


'''simple docstring'''







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A="attention"					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=	params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
       _lowerCAmelCase    :							Any   			=	params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
       _lowerCAmelCase    :							Any   			=	params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
       _lowerCAmelCase    :							Optional[int]   			=	params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
       return k, o, q, v






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A=False					):







       """simple docstring"""







       if split_mlp_wi:
              _lowerCAmelCase    :							Optional[Any]   			=	params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
              _lowerCAmelCase    :							Dict   			=	params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
              _lowerCAmelCase    :							Union[str, Any]   			=	(wi_a, wi_a)
       else:
              _lowerCAmelCase    :							Tuple   			=	params[f'{prefix}/layers_{i}/mlp/wi/kernel']

       _lowerCAmelCase    :							Tuple   			=	params[f'{prefix}/layers_{i}/mlp/wo/kernel']
       return wi, wo






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A					):







       """simple docstring"""







       return params[f'{prefix}/layers_{i}/{layer_name}/scale']






def 			lowercase				(_A   ,						*, _A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[str]   			=	traverse_util.flatten_dict(variables['target']					)
       _lowerCAmelCase    :							Dict   			=	{"/".join(_lowerCAmelCase					): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       _lowerCAmelCase    :							Tuple   			=	"encoder/layers_0/mlp/wi_0/kernel" in old
       print('Split MLP:'   ,						_lowerCAmelCase					)

       _lowerCAmelCase    :							Optional[int]   			=	collections.OrderedDict()

       # Shared embeddings.
       _lowerCAmelCase    :							Optional[int]   			=	old["token_embedder/embedding"]

       # Encoder.
       for i in range(_lowerCAmelCase					):
              # Block i, layer 0 (Self Attention).
              _lowerCAmelCase    :							int   			=	tax_layer_norm_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'encoder'   ,						'pre_attention_layer_norm'					)
              _lowerCAmelCase    :							Optional[Any]   			=	tax_attention_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'encoder'   ,						'attention'					)
              _lowerCAmelCase    :							List[Any]   			=	layer_norm
              _lowerCAmelCase    :							List[str]   			=	k.T
              _lowerCAmelCase    :							Tuple   			=	o.T
              _lowerCAmelCase    :							str   			=	q.T
              _lowerCAmelCase    :							Dict   			=	v.T

              # Block i, layer 1 (MLP).
              _lowerCAmelCase    :							str   			=	tax_layer_norm_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'encoder'   ,						'pre_mlp_layer_norm'					)
              _lowerCAmelCase    :							Union[str, Any]   			=	tax_mlp_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'encoder'   ,						_lowerCAmelCase					)
              _lowerCAmelCase    :							int   			=	layer_norm
              if split_mlp_wi:
                     _lowerCAmelCase    :							Dict   			=	wi[0].T
                     _lowerCAmelCase    :							List[Any]   			=	wi[1].T
              else:
                     _lowerCAmelCase    :							Optional[Any]   			=	wi.T
              _lowerCAmelCase    :							Optional[int]   			=	wo.T

       _lowerCAmelCase    :							Optional[Any]   			=	old[
           "encoder/relpos_bias/rel_embedding"
       ].T
       _lowerCAmelCase    :							Optional[Any]   			=	old["encoder/encoder_norm/scale"]

       if not is_encoder_only:
              # Decoder.
              for i in range(_lowerCAmelCase					):
                     # Block i, layer 0 (Self Attention).
                     _lowerCAmelCase    :							Optional[int]   			=	tax_layer_norm_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'decoder'   ,						'pre_self_attention_layer_norm'					)
                     _lowerCAmelCase    :							List[str]   			=	tax_attention_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'decoder'   ,						'self_attention'					)
                     _lowerCAmelCase    :							Optional[Any]   			=	layer_norm
                     _lowerCAmelCase    :							Any   			=	k.T
                     _lowerCAmelCase    :							Any   			=	o.T
                     _lowerCAmelCase    :							Optional[int]   			=	q.T
                     _lowerCAmelCase    :							List[str]   			=	v.T

                     # Block i, layer 1 (Cross Attention).
                     _lowerCAmelCase    :							Any   			=	tax_layer_norm_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'decoder'   ,						'pre_cross_attention_layer_norm'					)
                     _lowerCAmelCase    :							int   			=	tax_attention_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'decoder'   ,						'encoder_decoder_attention'					)
                     _lowerCAmelCase    :							int   			=	layer_norm
                     _lowerCAmelCase    :							Union[str, Any]   			=	k.T
                     _lowerCAmelCase    :							List[str]   			=	o.T
                     _lowerCAmelCase    :							Any   			=	q.T
                     _lowerCAmelCase    :							str   			=	v.T

                     # Block i, layer 2 (MLP).
                     _lowerCAmelCase    :							Optional[int]   			=	tax_layer_norm_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'decoder'   ,						'pre_mlp_layer_norm'					)
                     _lowerCAmelCase    :							Optional[int]   			=	tax_mlp_lookup(_lowerCAmelCase   ,						_lowerCAmelCase   ,						'decoder'   ,						_lowerCAmelCase					)
                     _lowerCAmelCase    :							Union[str, Any]   			=	layer_norm
                     if split_mlp_wi:
                            _lowerCAmelCase    :							Optional[int]   			=	wi[0].T
                            _lowerCAmelCase    :							List[Any]   			=	wi[1].T
                     else:
                            _lowerCAmelCase    :							Dict   			=	wi.T
                     _lowerCAmelCase    :							Dict   			=	wo.T

              _lowerCAmelCase    :							Union[str, Any]   			=	old["decoder/decoder_norm/scale"]
              _lowerCAmelCase    :							Union[str, Any]   			=	old[
                  "decoder/relpos_bias/rel_embedding"
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     _lowerCAmelCase    :							Tuple   			=	old["decoder/logits_dense/kernel"].T

       return new






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=	collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              _lowerCAmelCase    :							Any   			=	state_dict["shared.weight"]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     _lowerCAmelCase    :							Tuple   			=	state_dict["shared.weight"]

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'					)
                     _lowerCAmelCase    :							Optional[Any]   			=	state_dict["shared.weight"]

       return state_dict






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							int   			=	checkpoints.load_tax_checkpoint(_lowerCAmelCase					)
       _lowerCAmelCase    :							Tuple   			=	convert_tax_to_pytorch(_lowerCAmelCase   ,						num_layers=config.num_layers   ,						is_encoder_only=_lowerCAmelCase					)
       _lowerCAmelCase    :							List[Any]   			=	make_state_dict(_lowerCAmelCase   ,						_lowerCAmelCase					)
       model.load_state_dict(_lowerCAmelCase   ,						strict=_lowerCAmelCase					)






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A = False					):







       """simple docstring"""







       _lowerCAmelCase    :							List[Any]   			=	TaConfig.from_json_file(_lowerCAmelCase					)
       print(f'Building PyTorch model from configuration: {config}'					)
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              _lowerCAmelCase    :							Optional[Any]   			=	TaEncoderModel(_lowerCAmelCase					)
       else:
              _lowerCAmelCase    :							Any   			=	TaForConditionalGeneration(_lowerCAmelCase					)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase					)

       # Save pytorch-model
       print(f'Save PyTorch model to {pytorch_dump_path}'					)
       model.save_pretrained(_lowerCAmelCase					)

       # Verify that we can load the checkpoint.
       model.from_pretrained(_lowerCAmelCase					)
       print('Done'					)


if __name__ == "__main__":
       lowerCAmelCase    :     Union[str, Any]					=						argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       lowerCAmelCase    :     Optional[int]					=						parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )





'''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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(snake_case__      ,	'width_multiplier'   )   )
class 	UpperCamelCase__					:



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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _lowerCAmelCase    :							Optional[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    :							Optional[int]   			=       self.get_config()

            return config, pixel_values, labels, pixel_labels







     def a	( 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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       MobileViTVaModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							str   			=       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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.num_labels
            _lowerCAmelCase    :							List[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 a	( 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    :							Dict   			=       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    :							Any   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       config_and_inputs
            _lowerCAmelCase    :							Tuple   			=       {'pixel_values': pixel_values}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






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

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

     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       MobileViTVaModelTester(self   )
            _lowerCAmelCase    :							Dict   			=       MobileViTVaConfigTester(self      ,	config_class=snake_case__      ,	has_text_modality=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							str   			=       model_class(snake_case__   )
                   _lowerCAmelCase    :							List[str]   			=       inspect.signature(model.forward   )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   _lowerCAmelCase    :							int   			=       [*signature.parameters.keys()]

                   _lowerCAmelCase    :							Any   			=       ['pixel_values']
                   self.assertListEqual(arg_names[:1]      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            def check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Dict   			=       model_class(snake_case__   )
                   model.to(snake_case__   )
                   model.eval()

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

                   _lowerCAmelCase    :							List[str]   			=       outputs.hidden_states

                   _lowerCAmelCase    :							List[str]   			=       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    :							List[Any]   			=       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    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							Optional[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    :							Any   			=       True

                   check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__   )







     @slow
     def a	( 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 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image
@require_torch
@require_vision
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @cached_property
     def a	( self   ):

            '''simple docstring'''

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







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
                snake_case__   )

            _lowerCAmelCase    :							str   			=       self.default_image_processor
            _lowerCAmelCase    :							Any   			=       prepare_img()
            _lowerCAmelCase    :							Optional[int]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							Tuple   			=       model(**snake_case__   )

            # verify the logits
            _lowerCAmelCase    :							Optional[Any]   			=       torch.Size((1, 1000)   )
            self.assertEqual(outputs.logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
            _lowerCAmelCase    :							Any   			=       model.to(snake_case__   )

            _lowerCAmelCase    :							int   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Optional[int]   			=       prepare_img()
            _lowerCAmelCase    :							Dict   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							int   			=       model(**snake_case__   )
            _lowerCAmelCase    :							Dict   			=       outputs.logits

            # verify the logits
            _lowerCAmelCase    :							str   			=       torch.Size((1, 21, 32, 32)   )
            self.assertEqual(logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							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=snake_case__      ,	)

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







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       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    :							Tuple   			=       prepare_img()
            _lowerCAmelCase    :							List[str]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							Any   			=       model(**snake_case__   )

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

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

            _lowerCAmelCase    :							List[str]   			=       image_processor.post_process_semantic_segmentation(outputs=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       torch.Size((32, 32)   )
            self.assertEqual(segmentation[0].shape      ,	snake_case__   )

