from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase			: List[str]							   =		logging.get_logger(__name__)

__lowerCamelCase			: str							   =		{
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"donut-swin"

   lowerCAmelCase_														=							{
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }




   def __init__(    self			:				int       ,	_lowercase			:				List[str]=2_24       ,	_lowercase			:				List[str]=4       ,	_lowercase			:				Union[str, Any]=3       ,	_lowercase			:				Dict=96       ,	_lowercase			:				int=[2, 2, 6, 2]       ,	_lowercase			:				Dict=[3, 6, 12, 24]       ,	_lowercase			:				List[str]=7       ,	_lowercase			:				Dict=4.0       ,	_lowercase			:				Optional[int]=True       ,	_lowercase			:				str=0.0       ,	_lowercase			:				str=0.0       ,	_lowercase			:				Optional[int]=0.1       ,	_lowercase			:				List[str]="gelu"       ,	_lowercase			:				Tuple=False       ,	_lowercase			:				int=0.02       ,	_lowercase			:				Optional[Any]=1E-5       ,	**_lowercase			:				Dict       ,	):



     """simple docstring"""






     super().__init__(**_lowercase      )

     SCREAMING_SNAKE_CASE__										=      image_size
     SCREAMING_SNAKE_CASE__										=      patch_size
     SCREAMING_SNAKE_CASE__										=      num_channels
     SCREAMING_SNAKE_CASE__										=      embed_dim
     SCREAMING_SNAKE_CASE__										=      depths
     SCREAMING_SNAKE_CASE__										=      len(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      num_heads
     SCREAMING_SNAKE_CASE__										=      window_size
     SCREAMING_SNAKE_CASE__										=      mlp_ratio
     SCREAMING_SNAKE_CASE__										=      qkv_bias
     SCREAMING_SNAKE_CASE__										=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__										=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__										=      drop_path_rate
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      use_absolute_embeddings
     SCREAMING_SNAKE_CASE__										=      layer_norm_eps
     SCREAMING_SNAKE_CASE__										=      initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     SCREAMING_SNAKE_CASE__										=      int(embed_dim * 2 ** (len(_lowercase      ) - 1)      )

import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
