






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor







class 						_UpperCAmelCase	(	unittest.TestCase		):




      """simple docstring"""



      def __init__( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase=7     ,						_lowerCAmelCase=3     ,						_lowerCAmelCase=30     ,						_lowerCAmelCase=400     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=None     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=[0.5, 0.5, 0.5]     ,						_lowerCAmelCase=[0.5, 0.5, 0.5]     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=1 / 255     ,						_lowerCAmelCase=True     ,						):



          '''simple docstring'''
          lowerCAmelCase__ :int   				=					size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
          lowerCAmelCase__ :Dict   				=					parent
          lowerCAmelCase__ :Optional[Any]   				=					batch_size
          lowerCAmelCase__ :int   				=					num_channels
          lowerCAmelCase__ :Optional[int]   				=					min_resolution
          lowerCAmelCase__ :List[Any]   				=					max_resolution
          lowerCAmelCase__ :Optional[int]   				=					do_resize
          lowerCAmelCase__ :List[Any]   				=					size
          lowerCAmelCase__ :List[Any]   				=					do_normalize
          lowerCAmelCase__ :Dict   				=					image_mean
          lowerCAmelCase__ :Dict   				=					image_std
          lowerCAmelCase__ :Any   				=					do_rescale
          lowerCAmelCase__ :Tuple   				=					rescale_factor
          lowerCAmelCase__ :int   				=					do_pad
      def       snake_case_   ( self		):



          '''simple docstring'''
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }



      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase=False		):



          '''simple docstring'''

          if not batched:
              lowerCAmelCase__ :Tuple   				=					image_inputs[0]
              if isinstance(snake_case_     ,						Image.Image		):
                  lowerCAmelCase__				,lowerCAmelCase__ :Union[str, Any]   				=					image.size
              else:
                  lowerCAmelCase__				,lowerCAmelCase__ :List[Any]   				=					image.shape[1], image.shape[2]
              if w < h:
                  lowerCAmelCase__ :int   				=					int(self.size["shortest_edge"] * h / w		)
                  lowerCAmelCase__ :Tuple   				=					self.size["shortest_edge"]
              elif w > h:
                  lowerCAmelCase__ :Dict   				=					self.size["shortest_edge"]
                  lowerCAmelCase__ :Dict   				=					int(self.size["shortest_edge"] * w / h		)
              else:
                  lowerCAmelCase__ :int   				=					self.size["shortest_edge"]
                  lowerCAmelCase__ :Dict   				=					self.size["shortest_edge"]

          else:
              lowerCAmelCase__ :List[Any]   				=					[]
              for image in image_inputs:
                  lowerCAmelCase__				,lowerCAmelCase__ :str   				=					self.get_expected_values([image]		)
                  expected_values.append((expected_height, expected_width)		)
              lowerCAmelCase__ :Optional[int]   				=					max(snake_case_     ,						key=lambda _lowerCAmelCase		: item[0]		)[0]
              lowerCAmelCase__ :List[str]   				=					max(snake_case_     ,						key=lambda _lowerCAmelCase		: item[1]		)[1]

          return expected_height, expected_width







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




      """simple docstring"""



      A    =     DeformableDetrImageProcessor if is_vision_available() else None
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Union[str, Any]   				=					DeformableDetrImageProcessingTester(self		)
      @property
      def       snake_case_   ( self		):



          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :int   				=					self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(snake_case_     ,						"image_mean"		)		)
          self.assertTrue(hasattr(snake_case_     ,						"image_std"		)		)
          self.assertTrue(hasattr(snake_case_     ,						"do_normalize"		)		)
          self.assertTrue(hasattr(snake_case_     ,						"do_resize"		)		)
          self.assertTrue(hasattr(snake_case_     ,						"do_rescale"		)		)
          self.assertTrue(hasattr(snake_case_     ,						"do_pad"		)		)
          self.assertTrue(hasattr(snake_case_     ,						"size"		)		)
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :List[str]   				=					self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size     ,						{"shortest_edge": 18, "longest_edge": 1_333}		)
          self.assertEqual(image_processor.do_pad     ,						snake_case_		)

          lowerCAmelCase__ :List[Any]   				=					self.image_processing_class.from_dict(
              self.image_processor_dict     ,						size=42     ,						max_size=84     ,						pad_and_return_pixel_mask=snake_case_		)
          self.assertEqual(image_processor.size     ,						{"shortest_edge": 42, "longest_edge": 84}		)
          self.assertEqual(image_processor.do_pad     ,						snake_case_		)
      def       snake_case_   ( self		):



          '''simple docstring'''
          pass
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :str   				=					self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          lowerCAmelCase__ :List[str]   				=					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
          lowerCAmelCase__ :int   				=					image_processing(image_inputs[0]     ,						return_tensors="pt"		).pixel_values

          lowerCAmelCase__				,lowerCAmelCase__ :List[Any]   				=					self.image_processor_tester.get_expected_values(snake_case_		)

          self.assertEqual(
              encoded_images.shape     ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,						)

          # Test batched
          lowerCAmelCase__				,lowerCAmelCase__ :List[str]   				=					self.image_processor_tester.get_expected_values(snake_case_     ,						batched=snake_case_		)

          lowerCAmelCase__ :List[str]   				=					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,
                  expected_height,
                  expected_width,
              )     ,						)
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :List[str]   				=					self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          lowerCAmelCase__ :Optional[int]   				=					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
          lowerCAmelCase__ :Dict   				=					image_processing(image_inputs[0]     ,						return_tensors="pt"		).pixel_values

          lowerCAmelCase__				,lowerCAmelCase__ :Optional[Any]   				=					self.image_processor_tester.get_expected_values(snake_case_		)

          self.assertEqual(
              encoded_images.shape     ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,						)

          # Test batched
          lowerCAmelCase__ :int   				=					image_processing(snake_case_     ,						return_tensors="pt"		).pixel_values

          lowerCAmelCase__				,lowerCAmelCase__ :Tuple   				=					self.image_processor_tester.get_expected_values(snake_case_     ,						batched=snake_case_		)

          self.assertEqual(
              encoded_images.shape     ,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,						)
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Any   				=					self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          lowerCAmelCase__ :int   				=					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
          lowerCAmelCase__ :Any   				=					image_processing(image_inputs[0]     ,						return_tensors="pt"		).pixel_values

          lowerCAmelCase__				,lowerCAmelCase__ :Tuple   				=					self.image_processor_tester.get_expected_values(snake_case_		)

          self.assertEqual(
              encoded_images.shape     ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,						)

          # Test batched
          lowerCAmelCase__ :List[Any]   				=					image_processing(snake_case_     ,						return_tensors="pt"		).pixel_values

          lowerCAmelCase__				,lowerCAmelCase__ :int   				=					self.image_processor_tester.get_expected_values(snake_case_     ,						batched=snake_case_		)

          self.assertEqual(
              encoded_images.shape     ,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )     ,						)
      @slow
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :List[Any]   				=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,						"r"		) as f:
              lowerCAmelCase__ :Tuple   				=					json.loads(f.read()		)

          lowerCAmelCase__ :Dict   				=					{"image_id": 39_769, "annotations": target}

          # encode them
          lowerCAmelCase__ :Optional[Any]   				=					DeformableDetrImageProcessor()
          lowerCAmelCase__ :List[Any]   				=					image_processing(images=snake_case_     ,						annotations=snake_case_     ,						return_tensors="pt"		)

          # verify pixel values
          lowerCAmelCase__ :int   				=					torch.Size([1, 3, 800, 1_066]		)
          self.assertEqual(encoding["pixel_values"].shape     ,						snake_case_		)

          lowerCAmelCase__ :List[str]   				=					torch.tensor([0.2796, 0.3138, 0.3481]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,						snake_case_     ,						atol=1e-4		)		)

          # verify area
          lowerCAmelCase__ :Dict   				=					torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,						snake_case_		)		)
          # verify boxes
          lowerCAmelCase__ :Tuple   				=					torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape     ,						snake_case_		)
          lowerCAmelCase__ :List[Any]   				=					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,						snake_case_     ,						atol=1e-3		)		)
          # verify image_id
          lowerCAmelCase__ :int   				=					torch.tensor([39_769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,						snake_case_		)		)
          # verify is_crowd
          lowerCAmelCase__ :str   				=					torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,						snake_case_		)		)
          # verify class_labels
          lowerCAmelCase__ :Union[str, Any]   				=					torch.tensor([75, 75, 63, 65, 17, 17]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,						snake_case_		)		)
          # verify orig_size
          lowerCAmelCase__ :Tuple   				=					torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,						snake_case_		)		)
          # verify size
          lowerCAmelCase__ :Dict   				=					torch.tensor([800, 1_066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,						snake_case_		)		)



      @slow
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :str   				=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
          with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,						"r"		) as f:
              lowerCAmelCase__ :str   				=					json.loads(f.read()		)

          lowerCAmelCase__ :str   				=					{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

          lowerCAmelCase__ :List[Any]   				=					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

          # encode them
          lowerCAmelCase__ :Optional[int]   				=					DeformableDetrImageProcessor(format="coco_panoptic"		)
          lowerCAmelCase__ :Optional[Any]   				=					image_processing(images=snake_case_     ,						annotations=snake_case_     ,						masks_path=snake_case_     ,						return_tensors="pt"		)

          # verify pixel values
          lowerCAmelCase__ :Optional[Any]   				=					torch.Size([1, 3, 800, 1_066]		)
          self.assertEqual(encoding["pixel_values"].shape     ,						snake_case_		)

          lowerCAmelCase__ :Optional[Any]   				=					torch.tensor([0.2796, 0.3138, 0.3481]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,						snake_case_     ,						atol=1e-4		)		)

          # verify area
          lowerCAmelCase__ :Tuple   				=					torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,						snake_case_		)		)
          # verify boxes
          lowerCAmelCase__ :Union[str, Any]   				=					torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape     ,						snake_case_		)
          lowerCAmelCase__ :str   				=					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,						snake_case_     ,						atol=1e-3		)		)
          # verify image_id
          lowerCAmelCase__ :Optional[int]   				=					torch.tensor([39_769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,						snake_case_		)		)
          # verify is_crowd
          lowerCAmelCase__ :List[Any]   				=					torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,						snake_case_		)		)
          # verify class_labels
          lowerCAmelCase__ :List[Any]   				=					torch.tensor([17, 17, 63, 75, 75, 93]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,						snake_case_		)		)
          # verify masks
          lowerCAmelCase__ :Any   				=					822_873
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,						snake_case_		)
          # verify orig_size
          lowerCAmelCase__ :str   				=					torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,						snake_case_		)		)
          # verify size
          lowerCAmelCase__ :str   				=					torch.tensor([800, 1_066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,						snake_case_		)		)







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()


