


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			:			Union[str, Any]												=					logging.getLogger(__name__)

a			:			Any												=					{'''facebook/bart-base''': BartForConditionalGeneration}
a			:			int												=					{'''facebook/bart-base''': BartTokenizer}


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = 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.'''	)

			__lowercase			       = parser.parse_args()

			return args


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase="cpu"	):


			'''simple docstring'''







			__lowercase			       = model_dict[model_name].from_pretrained(_UpperCamelCase	).to(_UpperCamelCase	)
			__lowercase			       = tokenizer_dict[model_name].from_pretrained(_UpperCamelCase	)

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

			return huggingface_model, tokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			model.eval()

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

			with torch.no_grad():
						__lowercase			       = '''My friends are cool but they eat too many carbs.'''
						__lowercase			       = tokenizer([ARTICLE_TO_SUMMARIZE]							,      max_length=10_24							,      return_tensors='''pt'''	).to(model.device	)

						__lowercase			       = 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	)	)

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

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

						__lowercase			       = onnxruntime.InferenceSession(_UpperCamelCase	)
						__lowercase			       = 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 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = parse_args()
			__lowercase			       = 5
			__lowercase			       = 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()

			__lowercase			       = torch.device(args.device	)

			__lowercase       ,			__lowercase			       = 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:
						__lowercase			       = args.max_length

			if args.num_beams:
						__lowercase			       = args.num_beams

			if args.output_file_path:
						__lowercase			       = args.output_file_path
			else:
						__lowercase			       = '''BART.onnx'''

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


if __name__ == "__main__":
	main()






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DonutImageProcessor
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=1_8			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = image_size
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase			       = do_thumbnail
								__lowercase			       = do_align_axis
								__lowercase			       = do_pad
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DonutImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = DonutImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(snake_case_			,							'''do_resize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''size'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_thumbnail'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_align_long_axis'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,							{'''height''': 1_8, '''width''': 2_0}    )

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=4_2    )
								self.assertEqual(image_processor.size			,							{'''height''': 4_2, '''width''': 4_2}    )

								# Previous config had dimensions in (width, height) order
								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=(4_2, 8_4)    )
								self.assertEqual(image_processor.size			,							{'''height''': 8_4, '''width''': 4_2}    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					@is_flaky()
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							Image.Image    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							numpify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							np.ndarray    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							torchify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							torch.Tensor    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)


