



"""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				:     Union[str, Any]								=  logging.get_logger(__name__)

lowerCamelCase				:     List[str]								=  {
    """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       ):
    _A       = '''mobilevit'''




    def __init__(   self    ,							A_=3    ,							A_=256    ,							A_=2    ,							A_=[144, 192, 240]    ,							A_=[16, 32, 64, 96, 128, 160, 640]    ,							A_=4    ,							A_=2.0    ,							A_=4.0    ,							A_="silu"    ,							A_=3    ,							A_=32    ,							A_=0.1    ,							A_=0.0    ,							A_=0.1    ,							A_=0.02    ,							A_=1e-5    ,							A_=True    ,							A_=256    ,							A_=[6, 12, 18]    ,							A_=0.1    ,							A_=255    ,							**A_    ,							):





      '''simple docstring'''



      super().__init__(**lowercase_			)

      _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    __snake_case(					_UpperCAmelCase       ):
    _A       = version.parse('''1.11'''       )




    @property
    def   A   (   self			):





      '''simple docstring'''



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




    @property
    def   A   (   self			):





      '''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   A   (   self			):





      '''simple docstring'''



      return 1e-4



import math
import os

import cva
import numpy as np

lowercase_									=    255.0
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      float       ,     SCREAMING_SNAKE_CASE__     :      float )				-> float:





    '''simple docstring'''






    A__        =  np.mean((original - contrast) ** 2 )
    if mse == 0:
        return 100

    return 20 * math.logaa(PIXEL_MAX / math.sqrt(SCREAMING_SNAKE_CASE__ ) )
def 		_snake_case(       )				-> None:





    '''simple docstring'''






    A__        =  os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__ ) )
    # Loading images (original image and compressed image)
    A__        =  cva.imread(os.path.join(SCREAMING_SNAKE_CASE__       ,     'image_data/original_image.png' ) )
    A__        =  cva.imread(os.path.join(SCREAMING_SNAKE_CASE__       ,     'image_data/compressed_image.png' )       ,     1 )

    A__        =  cva.imread(os.path.join(SCREAMING_SNAKE_CASE__       ,     'image_data/PSNR-example-base.png' ) )
    A__        =  cva.imread(
        os.path.join(SCREAMING_SNAKE_CASE__       ,     'image_data/PSNR-example-comp-10.jpg' )       ,     1 )

    # Value expected: 29.73dB
    print('-- First Test --' )
    print(f'PSNR value is {peak_signal_to_noise_ratio(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__ )} dB' )

    # # Value expected: 31.53dB (Wikipedia Example)
    print('\n-- Second Test --' )
    print(f'PSNR value is {peak_signal_to_noise_ratio(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__ )} dB' )


if __name__ == "__main__":
     main()

