






'''simple docstring'''







import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


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

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met

class _lowercase			(       _A	):




							def __init__(							self:       Union[str, Any]    ,							*UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any=None    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       Optional[int]=None    ,							**UpperCamelCase__:       Optional[Any]  ):
														super().__init__(*UpperCamelCase__    ,							**UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= eval_examples
														lowerCamelCase__      :			List[str] 						= post_process_function
														lowerCamelCase__      :			Optional[Any] 						= quant_trainer_args
														lowerCamelCase__      :			Any 						= 128  # default number of calibration samples




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       List[Any]=None  ):






														if calib_dataset is None and self.calib_dataset is None:
																					raise ValueError("""Trainer: calibration requires an calib_dataset."""  )
														lowerCamelCase__      :			List[str] 						= calib_dataset if calib_dataset is not None else self.calib_dataset

														lowerCamelCase__      :			Optional[Any] 						= self._remove_unused_columns(UpperCamelCase__    ,							description="""Calibration"""  )

														return DataLoader(
														    UpperCamelCase__    ,							batch_size=self.args.eval_batch_size    ,							collate_fn=self.data_collator    ,							drop_last=self.args.dataloader_drop_last    ,							num_workers=self.args.dataloader_num_workers    ,							pin_memory=self.args.dataloader_pin_memory    ,							shuffle=UpperCamelCase__    ,							)




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Any=None  ):
														lowerCamelCase__      :			Any 						= self.train_dataset if calib_dataset is None else calib_dataset
														lowerCamelCase__      :			Optional[int] 						= self.get_calib_dataloader(UpperCamelCase__  )

														lowerCamelCase__      :			List[str] 						= self.model
														quant_trainer.configure_model(UpperCamelCase__    ,							self.quant_trainer_args    ,							calib=UpperCamelCase__  )
														model.eval()
														quant_trainer.enable_calibration(UpperCamelCase__  )

														logger.info("""***** Running calibration *****"""  )
														logger.info(F'''  Num examples = {self.calib_num}'''  )
														logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''  )

														for step, inputs in enumerate(UpperCamelCase__  ):
																					# Prediction step
																					lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.prediction_step(UpperCamelCase__    ,							UpperCamelCase__    ,							prediction_loss_only=UpperCamelCase__  )
																					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																												break

														quant_trainer.finish_calibration(UpperCamelCase__    ,							self.quant_trainer_args  )
														lowerCamelCase__      :			str 						= model




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str=None    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       List[str]=None    ,							UpperCamelCase__:       str = "eval"  ):
														lowerCamelCase__      :			Optional[int] 						= self.eval_dataset if eval_dataset is None else eval_dataset
														lowerCamelCase__      :			Any 						= self.get_eval_dataloader(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= self.eval_examples if eval_examples is None else eval_examples

														# Temporarily disable metric computation, we will do it in the loop here.
														lowerCamelCase__      :			Any 						= self.compute_metrics
														lowerCamelCase__      :			List[str] 						= None
														lowerCamelCase__      :			str 						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
														try:
																					lowerCamelCase__      :			List[Any] 						= eval_loop(
																					    UpperCamelCase__    ,							description="""Evaluation"""    ,							prediction_loss_only=True if compute_metrics is None else None    ,							ignore_keys=UpperCamelCase__    ,							)
														finally:
																					lowerCamelCase__      :			List[Any] 						= compute_metrics

														if self.post_process_function is not None and self.compute_metrics is not None:
																					lowerCamelCase__      :			int 						= self.post_process_function(UpperCamelCase__    ,							UpperCamelCase__    ,							output.predictions  )
																					lowerCamelCase__      :			List[Any] 						= self.compute_metrics(UpperCamelCase__  )

																					# Prefix all keys with metric_key_prefix + '_'
																					for key in list(metrics.keys()  ):
																												if not key.startswith(F'''{metric_key_prefix}_'''  ):
																																			lowerCamelCase__      :			Any 						= metrics.pop(UpperCamelCase__  )

																					self.log(UpperCamelCase__  )
														else:
																					lowerCamelCase__      :			str 						= {}

														if self.args.tpu_metrics_debug or self.args.debug:
																					# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																					xm.master_print(met.metrics_report()  )

														lowerCamelCase__      :			Dict 						= self.callback_handler.on_evaluate(self.args    ,							self.state    ,							self.control    ,							UpperCamelCase__  )
														return metrics




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       str=None    ,							UpperCamelCase__:       str = "test"  ):
														lowerCamelCase__      :			Any 						= self.get_test_dataloader(UpperCamelCase__  )

														# Temporarily disable metric computation, we will do it in the loop here.
														lowerCamelCase__      :			Optional[int] 						= self.compute_metrics
														lowerCamelCase__      :			List[Any] 						= None
														lowerCamelCase__      :			Union[str, Any] 						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
														try:
																					lowerCamelCase__      :			int 						= eval_loop(
																					    UpperCamelCase__    ,							description="""Prediction"""    ,							prediction_loss_only=True if compute_metrics is None else None    ,							ignore_keys=UpperCamelCase__    ,							)
														finally:
																					lowerCamelCase__      :			Optional[Any] 						= compute_metrics

														if self.post_process_function is None or self.compute_metrics is None:
																					return output

														lowerCamelCase__      :			Any 						= self.post_process_function(UpperCamelCase__    ,							UpperCamelCase__    ,							output.predictions    ,							"""predict"""  )
														lowerCamelCase__      :			int 						= self.compute_metrics(UpperCamelCase__  )

														# Prefix all keys with metric_key_prefix + '_'
														for key in list(metrics.keys()  ):
																					if not key.startswith(F'''{metric_key_prefix}_'''  ):
																												lowerCamelCase__      :			Optional[Any] 						= metrics.pop(UpperCamelCase__  )

														return PredictionOutput(predictions=predictions.predictions    ,							label_ids=predictions.label_ids    ,							metrics=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Optional[int]="./"  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.eval_dataset
														lowerCamelCase__      :			List[str] 						= self.get_eval_dataloader(UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= next(iter(UpperCamelCase__  )  )

														# saving device - to make it consistent
														lowerCamelCase__      :			Union[str, Any] 						= torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )

														# convert to tuple
														lowerCamelCase__      :			str 						= tuple(v.to(UpperCamelCase__  ) for k, v in batch.items()  )

														logger.info("""Converting model to be onnx compatible"""  )
														from pytorch_quantization.nn import TensorQuantizer

														lowerCamelCase__      :			Optional[Any] 						= True

														lowerCamelCase__      :			str 						= self.model.to(UpperCamelCase__  )

														model.eval()
														model.float()

														lowerCamelCase__      :			Optional[Any] 						= model.module if hasattr(UpperCamelCase__    ,							"""module"""  ) else model
														quant_trainer.configure_model(UpperCamelCase__    ,							self.quant_trainer_args  )

														lowerCamelCase__      :			int 						= os.path.join(UpperCamelCase__    ,							"""model.onnx"""  )
														logger.info(F'''exporting model to {output_model_file}'''  )

														lowerCamelCase__      :			Optional[int] 						= {0: """batch_size""", 1: """seq_len"""}

														torch.onnx.export(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							export_params=UpperCamelCase__    ,							opset_version=13    ,							do_constant_folding=UpperCamelCase__    ,							input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]    ,							output_names=["""output_start_logits""", """output_end_logits"""]    ,							dynamic_axes={
														        """input_ids""": axes,
														        """attention_mask""": axes,
														        """token_type_ids""": axes,
														        """output_start_logits""": axes,
														        """output_end_logits""": axes,
														    }    ,							verbose=UpperCamelCase__    ,							)
														logger.info("""onnx export finished"""  )







'''simple docstring'''







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 _lowercase			(       _lowercase	):




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.config_class(**self.inputs_dict  )
														self.parent.assertTrue(hasattr(UpperCamelCase__    ,							"""width_multiplier"""  )  )



class _lowercase			:




							def __init__(							self:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       str=13    ,							UpperCamelCase__:       Any=64    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       str=3    ,							UpperCamelCase__:       List[str]="swish"    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       int=0.02    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       List[Any]=0.25    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							):
														lowerCamelCase__      :			Any 						= parent
														lowerCamelCase__      :			Optional[Any] 						= batch_size
														lowerCamelCase__      :			Optional[int] 						= image_size
														lowerCamelCase__      :			str 						= patch_size
														lowerCamelCase__      :			Optional[int] 						= num_channels
														lowerCamelCase__      :			Optional[Any] 						= make_divisible(512 * width_multiplier    ,							divisor=8  )
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			Any 						= conv_kernel_size
														lowerCamelCase__      :			Any 						= output_stride
														lowerCamelCase__      :			Union[str, Any] 						= classifier_dropout_prob
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			Optional[Any] 						= is_training
														lowerCamelCase__      :			List[str] 						= num_labels
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			List[Any] 						= scope
														lowerCamelCase__      :			Tuple 						= width_multiplier
														lowerCamelCase__      :			List[Any] 						= ffn_dropout
														lowerCamelCase__      :			Any 						= attn_dropout




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Dict 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCamelCase__      :			Tuple 						= None
														lowerCamelCase__      :			Optional[Any] 						= None
														if self.use_labels:
																					lowerCamelCase__      :			List[Any] 						= ids_tensor([self.batch_size]    ,							self.num_labels  )
																					lowerCamelCase__      :			str 						= ids_tensor([self.batch_size, self.image_size, self.image_size]    ,							self.num_labels  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_config()

														return config, pixel_values, labels, pixel_labels




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														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:       Union[str, Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			str 						= model(UpperCamelCase__  )
														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:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Tuple 						= self.num_labels
														lowerCamelCase__      :			Dict 						= MobileViTVaForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			List[str] 						= self.num_labels
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaForSemanticSegmentation(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__  )
														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[Any] 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														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:       Tuple  ):
														lowerCamelCase__      :			Any 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= config_and_inputs
														lowerCamelCase__      :			Optional[Any] 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)

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

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= MobileViTVaModelTester(self  )
														lowerCamelCase__      :			List[str] 						= MobileViTVaConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""  )
							def 	lowerCamelCase_       (							self:       List[str]  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not output attentions"""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




							@require_torch_multi_gpu
							@unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__						,	lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__      :			str 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														def check_hidden_states_output(UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
																					lowerCamelCase__      :			List[Any] 						= model_class(UpperCamelCase__  )
																					model.to(UpperCamelCase__  )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__      :			Tuple 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

																					lowerCamelCase__      :			Optional[int] 						= outputs.hidden_states

																					lowerCamelCase__      :			List[Any] 						= 5
																					self.assertEqual(len(UpperCamelCase__  )    ,							UpperCamelCase__  )

																					# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																					# with the width and height being successively divided by 2.
																					lowerCamelCase__      :			int 						= 2
																					for i in range(len(UpperCamelCase__  )  ):
																												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__      :			Tuple 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			int 						= True
																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

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

																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[int]:
							lowerCamelCase__      :			str 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




							@cached_property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														return (
														    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  )
														    if is_vision_available()
														    else None
														)




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  ).to(
														    UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.default_image_processor
														lowerCamelCase__      :			List[Any] 						= prepare_img()
														lowerCamelCase__      :			Any 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(**UpperCamelCase__  )

														# verify the logits
														lowerCamelCase__      :			str 						= torch.Size((1, 1_000)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(UpperCamelCase__  )

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




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			Optional[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Union[str, Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Optional[Any] 						= model(**UpperCamelCase__  )
														lowerCamelCase__      :			str 						= outputs.logits

														# verify the logits
														lowerCamelCase__      :			List[str] 						= torch.Size((1, 21, 32, 32)  )
														self.assertEqual(logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= torch.tensor(
														    [
														        [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
														        [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
														        [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
														    ]    ,							device=UpperCamelCase__    ,							)

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




							@slow
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			List[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Optional[Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Dict 						= model(**UpperCamelCase__  )

														lowerCamelCase__      :			List[str] 						= outputs.logits.detach().cpu()

														lowerCamelCase__      :			List[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__    ,							target_sizes=[(50, 60)]  )
														lowerCamelCase__      :			int 						= torch.Size((50, 60)  )
														self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

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

