




from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__):




		'''simple docstring'''



		@register_to_config
		def __init__(self	,__lowerCamelCase = 16	,__lowerCamelCase = 88	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = 1	,__lowerCamelCase = 0.0	,__lowerCamelCase = 32	,__lowerCamelCase = None	,__lowerCamelCase = False	,__lowerCamelCase = None	,__lowerCamelCase = "geglu"	,__lowerCamelCase = True	,__lowerCamelCase = True	,)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       List[str]				=  num_attention_heads
						lowerCAmelCase__  :       int				=  attention_head_dim
						lowerCAmelCase__  :       Any				=  num_attention_heads * attention_head_dim

						lowerCAmelCase__  :       Optional[Any]				=  in_channels

						lowerCAmelCase__  :       Optional[Any]				=  torch.nn.GroupNorm(num_groups=__lowerCamelCase	,num_channels=__lowerCamelCase	,eps=1e-6	,affine=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  nn.Linear(__lowerCamelCase	,__lowerCamelCase				)

						# 3. Define transformers blocks
						lowerCAmelCase__  :       List[str]				=  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,dropout=__lowerCamelCase	,cross_attention_dim=__lowerCamelCase	,activation_fn=__lowerCamelCase	,attention_bias=__lowerCamelCase	,double_self_attention=__lowerCamelCase	,norm_elementwise_affine=__lowerCamelCase	,)
						        for d in range(__lowerCamelCase				)
						    ]				)

						lowerCAmelCase__  :       List[Any]				=  nn.Linear(__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=1	,__lowerCamelCase=None	,__lowerCamelCase = True	,)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  hidden_states.shape
						lowerCAmelCase__  :       Optional[int]				=  batch_frames // num_frames

						lowerCAmelCase__  :       Optional[int]				=  hidden_states

						lowerCAmelCase__  :       str				=  hidden_states[None, :].reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  hidden_states.permute(0	,2	,1	,3	,4				)

						lowerCAmelCase__  :       str				=  self.norm(__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  hidden_states.permute(0	,3	,4	,2	,1				).reshape(batch_size * height * width	,__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[int]				=  self.proj_in(__lowerCamelCase				)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__  :       Union[str, Any]				=  block(
										    __lowerCamelCase	,encoder_hidden_states=__lowerCamelCase	,timestep=__lowerCamelCase	,cross_attention_kwargs=__lowerCamelCase	,class_labels=__lowerCamelCase	,)

						# 3. Output
						lowerCAmelCase__  :       str				=  self.proj_out(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  (
						    hidden_states[None, None, :]
						    .reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
						    .permute(0	,3	,4	,1	,2				)
						    .contiguous()
						)
						lowerCAmelCase__  :       Optional[int]				=  hidden_states.reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=__lowerCamelCase				)






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				)

