



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

__snake_case       :	List[str]            ={'facebook/bart-base': BartForConditionalGeneration}
__snake_case       :	List[str]            ={'facebook/bart-base': BartTokenizer}






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  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__  :       Tuple				=  parser.parse_args()

				return args






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict="cpu"):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  model_dict[model_name].from_pretrained(lowerCamelCase_).to(lowerCamelCase_)
				lowerCAmelCase__  :       Dict				=  tokenizer_dict[model_name].from_pretrained(lowerCamelCase_)

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

				return huggingface_model, tokenizer






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int] ,lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				model.eval()

				lowerCAmelCase__  :       Optional[int]				=  None
				lowerCAmelCase__  :       List[str]				=  torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_))

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

								lowerCAmelCase__  :       Any				=  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__  :       Any				=  remove_dup_initializers(os.path.abspath(lowerCamelCase_))

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

								lowerCAmelCase__  :       List[Any]				=  onnxruntime.InferenceSession(lowerCamelCase_)
								lowerCAmelCase__  :       Optional[int]				=  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 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  parse_args()
				lowerCAmelCase__  :       Any				=  5
				lowerCAmelCase__  :       Union[str, Any]				=  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__  :       Tuple				=  torch.device(args.device)

				lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  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__  :       List[Any]				=  args.max_length

				if args.num_beams:
								lowerCAmelCase__  :       Optional[int]				=  args.num_beams

				if args.output_file_path:
								lowerCAmelCase__  :       Optional[int]				=  args.output_file_path
				else:
								lowerCAmelCase__  :       int				=  '''BART.onnx'''

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


if __name__ == "__main__":
							main()






import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
							from transformers.models.mobilevitva.modeling_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
							    make_divisible,
							)


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.config_class(**self.inputs_dict				)
						self.parent.assertTrue(hasattr(__lowerCamelCase	,'''width_multiplier'''				)				)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=64	,__lowerCamelCase=2	,__lowerCamelCase=3	,__lowerCamelCase="swish"	,__lowerCamelCase=3	,__lowerCamelCase=32	,__lowerCamelCase=0.1	,__lowerCamelCase=0.02	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=10	,__lowerCamelCase=None	,__lowerCamelCase=0.25	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  parent
						lowerCAmelCase__  :       List[str]				=  batch_size
						lowerCAmelCase__  :       Dict				=  image_size
						lowerCAmelCase__  :       Tuple				=  patch_size
						lowerCAmelCase__  :       List[Any]				=  num_channels
						lowerCAmelCase__  :       Tuple				=  make_divisible(5_12 * width_multiplier	,divisor=8				)
						lowerCAmelCase__  :       Dict				=  hidden_act
						lowerCAmelCase__  :       Optional[int]				=  conv_kernel_size
						lowerCAmelCase__  :       int				=  output_stride
						lowerCAmelCase__  :       List[Any]				=  classifier_dropout_prob
						lowerCAmelCase__  :       Dict				=  use_labels
						lowerCAmelCase__  :       Tuple				=  is_training
						lowerCAmelCase__  :       str				=  num_labels
						lowerCAmelCase__  :       Optional[Any]				=  initializer_range
						lowerCAmelCase__  :       Tuple				=  scope
						lowerCAmelCase__  :       Tuple				=  width_multiplier
						lowerCAmelCase__  :       Optional[int]				=  ffn_dropout
						lowerCAmelCase__  :       Dict				=  attn_dropout



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowerCAmelCase__  :       Union[str, Any]				=  None
						lowerCAmelCase__  :       Dict				=  None
						if self.use_labels:
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size]	,self.num_labels				)
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels				)

						lowerCAmelCase__  :       Tuple				=  self.get_config()

						return config, pixel_values, labels, pixel_labels



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

						"""simple docstring"""






						return MobileViTVaConfig(
						    image_size=self.image_size	,patch_size=self.patch_size	,num_channels=self.num_channels	,hidden_act=self.hidden_act	,conv_kernel_size=self.conv_kernel_size	,output_stride=self.output_stride	,classifier_dropout_prob=self.classifier_dropout_prob	,initializer_range=self.initializer_range	,width_multiplier=self.width_multiplier	,ffn_dropout=self.ffn_dropout_prob	,attn_dropout=self.attn_dropout_prob	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  MobileViTVaModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase				)
						self.parent.assertEqual(
						    result.last_hidden_state.shape	,(
						        self.batch_size,
						        self.last_hidden_size,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.num_labels
						lowerCAmelCase__  :       Union[str, Any]				=  MobileViTVaForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.num_labels
						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaForSemanticSegmentation(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape	,(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )	,)
						lowerCAmelCase__  :       List[str]				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape	,(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )	,)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  config_and_inputs
						lowerCAmelCase__  :       Dict				=  {'''pixel_values''': pixel_values}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(
		    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)

		snake_case_			=(
		    {
		        """feature-extraction""": MobileViTVaModel,
		        """image-classification""": MobileViTVaForImageClassification,
		        """image-segmentation""": MobileViTVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		snake_case_			=False
		snake_case_			=False
		snake_case_			=False
		snake_case_			=False



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MobileViTVaModelTester(self				)
						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaConfigTester(self	,config_class=__lowerCamelCase	,has_text_modality=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MobileViTV2 does not output attentions'''				)
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						pass



		@require_torch_multi_gpu
		@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Union[str, Any]				=  model_class(__lowerCamelCase				)
										lowerCAmelCase__  :       List[Any]				=  inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__  :       str				=  [*signature.parameters.keys()]

										lowerCAmelCase__  :       Union[str, Any]				=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]	,__lowerCamelCase				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						def check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()

										with torch.no_grad():
														lowerCAmelCase__  :       Optional[Any]				=  model(**self._prepare_for_class(__lowerCamelCase	,__lowerCamelCase				)				)

										lowerCAmelCase__  :       Dict				=  outputs.hidden_states

										lowerCAmelCase__  :       Any				=  5
										self.assertEqual(len(__lowerCamelCase				)	,__lowerCamelCase				)

										# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
										# with the width and height being successively divided by 2.
										lowerCAmelCase__  :       Optional[Any]				=  2
										for i in range(len(__lowerCamelCase				)				):
														self.assertListEqual(
														    list(hidden_states[i].shape[-2:]				)	,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,)
														divisor *= 2

										self.assertEqual(self.model_tester.output_stride	,divisor // 2				)

						lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Any				=  True
										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowerCAmelCase__  :       Tuple				=  True

										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       Optional[int]				=  MobileViTVaModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image



@require_torch
@require_vision
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		@cached_property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return (
						    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''				)
						    if is_vision_available()
						    else None
						)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''				).to(
						    __lowerCamelCase				)

						lowerCAmelCase__  :       str				=  self.default_image_processor
						lowerCAmelCase__  :       Union[str, Any]				=  prepare_img()
						lowerCAmelCase__  :       int				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       Union[str, Any]				=  model(**__lowerCamelCase				)

						# verify the logits
						lowerCAmelCase__  :       str				=  torch.Size((1, 10_00)				)
						self.assertEqual(outputs.logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]				).to(__lowerCamelCase				)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]	,__lowerCamelCase	,atol=1e-4				)				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)
						lowerCAmelCase__  :       Optional[Any]				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)

						lowerCAmelCase__  :       Optional[Any]				=  prepare_img()
						lowerCAmelCase__  :       Union[str, Any]				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       Optional[int]				=  torch.Size((1, 21, 32, 32)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  torch.tensor(
						    [
						        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
						        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
						        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
						    ]	,device=__lowerCamelCase	,)

						self.assertTrue(torch.allclose(logits[0, :3, :3, :3]	,__lowerCamelCase	,atol=1e-4				)				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)
						lowerCAmelCase__  :       Dict				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)

						lowerCAmelCase__  :       int				=  prepare_img()
						lowerCAmelCase__  :       Any				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  outputs.logits.detach().cpu()

						lowerCAmelCase__  :       Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase	,target_sizes=[(50, 60)]				)
						lowerCAmelCase__  :       Tuple				=  torch.Size((50, 60)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  torch.Size((32, 32)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)

