



'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_UpperCamelCase					:  Optional[Any]        =logging.get_logger(__name__)

def  lowerCamelCase_					(       A_    ,				A_				):
	__lowerCamelCase		       =		b.T
	__lowerCamelCase		       =		np.sum(np.square(UpperCamelCase_				)    ,				axis=1				)
	__lowerCamelCase		       =		np.sum(np.square(UpperCamelCase_				)    ,				axis=0				)
	__lowerCamelCase		       =		np.matmul(UpperCamelCase_    ,				UpperCamelCase_				)
	__lowerCamelCase		       =		aa[:, None] - 2 * ab + ba[None, :]
	return d

def  lowerCamelCase_					(       A_    ,				A_				):
	__lowerCamelCase		       =		x.reshape(-1    ,				3				)
	__lowerCamelCase		       =		squared_euclidean_distance(UpperCamelCase_    ,				UpperCamelCase_				)
	return np.argmin(UpperCamelCase_    ,				axis=1				)







class 						_SCREAMING_SNAKE_CASE      ( _snake_case						):






			"""simple docstring"""







			SCREAMING_SNAKE_CASE_   			=				['pixel_values']


			def __init__(   self       , _snake_case = None       , _snake_case = True       , _snake_case = None       , _snake_case = PILImageResampling.BILINEAR       , _snake_case = True       , _snake_case = True       , **_snake_case       , ):







				"""simple docstring"""



				super().__init__(**__lowerCamelCase    )
				__lowerCamelCase		       =		size if size is not None else {'''height''': 2_56, '''width''': 2_56}
				__lowerCamelCase		       =		get_size_dict(__lowerCamelCase    )
				__lowerCamelCase		       =		np.array(__lowerCamelCase    ) if clusters is not None else None
				__lowerCamelCase		       =		do_resize
				__lowerCamelCase		       =		size
				__lowerCamelCase		       =		resample
				__lowerCamelCase		       =		do_normalize
				__lowerCamelCase		       =		do_color_quantize


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case       , _snake_case = PILImageResampling.BILINEAR       , _snake_case = None       , **_snake_case       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		get_size_dict(__lowerCamelCase    )
				if "height" not in size or "width" not in size:
					raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''    )
				return resize(
				    __lowerCamelCase       , size=(size['''height'''], size['''width'''])       , resample=__lowerCamelCase       , data_format=__lowerCamelCase       , **__lowerCamelCase    )


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case = None       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		rescale(image=__lowerCamelCase       , scale=1 / 1_27.5       , data_format=__lowerCamelCase    )
				__lowerCamelCase		       =		image - 1
				return image


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case = None       , _snake_case = None       , _snake_case = None       , _snake_case = None       , _snake_case = None       , _snake_case = None       , _snake_case = None       , _snake_case = ChannelDimension.FIRST       , **_snake_case       , ):







				"""simple docstring"""



				__lowerCamelCase		       =		do_resize if do_resize is not None else self.do_resize
				__lowerCamelCase		       =		size if size is not None else self.size
				__lowerCamelCase		       =		get_size_dict(__lowerCamelCase    )
				__lowerCamelCase		       =		resample if resample is not None else self.resample
				__lowerCamelCase		       =		do_normalize if do_normalize is not None else self.do_normalize
				__lowerCamelCase		       =		do_color_quantize if do_color_quantize is not None else self.do_color_quantize
				__lowerCamelCase		       =		clusters if clusters is not None else self.clusters
				__lowerCamelCase		       =		np.array(__lowerCamelCase    )

				__lowerCamelCase		       =		make_list_of_images(__lowerCamelCase    )

				if not valid_images(__lowerCamelCase    ):
					raise ValueError(
					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

				if do_resize and size is None or resample is None:
					raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

				if do_color_quantize and clusters is None:
					raise ValueError('''Clusters must be specified if do_color_quantize is True.'''    )

				# All transformations expect numpy arrays.
				__lowerCamelCase		       =		[to_numpy_array(__lowerCamelCase    ) for image in images]

				if do_resize:
					__lowerCamelCase		       =		[self.resize(image=__lowerCamelCase       , size=__lowerCamelCase       , resample=__lowerCamelCase    ) for image in images]

				if do_normalize:
					__lowerCamelCase		       =		[self.normalize(image=__lowerCamelCase    ) for image in images]

				if do_color_quantize:
					__lowerCamelCase		       =		[to_channel_dimension_format(__lowerCamelCase       , ChannelDimension.LAST    ) for image in images]
					# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
					__lowerCamelCase		       =		np.array(__lowerCamelCase    )
					__lowerCamelCase		       =		color_quantize(__lowerCamelCase       , __lowerCamelCase    ).reshape(images.shape[:-1]    )

					# flatten to (batch_size, height*width)
					__lowerCamelCase		       =		images.shape[0]
					__lowerCamelCase		       =		images.reshape(__lowerCamelCase       , -1    )

					# We need to convert back to a list of images to keep consistent behaviour across processors.
					__lowerCamelCase		       =		list(__lowerCamelCase    )
				else:
					__lowerCamelCase		       =		[to_channel_dimension_format(__lowerCamelCase       , __lowerCamelCase    ) for image in images]

				__lowerCamelCase		       =		{'''input_ids''': images}
				return BatchFeature(data=__lowerCamelCase       , tensor_type=__lowerCamelCase    )





'''simple docstring'''





from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str						):
   UpperCAmelCase__	:Any      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count += 1
         UpperCAmelCase__	:Union[str, Any]      =      '''_'''
   if count > 1:
      return False
   else:
      return "".join(UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:int      =      []
   while True:
      UpperCAmelCase__	:Dict      =      ['''$'''] * len(UpperCamelCase_						)
      UpperCAmelCase__	:List[str]      =      []
      for i in range(len(UpperCamelCase_						)						):
         for j in range(i + 1,   len(UpperCamelCase_						)						):
            UpperCAmelCase__	:Optional[Any]      =      compare_string(binary[i],   binary[j]						)
            if k is False:
               UpperCAmelCase__	:Any      =      '''*'''
               UpperCAmelCase__	:List[Any]      =      '''*'''
               temp.append('''X'''						)
      for i in range(len(UpperCamelCase_						)						):
         if checka[i] == "$":
            pi.append(binary[i]						)
      if len(UpperCamelCase_						) == 0:
         return pi
      UpperCAmelCase__	:Tuple      =      list(set(UpperCamelCase_						)						)


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Sequence[float]						):
   UpperCAmelCase__	:int      =      []
   for minterm in minterms:
      UpperCAmelCase__	:int      =      ''''''
      for _ in range(UpperCamelCase_						):
         UpperCAmelCase__	:Optional[int]      =      str(minterm % 2						) + string
         minterm //= 2
      temp.append(UpperCamelCase_						)
   return temp


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str,   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:Dict      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count_n += 1
   return count_n == count


def        a__     (					UpperCamelCase_						:      list[list[int]],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:Optional[Any]      =      []
   UpperCAmelCase__	:List[Any]      =      [0] * len(UpperCamelCase_						)
   for i in range(len(chart[0]						)						):
      UpperCAmelCase__	:Optional[Any]      =      0
      UpperCAmelCase__	:Union[str, Any]      =      -1
      for j in range(len(UpperCamelCase_						)						):
         if chart[j][i] == 1:
            count += 1
            UpperCAmelCase__	:Any      =      j
      if count == 1:
         UpperCAmelCase__	:Any      =      1
   for i in range(len(UpperCamelCase_						)						):
      if select[i] == 1:
         for j in range(len(chart[0]						)						):
            if chart[i][j] == 1:
               for k in range(len(UpperCamelCase_						)						):
                  UpperCAmelCase__	:int      =      0
         temp.append(prime_implicants[i]						)
   while True:
      UpperCAmelCase__	:Optional[int]      =      0
      UpperCAmelCase__	:Dict      =      -1
      UpperCAmelCase__	:Optional[Any]      =      0
      for i in range(len(UpperCamelCase_						)						):
         UpperCAmelCase__	:str      =      chart[i].count(1						)
         if count_n > max_n:
            UpperCAmelCase__	:Any      =      count_n
            UpperCAmelCase__	:List[Any]      =      i

      if max_n == 0:
         return temp

      temp.append(prime_implicants[rem]						)

      for i in range(len(chart[0]						)						):
         if chart[rem][i] == 1:
            for j in range(len(UpperCamelCase_						)						):
               UpperCAmelCase__	:Optional[int]      =      0


def        a__     (					UpperCamelCase_						:      list[str],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:List[str]      =      [[0 for x in range(len(UpperCamelCase_						)						)] for x in range(len(UpperCamelCase_						)						)]
   for i in range(len(UpperCamelCase_						)						):
      UpperCAmelCase__	:Tuple      =      prime_implicants[i].count('''_'''						)
      for j in range(len(UpperCamelCase_						)						):
         if is_for_table(prime_implicants[i],   binary[j],   UpperCamelCase_						):
            UpperCAmelCase__	:List[str]      =      1

   return chart


def        a__     (					):
   UpperCAmelCase__	:int      =      int(input('''Enter the no. of variables\n'''						)						)
   UpperCAmelCase__	:Tuple      =      [
       float(UpperCamelCase_						)
       for x in input(
           '''Enter the decimal representation of Minterms \'Spaces Separated\'\n'''						).split()
   ]
   UpperCAmelCase__	:Union[str, Any]      =      decimal_to_binary(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Optional[Any]      =      check(UpperCamelCase_						)
   print('''Prime Implicants are:'''						)
   print(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      prime_implicant_chart(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Dict      =      selection(UpperCamelCase_,   UpperCamelCase_						)
   print('''Essential Prime Implicants are:'''						)
   print(UpperCamelCase_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



