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


UpperCamelCase__       							=      logging.get_logger(__name__)

UpperCamelCase__       							=      {
    '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							(       lowerCAmelCase_  ):
   __UpperCAmelCase				:				int          =    'mobilevit'


   def __init__(self  :		str ,						__UpperCAmelCase  :		List[str]=3 ,						__UpperCAmelCase  :		List[str]=2_5_6 ,						__UpperCAmelCase  :		int=2 ,						__UpperCAmelCase  :		int=[1_4_4, 1_9_2, 2_4_0] ,						__UpperCAmelCase  :		List[Any]=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0] ,						__UpperCAmelCase  :		List[str]=4 ,						__UpperCAmelCase  :		int=2.0 ,						__UpperCAmelCase  :		Any=4.0 ,						__UpperCAmelCase  :		int="silu" ,						__UpperCAmelCase  :		Dict=3 ,						__UpperCAmelCase  :		List[Any]=3_2 ,						__UpperCAmelCase  :		Dict=0.1 ,						__UpperCAmelCase  :		List[Any]=0.0 ,						__UpperCAmelCase  :		Optional[Any]=0.1 ,						__UpperCAmelCase  :		int=0.02 ,						__UpperCAmelCase  :		Optional[int]=1E-5 ,						__UpperCAmelCase  :		int=True ,						__UpperCAmelCase  :		Tuple=2_5_6 ,						__UpperCAmelCase  :		Tuple=[6, 1_2, 1_8] ,						__UpperCAmelCase  :		Optional[int]=0.1 ,						__UpperCAmelCase  :		Optional[Any]=2_5_5 ,						**__UpperCAmelCase  :		int ,						)		->       Union[str, Any]:
         """simple docstring"""



         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   A							(       lowerCAmelCase_  ):
   __UpperCAmelCase				:				List[str]          =    version.parse('1.11'  )


   @property
   def     lowercase_   (self  :		Any	)		->       str:
         """simple docstring"""



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


   @property
   def     lowercase_   (self  :		Optional[Any]	)		->       int:
         """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     lowercase_   (self  :		Optional[int]	)		->       Union[str, Any]:
         """simple docstring"""



         return 1E-4




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 EfficientNetImageProcessor





class   A							(       unittest.TestCase  ):


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		Dict ,						__UpperCAmelCase  :		List[Any]=1_3 ,						__UpperCAmelCase  :		int=3 ,						__UpperCAmelCase  :		int=1_8 ,						__UpperCAmelCase  :		Tuple=3_0 ,						__UpperCAmelCase  :		List[Any]=4_0_0 ,						__UpperCAmelCase  :		Any=True ,						__UpperCAmelCase  :		Dict=None ,						__UpperCAmelCase  :		Optional[Any]=True ,						__UpperCAmelCase  :		Tuple=[0.5, 0.5, 0.5] ,						__UpperCAmelCase  :		Optional[Any]=[0.5, 0.5, 0.5] ,						)		->       Any:
         """simple docstring"""



         UpperCAmelCase__			=						size if size is not None else {"height": 1_8, "width": 1_8}
         UpperCAmelCase__			=						parent
         UpperCAmelCase__			=						batch_size
         UpperCAmelCase__			=						num_channels
         UpperCAmelCase__			=						image_size
         UpperCAmelCase__			=						min_resolution
         UpperCAmelCase__			=						max_resolution
         UpperCAmelCase__			=						do_resize
         UpperCAmelCase__			=						size
         UpperCAmelCase__			=						do_normalize
         UpperCAmelCase__			=						image_mean
         UpperCAmelCase__			=						image_std





   def     lowercase_   (self  :		Optional[int]	)		->       Optional[Any]:
         """simple docstring"""



         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }





@require_torch
@require_vision
class   A							(       UpperCAmelCase_	,       unittest.TestCase  ):
   __UpperCAmelCase				:				Dict          =    EfficientNetImageProcessor if is_vision_available() else None


   def     lowercase_   (self  :		str	)		->       int:
         """simple docstring"""



         UpperCAmelCase__			=						EfficientNetImageProcessorTester(self	)


   @property
   def     lowercase_   (self  :		Tuple	)		->       Optional[Any]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def     lowercase_   (self  :		Tuple	)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(__UpperCAmelCase ,						"image_mean"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase ,						"image_std"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase ,						"do_normalize"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase ,						"do_resize"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase ,						"size"	)	)


   def     lowercase_   (self  :		Tuple	)		->       str:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size ,						{"height": 1_8, "width": 1_8}	)

         UpperCAmelCase__			=						self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2	)
         self.assertEqual(image_processor.size ,						{"height": 4_2, "width": 4_2}	)


   def     lowercase_   (self  :		List[Any]	)		->       Optional[int]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         UpperCAmelCase__			=						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__UpperCAmelCase	)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase ,						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.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,						)

         # Test batched
         UpperCAmelCase__			=						image_processing(__UpperCAmelCase ,						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.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,						)


   def     lowercase_   (self  :		Tuple	)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         UpperCAmelCase__			=						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__UpperCAmelCase ,						numpify=__UpperCAmelCase	)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase ,						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.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,						)

         # Test batched
         UpperCAmelCase__			=						image_processing(__UpperCAmelCase ,						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.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,						)





   def     lowercase_   (self  :		Optional[int]	)		->       str:
         """simple docstring"""



         UpperCAmelCase__			=						self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         UpperCAmelCase__			=						prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__UpperCAmelCase ,						torchify=__UpperCAmelCase	)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase ,						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.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,						)

         # Test batched
         UpperCAmelCase__			=						image_processing(__UpperCAmelCase ,						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.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,						)



