"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def __init__(							self  :      List[str]       ,lowerCamelCase__  :      str       ,lowerCamelCase__  :      Any=7       ,lowerCamelCase__  :      Dict=3       ,lowerCamelCase__  :      Union[str, Any]=18       ,lowerCamelCase__  :      Optional[Any]=30       ,lowerCamelCase__  :      List[Any]=400       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      List[Any]=None       ,lowerCamelCase__  :      List[str]=True       ,lowerCamelCase__  :      Optional[int]=None       ,lowerCamelCase__  :      Optional[Any]=True       ,lowerCamelCase__  :      str=[0.5, 0.5, 0.5]       ,lowerCamelCase__  :      int=[0.5, 0.5, 0.5]       ,):
									UpperCAmelCase__			 =				size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__			 =				crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__			 =				parent
									UpperCAmelCase__			 =				batch_size
									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				min_resolution
									UpperCAmelCase__			 =				max_resolution
									UpperCAmelCase__			 =				do_resize
									UpperCAmelCase__			 =				size
									UpperCAmelCase__			 =				do_center_crop
									UpperCAmelCase__			 =				crop_size
									UpperCAmelCase__			 =				do_normalize
									UpperCAmelCase__			 =				image_mean
									UpperCAmelCase__			 =				image_std







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}






@require_torch
@require_vision
class 							snake_case			(					__UpperCAmelCase ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					DeiTImageProcessor if is_vision_available() else None
					snake_case__							       =					True







					def 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				DeiTImageProcessingTester(self     )







					@property
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'do_resize'     )     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'size'     )     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'do_center_crop'     )     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'center_crop'     )     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'do_normalize'     )     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'image_mean'     )     )
									self.assertTrue(hasattr(lowerCamelCase__       ,'image_std'     )     )







					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									UpperCAmelCase__			 =				self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size       ,{'height': 20, 'width': 20}     )
									self.assertEqual(image_processor.crop_size       ,{'height': 18, 'width': 18}     )

									UpperCAmelCase__			 =				self.image_processing_class.from_dict(self.image_processor_dict       ,size=42       ,crop_size=84     )
									self.assertEqual(image_processor.size       ,{'height': 42, 'width': 42}     )
									self.assertEqual(image_processor.crop_size       ,{'height': 84, 'width': 84}     )







					def 						__lowerCAmelCase		(							self  :      int     ):
									pass







					def 						__lowerCAmelCase		(							self  :      Any     ):
									# Initialize image_processing
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									UpperCAmelCase__			 =				prepare_image_inputs(self.image_processor_tester       ,equal_resolution=lowerCamelCase__     )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__       ,Image.Image     )

									# Test not batched input
									UpperCAmelCase__			 =				image_processing(image_inputs[0]       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape       ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)

									# Test batched
									UpperCAmelCase__			 =				image_processing(lowerCamelCase__       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape       ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									# Initialize image_processing
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									UpperCAmelCase__			 =				prepare_image_inputs(self.image_processor_tester       ,equal_resolution=lowerCamelCase__       ,numpify=lowerCamelCase__     )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__       ,np.ndarray     )

									# Test not batched input
									UpperCAmelCase__			 =				image_processing(image_inputs[0]       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape       ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)

									# Test batched
									UpperCAmelCase__			 =				image_processing(lowerCamelCase__       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape       ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)







					def 						__lowerCAmelCase		(							self  :      int     ):
									# Initialize image_processing
									UpperCAmelCase__			 =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									UpperCAmelCase__			 =				prepare_image_inputs(self.image_processor_tester       ,equal_resolution=lowerCamelCase__       ,torchify=lowerCamelCase__     )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__       ,torch.Tensor     )

									# Test not batched input
									UpperCAmelCase__			 =				image_processing(image_inputs[0]       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape       ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)

									# Test batched
									UpperCAmelCase__			 =				image_processing(lowerCamelCase__       ,return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape       ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )       ,)




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__    :    List[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    Dict			=      {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"mobilevit"







					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      str=256       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Union[str, Any]=[144, 192, 240]       ,lowerCamelCase__  :      Optional[int]=[16, 32, 64, 96, 128, 160, 640]       ,lowerCamelCase__  :      Dict=4       ,lowerCamelCase__  :      int=2.0       ,lowerCamelCase__  :      Optional[Any]=4.0       ,lowerCamelCase__  :      Optional[int]="silu"       ,lowerCamelCase__  :      List[str]=3       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      int=0.1       ,lowerCamelCase__  :      List[str]=0.0       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Tuple=0.0_2       ,lowerCamelCase__  :      Union[str, Any]=1e-5       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      Dict=256       ,lowerCamelCase__  :      Any=[6, 12, 18]       ,lowerCamelCase__  :      Optional[int]=0.1       ,lowerCamelCase__  :      List[str]=255       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )

									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				patch_size
									UpperCAmelCase__			 =				hidden_sizes
									UpperCAmelCase__			 =				neck_hidden_sizes
									UpperCAmelCase__			 =				num_attention_heads
									UpperCAmelCase__			 =				mlp_ratio
									UpperCAmelCase__			 =				expand_ratio
									UpperCAmelCase__			 =				hidden_act
									UpperCAmelCase__			 =				conv_kernel_size
									UpperCAmelCase__			 =				output_stride
									UpperCAmelCase__			 =				hidden_dropout_prob
									UpperCAmelCase__			 =				attention_probs_dropout_prob
									UpperCAmelCase__			 =				classifier_dropout_prob
									UpperCAmelCase__			 =				initializer_range
									UpperCAmelCase__			 =				layer_norm_eps
									UpperCAmelCase__			 =				qkv_bias

									# decode head attributes for semantic segmentation
									UpperCAmelCase__			 =				aspp_out_channels
									UpperCAmelCase__			 =				atrous_rates
									UpperCAmelCase__			 =				aspp_dropout_prob
									UpperCAmelCase__			 =				semantic_loss_ignore_index






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					version.parse("1.11"							)







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									if self.task == "image-classification":
													return OrderedDict([('logits', {0: 'batch'})]     )
									else:
													return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return 1e-4



