
'''simple docstring'''



import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
						import torch.distributed.checkpoint as dist_cp
						from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
						from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
						from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
						from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


_lowercase          =						get_logger(__name__)


def A (__lowerCamelCase :str					,      __lowerCamelCase :List[str]					,      __lowerCamelCase :Any					,      __lowerCamelCase :int					,      __lowerCamelCase :Optional[int]=0						):
						os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)
						with FSDP.state_dict_type(
						    __lowerCamelCase					,      fsdp_plugin.state_dict_type					,      fsdp_plugin.state_dict_config					,      fsdp_plugin.optim_state_dict_config						):
												_lowerCAmelCase    		=					model.state_dict()
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		_lowerCAmelCase    		=					f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      __lowerCamelCase						)
																		if accelerator.process_index == 0:
																								logger.info(f'Saving model to {output_model_file}'						)
																								torch.save(__lowerCamelCase					,      __lowerCamelCase						)
																								logger.info(f'Model saved to {output_model_file}'						)
												elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																		_lowerCAmelCase    		=					(
																		    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																		    if model_index == 0
																		    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																		)
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      __lowerCamelCase						)
																		logger.info(f'Saving model to {output_model_file}'						)
																		torch.save(__lowerCamelCase					,      __lowerCamelCase						)
																		logger.info(f'Model saved to {output_model_file}'						)
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      f'{MODEL_NAME}_{model_index}'						)
																		os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)
																		logger.info(f'Saving model to {ckpt_dir}'						)
																		_lowerCAmelCase    		=					{"""model""": state_dict}

																		dist_cp.save_state_dict(
																		    state_dict=__lowerCamelCase					,      storage_writer=dist_cp.FileSystemWriter(__lowerCamelCase						)					,      planner=DefaultSavePlanner()					,      )
																		logger.info(f'Model saved to {ckpt_dir}'						)


def A (__lowerCamelCase :str					,      __lowerCamelCase :Any					,      __lowerCamelCase :Any					,      __lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :List[Any]=0						):
						accelerator.wait_for_everyone()
						with FSDP.state_dict_type(
						    __lowerCamelCase					,      fsdp_plugin.state_dict_type					,      fsdp_plugin.state_dict_config					,      fsdp_plugin.optim_state_dict_config						):
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		if type(__lowerCamelCase						) != FSDP and accelerator.process_index != 0:
																								if not fsdp_plugin.sync_module_states:
																														raise ValueError(
																														    """Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
																														    """initializing FSDP object"""						)
																								return
																		_lowerCAmelCase    		=					f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      __lowerCamelCase						)
																		logger.info(f'Loading model from {input_model_file}'						)
																		_lowerCAmelCase    		=					torch.load(__lowerCamelCase						)
																		logger.info(f'Model loaded from {input_model_file}'						)
												elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																		_lowerCAmelCase    		=					(
																		    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																		    if model_index == 0
																		    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																		)
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      __lowerCamelCase						)
																		logger.info(f'Loading model from {input_model_file}'						)
																		_lowerCAmelCase    		=					torch.load(__lowerCamelCase						)
																		logger.info(f'Model loaded from {input_model_file}'						)
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		_lowerCAmelCase    		=					(
																		    os.path.join(__lowerCamelCase					,      f'{MODEL_NAME}_{model_index}'						)
																		    if f'{MODEL_NAME}' not in input_dir
																		    else input_dir
																		)
																		logger.info(f'Loading model from {ckpt_dir}'						)
																		_lowerCAmelCase    		=					{"""model""": model.state_dict()}
																		dist_cp.load_state_dict(
																		    state_dict=__lowerCamelCase					,      storage_reader=dist_cp.FileSystemReader(__lowerCamelCase						)					,      planner=DefaultLoadPlanner()					,      )
																		_lowerCAmelCase    		=					state_dict["""model"""]
																		logger.info(f'Model loaded from {ckpt_dir}'						)
												model.load_state_dict(__lowerCamelCase						)


def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :str					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Any					,      __lowerCamelCase :Optional[Any]=0						):
						os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)
						with FSDP.state_dict_type(
						    __lowerCamelCase					,      fsdp_plugin.state_dict_type					,      fsdp_plugin.state_dict_config					,      fsdp_plugin.optim_state_dict_config						):
												_lowerCAmelCase    		=					FSDP.optim_state_dict(__lowerCamelCase					,      __lowerCamelCase						)
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		if accelerator.process_index == 0:
																								_lowerCAmelCase    		=					(
																								    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																								)
																								_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      __lowerCamelCase						)
																								logger.info(f'Saving Optimizer state to {output_optimizer_file}'						)
																								torch.save(__lowerCamelCase					,      __lowerCamelCase						)
																								logger.info(f'Optimizer state saved in {output_optimizer_file}'						)
												else:
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      f'{OPTIMIZER_NAME}_{optimizer_index}'						)
																		os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)
																		logger.info(f'Saving Optimizer state to {ckpt_dir}'						)
																		dist_cp.save_state_dict(
																		    state_dict={"""optimizer""": optim_state}					,      storage_writer=dist_cp.FileSystemWriter(__lowerCamelCase						)					,      planner=DefaultSavePlanner()					,      )
																		logger.info(f'Optimizer state saved in {ckpt_dir}'						)





def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :List[str]					,      __lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Any					,      __lowerCamelCase :List[Any]=0						):
						accelerator.wait_for_everyone()
						with FSDP.state_dict_type(
						    __lowerCamelCase					,      fsdp_plugin.state_dict_type					,      fsdp_plugin.state_dict_config					,      fsdp_plugin.optim_state_dict_config						):
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		_lowerCAmelCase    		=					None
																		# below check should work but currently it isn't working (mostly opytorch issue),
																		# in the meantime disabling it at the cost of excess memory usage
																		# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
																		_lowerCAmelCase    		=					(
																		    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																		)
																		_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      __lowerCamelCase						)
																		logger.info(f'Loading Optimizer state from {input_optimizer_file}'						)
																		_lowerCAmelCase    		=					torch.load(__lowerCamelCase						)
																		logger.info(f'Optimizer state loaded from {input_optimizer_file}'						)
												else:
																		_lowerCAmelCase    		=					(
																		    os.path.join(__lowerCamelCase					,      f'{OPTIMIZER_NAME}_{optimizer_index}'						)
																		    if f'{OPTIMIZER_NAME}' not in input_dir
																		    else input_dir
																		)
																		logger.info(f'Loading Optimizer from {ckpt_dir}'						)
																		_lowerCAmelCase    		=					load_sharded_optimizer_state_dict(
																		    model_state_dict=model.state_dict()					,      optimizer_key="""optimizer"""					,      storage_reader=dist_cp.FileSystemReader(__lowerCamelCase						)					,      )
																		_lowerCAmelCase    		=					optim_state["""optimizer"""]
																		logger.info(f'Optimizer loaded from {ckpt_dir}'						)
												_lowerCAmelCase    		=					FSDP.optim_state_dict_to_load(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
												optimizer.load_state_dict(__lowerCamelCase						)



"""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 			__a					(						__magic_name__       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(snake_case			,					"width_multiplier"   )   )






class 			__a					:





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=13			,					snake_case=64			,					snake_case=2			,					snake_case=3			,					snake_case="swish"			,					snake_case=3			,					snake_case=32			,					snake_case=0.1			,					snake_case=0.02			,					snake_case=True			,					snake_case=True			,					snake_case=10			,					snake_case=None			,					snake_case=0.25			,					snake_case=0.0			,					snake_case=0.0			,					):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				parent
        lowerCAmelCase__   :				Optional[Any] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				image_size
        lowerCAmelCase__   :				Tuple 			=				patch_size
        lowerCAmelCase__   :				Any 			=				num_channels
        lowerCAmelCase__   :				Tuple 			=				make_divisible(512 * width_multiplier			,					divisor=8   )
        lowerCAmelCase__   :				str 			=				hidden_act
        lowerCAmelCase__   :				Union[str, Any] 			=				conv_kernel_size
        lowerCAmelCase__   :				List[str] 			=				output_stride
        lowerCAmelCase__   :				List[Any] 			=				classifier_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				use_labels
        lowerCAmelCase__   :				Tuple 			=				is_training
        lowerCAmelCase__   :				Optional[int] 			=				num_labels
        lowerCAmelCase__   :				int 			=				initializer_range
        lowerCAmelCase__   :				str 			=				scope
        lowerCAmelCase__   :				Optional[Any] 			=				width_multiplier
        lowerCAmelCase__   :				Union[str, Any] 			=				ffn_dropout
        lowerCAmelCase__   :				Tuple 			=				attn_dropout






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

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

        lowerCAmelCase__   :				int 			=				self.get_config()

        return config, pixel_values, labels, pixel_labels






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				MobileViTVaModel(config=snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(snake_case   )
        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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.num_labels
        lowerCAmelCase__   :				Any 			=				MobileViTVaForImageClassification(snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(snake_case			,					labels=snake_case   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.num_labels
        lowerCAmelCase__   :				Optional[int] 			=				MobileViTVaForSemanticSegmentation(snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				Any 			=				model(snake_case   )
        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__   :				Tuple 			=				model(snake_case			,					labels=snake_case   )
        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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.prepare_config_and_inputs()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				config_and_inputs
        lowerCAmelCase__   :				int 			=				{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class 			__a					(						__magic_name__       ,	__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						(
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       __UpperCamelCase					:      List[Any]    =						(
           {
               'feature-extraction': MobileViTVaModel,
               'image-classification': MobileViTVaForImageClassification,
               'image-segmentation': MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __UpperCamelCase					:      str    =						False
       __UpperCamelCase					:      int    =						False
       __UpperCamelCase					:      Tuple    =						False
       __UpperCamelCase					:      List[str]    =						False






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				MobileViTVaModelTester(self   )
        lowerCAmelCase__   :				List[str] 			=				MobileViTVaConfigTester(self			,					config_class=snake_case			,					has_text_modality=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        self.config_tester.run_common_tests()






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





        """simple docstring"""

        pass






       @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip(reason="MobileViTV2 does not output attentions"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






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





        """simple docstring"""

        pass






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





        """simple docstring"""

        pass






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCAmelCase__   :				Dict 			=				["pixel_values"]
         self.assertListEqual(arg_names[:1]			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        def check_hidden_states_output(snake_case			,					snake_case			,					snake_case   ):
         lowerCAmelCase__   :				List[str] 			=				model_class(snake_case   )
         model.to(snake_case   )
         model.eval()

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

         lowerCAmelCase__   :				str 			=				outputs.hidden_states

         lowerCAmelCase__   :				List[Any] 			=				5
         self.assertEqual(len(snake_case   )			,					snake_case   )

         # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
         # with the width and height being successively divided by 2.
         lowerCAmelCase__   :				str 			=				2
         for i in range(len(snake_case   )   ):
          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(snake_case			,					snake_case			,					snake_case   )

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

         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__   :				Dict 			=				MobileViTVaModel.from_pretrained(snake_case   )
         self.assertIsNotNone(snake_case   )




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






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





       """simple docstring"""


       @cached_property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"   ).to(
            snake_case   )

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

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				str 			=				model(**snake_case   )

        # verify the logits
        lowerCAmelCase__   :				int 			=				torch.Size((1, 1_000)   )
        self.assertEqual(outputs.logits.shape			,					snake_case   )

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

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )
        lowerCAmelCase__   :				Tuple 			=				model.to(snake_case   )

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

        lowerCAmelCase__   :				Union[str, Any] 			=				prepare_img()
        lowerCAmelCase__   :				str 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

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

        # verify the logits
        lowerCAmelCase__   :				Tuple 			=				torch.Size((1, 21, 32, 32)   )
        self.assertEqual(logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Dict 			=				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=snake_case			,					)

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        lowerCAmelCase__   :				str 			=				MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )

        lowerCAmelCase__   :				Optional[int] 			=				prepare_img()
        lowerCAmelCase__   :				List[Any] 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

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

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

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

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






