

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


UpperCAmelCase_							:					Optional[int]		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				['''pixel_values''']




	def __init__(     self						:     Dict					,      __A						:     bool = True					,      __A						:     Optional[Dict[str, int]] = None					,      __A						:     PILImageResampling = PILImageResampling.BILINEAR					,      __A						:     bool = True					,      __A						:     Dict[str, int] = None					,      __A						:     bool = True					,      __A						:     Union[int, float] = 1 / 255					,      __A						:     bool = True					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      **__A						:     int					,      ):
		super().__init__(**__A							)
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 256}
		__A :			Dict		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			str		    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			str		    =			do_resize
		__A :			Dict		    =			size
		__A :			Any		    =			resample
		__A :			Optional[Any]		    =			do_center_crop
		__A :			List[str]		    =			crop_size
		__A :			Optional[int]		    =			do_rescale
		__A :			int		    =			rescale_factor
		__A :			Union[str, Any]		    =			do_normalize
		__A :			int		    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		__A :			Dict		    =			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     PILImageResampling = PILImageResampling.BICUBIC					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     List[Any]					,      ):
		__A :			str		    =			get_size_dict(__A					,      default_to_square=__A							)
		if "shortest_edge" not in size:
			raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		__A :			Dict		    =			get_resize_output_image_size(__A					,      size=size["""shortest_edge"""]					,      default_to_square=__A							)
		return resize(__A					,      size=__A					,      resample=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     str					,      ):
		__A :			str		    =			get_size_dict(__A							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""							)
		return center_crop(__A					,      size=(size["""height"""], size["""width"""])					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     np.ndarray					,      __A						:     float					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Optional[int]							):
		return rescale(__A					,      scale=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     np.ndarray					,      __A						:     Union[float, List[float]]					,      __A						:     Union[float, List[float]]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Tuple					,      ):
		return normalize(__A					,      mean=__A					,      std=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     ImageInput					,      __A						:     Optional[bool] = None					,      __A						:     Dict[str, int] = None					,      __A						:     PILImageResampling = None					,      __A						:     bool = None					,      __A						:     Dict[str, int] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[float] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[str, TensorType]] = None					,      __A						:     Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **__A						:     Optional[int]					,      ):
		__A :			List[str]		    =			do_resize if do_resize is not None else self.do_resize
		__A :			Any		    =			size if size is not None else self.size
		__A :			Union[str, Any]		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			Tuple		    =			resample if resample is not None else self.resample
		__A :			Tuple		    =			do_center_crop if do_center_crop is not None else self.do_center_crop
		__A :			List[Any]		    =			crop_size if crop_size is not None else self.crop_size
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			Tuple		    =			do_rescale if do_rescale is not None else self.do_rescale
		__A :			Optional[Any]		    =			rescale_factor if rescale_factor is not None else self.rescale_factor
		__A :			Optional[int]		    =			do_normalize if do_normalize is not None else self.do_normalize
		__A :			Optional[int]		    =			image_mean if image_mean is not None else self.image_mean
		__A :			List[str]		    =			image_std if image_std is not None else self.image_std

		__A :			Union[str, Any]		    =			make_list_of_images(__A							)

		if not valid_images(__A							):
			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:
			raise ValueError("""Size must be specified if do_resize is True."""							)

		if do_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

		if do_rescale and rescale_factor is None:
			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

		# All transformations expect numpy arrays.
		__A :			Union[str, Any]		    =			[to_numpy_array(__A							) for image in images]

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

		if do_center_crop:
			__A :			Optional[Any]		    =			[self.center_crop(image=__A					,      size=__A							) for image in images]

		if do_rescale:
			__A :			List[Any]		    =			[self.rescale(image=__A					,      scale=__A							) for image in images]

		if do_normalize:
			__A :			Any		    =			[self.normalize(image=__A					,      mean=__A					,      std=__A							) for image in images]

		__A :			int		    =			[to_channel_dimension_format(__A					,      __A							) for image in images]

		__A :			Tuple		    =			{"""pixel_values""": images}
		return BatchFeature(data=__A					,      tensor_type=__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[Tuple] = None							):
		__A :			Union[str, Any]		    =			outputs.logits

		# Resize logits and compute semantic segmentation maps
		if target_sizes is not None:
			if len(__A							) != len(__A							):
				raise ValueError(
				    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

			if is_torch_tensor(__A							):
				__A :			str		    =			target_sizes.numpy()

			__A :			int		    =			[]

			for idx in range(len(__A							)							):
				__A :			Any		    =			torch.nn.functional.interpolate(
				    logits[idx].unsqueeze(dim=0							)					,      size=target_sizes[idx]					,      mode="""bilinear"""					,      align_corners=__A							)
				__A :			Union[str, Any]		    =			resized_logits[0].argmax(dim=0							)
				semantic_segmentation.append(__A							)
		else:
			__A :			List[str]		    =			logits.argmax(dim=1							)
			__A :			Tuple		    =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

		return semantic_segmentation








from __future__ import annotations

from collections.abc import Sequence
from typing import Literal
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str       )					->				str | Literal[False]:
	__A :			Tuple		    =			list(a__       )
	__A :			Optional[int]		    =			list(a__       )
	__A :			int		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count += 1
			__A :			int		    =			"""_"""
	if count > 1:
		return False
	else:
		return "".join(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]       )					->				list[str]:
	__A :			Optional[Any]		    =			[]
	while True:
		__A :			Tuple		    =			["""$"""] * len(a__       )
		__A :			Union[str, Any]		    =			[]
		for i in range(len(a__       )       ):
			for j in range(i + 1			,len(a__       )       ):
				__A :			int		    =			compare_string(binary[i]			,binary[j]       )
				if k is False:
					__A :			List[str]		    =			"""*"""
					__A :			Any		    =			"""*"""
					temp.append("""X"""       )
		for i in range(len(a__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(a__       ) == 0:
			return pi
		__A :			Optional[Any]		    =			list(set(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Sequence[float]       )					->				list[str]:
	__A :			List[str]		    =			[]
	for minterm in minterms:
		__A :			List[Any]		    =			""""""
		for _ in range(a__       ):
			__A :			Union[str, Any]		    =			str(minterm % 2       ) + string
			minterm //= 2
		temp.append(a__       )
	return temp
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   int       )					->				bool:
	__A :			Optional[Any]		    =			list(a__       )
	__A :			Tuple		    =			list(a__       )
	__A :			Any		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[list[int]]			,a__    :   list[str]       )					->				list[str]:
	__A :			Optional[int]		    =			[]
	__A :			Tuple		    =			[0] * len(a__       )
	for i in range(len(chart[0]       )       ):
		__A :			str		    =			0
		__A :			Any		    =			-1
		for j in range(len(a__       )       ):
			if chart[j][i] == 1:
				count += 1
				__A :			Optional[Any]		    =			j
		if count == 1:
			__A :			int		    =			1
	for i in range(len(a__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(a__       )       ):
						__A :			List[str]		    =			0
			temp.append(prime_implicants[i]       )
	while True:
		__A :			Optional[Any]		    =			0
		__A :			Any		    =			-1
		__A :			int		    =			0
		for i in range(len(a__       )       ):
			__A :			List[Any]		    =			chart[i].count(1       )
			if count_n > max_n:
				__A :			Dict		    =			count_n
				__A :			Tuple		    =			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(a__       )       ):
					__A :			Union[str, Any]		    =			0
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]			,a__    :   list[str]       )					->				list[list[int]]:
	__A :			Any		    =			[[0 for x in range(len(a__       )       )] for x in range(len(a__       )       )]
	for i in range(len(a__       )       ):
		__A :			List[Any]		    =			prime_implicants[i].count("""_"""       )
		for j in range(len(a__       )       ):
			if is_for_table(prime_implicants[i]			,binary[j]			,a__       ):
				__A :			Union[str, Any]		    =			1

	return chart
def 							__SCREAMING_SNAKE_CASE     (   )					->				None:
	__A :			Any		    =			int(input("""Enter the no. of variables\n"""       )       )
	__A :			List[str]		    =			[
	    float(a__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	__A :			Dict		    =			decimal_to_binary(a__			,a__       )

	__A :			Union[str, Any]		    =			check(a__       )
	print("""Prime Implicants are:"""       )
	print(a__       )
	__A :			Optional[Any]		    =			prime_implicant_chart(a__			,a__       )

	__A :			Any		    =			selection(a__			,a__       )
	print("""Essential Prime Implicants are:"""       )
	print(a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





