set(my.items()  ) == set(py.items()  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		def is_public(lowerCamelCase__  ) -> bool:
				return not name.startswith("""_"""  )

		lowerCAmelCase__   = {name for name in dir({}  ) if is_public(lowerCamelCase__  )}
		lowerCAmelCase__   = {name for name in dir(HashMap()  ) if is_public(lowerCamelCase__  )}

		assert dict_public_names > hash_public_names




