

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 		__magic_name__ (				unittest.TestCase							):






     def __init__(							self						,		_lowercase						,		_lowercase=7						,		_lowercase=3						,		_lowercase=18						,		_lowercase=30						,		_lowercase=400						,		_lowercase=True						,		_lowercase=None						,		_lowercase=True						,		_lowercase=None						,		_lowercase=True						,		_lowercase=[0.5, 0.5, 0.5]						,		_lowercase=[0.5, 0.5, 0.5]						,		)->			int:
         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      UpperCAmelCase_							(							self     )->			str:
         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 		__magic_name__ (				snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :str									=	DeiTImageProcessor if is_vision_available() else None
     UpperCamelCase_     :List[str]									=	True






     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		DeiTImageProcessingTester(self     )






     @property
     def      UpperCAmelCase_							(							self     )->			Any:
         return self.image_processor_tester.prepare_image_processor_dict()






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_          =		self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_lowercase						,		"do_resize"     )     )
         self.assertTrue(hasattr(_lowercase						,		"size"     )     )
         self.assertTrue(hasattr(_lowercase						,		"do_center_crop"     )     )
         self.assertTrue(hasattr(_lowercase						,		"center_crop"     )     )
         self.assertTrue(hasattr(_lowercase						,		"do_normalize"     )     )
         self.assertTrue(hasattr(_lowercase						,		"image_mean"     )     )
         self.assertTrue(hasattr(_lowercase						,		"image_std"     )     )






     def      UpperCAmelCase_							(							self     )->			int:
         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      UpperCAmelCase_							(							self     )->			Tuple:
         pass






     def      UpperCAmelCase_							(							self     )->			Union[str, 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=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase						,		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(_lowercase						,		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      UpperCAmelCase_							(							self     )->			Optional[int]:
         # 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=_lowercase						,		numpify=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase						,		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(_lowercase						,		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      UpperCAmelCase_							(							self     )->			List[str]:
         # 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=_lowercase						,		torchify=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase						,		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(_lowercase						,		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"],
             )						,		)




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


snake_case_  :	Any							  =							logging.get_logger(__name__)

snake_case_  :	Any							  =							{
    '''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   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mobilevit"




						def __init__(				self    :					Tuple ,			_a    :					Any=3 ,			_a    :					Any=256 ,			_a    :					Any=2 ,			_a    :					List[str]=[144, 192, 240] ,			_a    :					Optional[int]=[16, 32, 64, 96, 128, 160, 640] ,			_a    :					Union[str, Any]=4 ,			_a    :					Any=2.0 ,			_a    :					List[str]=4.0 ,			_a    :					Tuple="silu" ,			_a    :					Optional[Any]=3 ,			_a    :					List[str]=32 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Union[str, Any]=0.02 ,			_a    :					Tuple=1E-5 ,			_a    :					Any=True ,			_a    :					str=256 ,			_a    :					Tuple=[6, 12, 18] ,			_a    :					int=0.1 ,			_a    :					str=255 ,			**_a    :					Any ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__(**_a     )

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

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

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  version.parse("1.11"     )




						@property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]     )




						@property
						def 							__UpperCamelCase (				self    :					Dict     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





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




						@property
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   float:


										"""simple docstring"""





										return 1E-4