





'''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_ : Optional[Any]	  =       logging.getLogger(__name__)

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







def  _lowerCamelCase	() ->     List[str]:
			a__	 =			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."  )

			a__	 =			parser.parse_args()

			return args







def  _lowerCamelCase	(__lowerCamelCase	:  Optional[Any]       ,	__lowerCamelCase	:  str="cpu"  ) ->     int:
			a__	 =			model_dict[model_name].from_pretrained(_UpperCamelCase  ).to(_UpperCamelCase  )
			a__	 =			tokenizer_dict[model_name].from_pretrained(_UpperCamelCase  )

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

			return huggingface_model, tokenizer







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

			a__	 =			None
			a__	 =			torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase  )  )

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

						a__	 =			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  )  )

						a__	 =			remove_dup_initializers(os.path.abspath(_UpperCamelCase  )  )

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

						a__	 =			onnxruntime.InferenceSession(_UpperCamelCase  )
						a__	 =			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  _lowerCamelCase	() ->     str:
			a__	 =			parse_args()
			a__	 =			5
			a__	 =			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()

			a__	 =			torch.device(args.device  )

			a__ ,					a__	 =			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:
						a__	 =			args.max_length

			if args.num_beams:
						a__	 =			args.num_beams

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

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


if __name__ == "__main__":
		main()








import os
import sys
import unittest


a_								=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a_								=       os.path.join(git_repo_path, """src""", """transformers""")

a_								=       """
{0} = None
"""

a_								=       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


a_								=       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''					)
    self.assertIsNone(__UpperCAmelCase					)

    __lowerCamelCase    				=     find_backend('''    if not is_tokenizers_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tokenizers'''					)

    __lowerCamelCase    				=     find_backend('''    if not is_tensorflow_text_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers_and_vision'''					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     read_init()
    # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
    self.assertIn('''torch'''						,					__UpperCAmelCase					)
    self.assertIn('''tensorflow_text'''						,					__UpperCAmelCase					)
    self.assertIn('''sentencepiece_and_tokenizers'''						,					__UpperCAmelCase					)

    # Likewise, we can't assert on the exact content of a key
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertModel'''						,					objects['''tf''']					)
    self.assertIn('''FlaxBertModel'''						,					objects['''flax''']					)
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertTokenizer'''						,					objects['''tensorflow_text''']					)
    self.assertIn('''convert_slow_tokenizer'''						,					objects['''sentencepiece_and_tokenizers''']					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     create_dummy_object('''CONSTANT'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					'''\nCONSTANT = None\n'''					)

    __lowerCamelCase    				=     create_dummy_object('''function'''						,					'''\'torch\''''					)
    self.assertEqual(
        __UpperCAmelCase						,					'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''					)

    __lowerCamelCase    				=     '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
    __lowerCamelCase    				=     create_dummy_object('''FakeClass'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
    __lowerCamelCase    				=     create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}					)
    self.assertEqual(dummy_files['''torch''']						,					__UpperCAmelCase					)






