

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal




def     __UpperCamelCase			(	_A :				str	,   _A :				List[str]						)							->Tuple:



							"""simple docstring"""
							lowerCamelCase_				     =list(__A						)
							lowerCamelCase_				     =list(__A						)
							lowerCamelCase_				     =0
							for i in range(len(__A						)						):
														if lista[i] != lista[i]:
																					count += 1
																					lowerCamelCase_				     ="""_"""
							if count > 1:
														return False
							else:
														return "".join(__A						)




def     __UpperCamelCase			(	_A :				Optional[Any]						)							->str:



							"""simple docstring"""
							lowerCamelCase_				     =[]
							while True:
														lowerCamelCase_				     =["""$"""] * len(__A						)
														lowerCamelCase_				     =[]
														for i in range(len(__A						)						):
																					for j in range(i + 1	,   len(__A						)						):
																												lowerCamelCase_				     =compare_string(binary[i]	,   binary[j]						)
																												if k is False:
																																			lowerCamelCase_				     ="""*"""
																																			lowerCamelCase_				     ="""*"""
																																			temp.append("""X"""						)
														for i in range(len(__A						)						):
																					if checka[i] == "$":
																												pi.append(binary[i]						)
														if len(__A						) == 0:
																					return pi
														lowerCamelCase_				     =list(set(__A						)						)




def     __UpperCamelCase			(	_A :				Any	,   _A :				Tuple						)							->Any:



							"""simple docstring"""
							lowerCamelCase_				     =[]
							for minterm in minterms:
														lowerCamelCase_				     =""""""
														for _ in range(__A						):
																					lowerCamelCase_				     =str(minterm % 2						) + string
																					minterm //= 2
														temp.append(__A						)
							return temp




def     __UpperCamelCase			(	_A :				Union[str, Any]	,   _A :				List[str]	,   _A :				List[str]						)							->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_				     =list(__A						)
							lowerCamelCase_				     =list(__A						)
							lowerCamelCase_				     =0
							for i in range(len(__A						)						):
														if lista[i] != lista[i]:
																					count_n += 1
							return count_n == count




def     __UpperCamelCase			(	_A :				List[str]	,   _A :				str						)							->Dict:



							"""simple docstring"""
							lowerCamelCase_				     =[]
							lowerCamelCase_				     =[0] * len(__A						)
							for i in range(len(chart[0]						)						):
														lowerCamelCase_				     =0
														lowerCamelCase_				     =-1
														for j in range(len(__A						)						):
																					if chart[j][i] == 1:
																												count += 1
																												lowerCamelCase_				     =j
														if count == 1:
																					lowerCamelCase_				     =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						)						):
																																										lowerCamelCase_				     =0
																					temp.append(prime_implicants[i]						)
							while True:
														lowerCamelCase_				     =0
														lowerCamelCase_				     =-1
														lowerCamelCase_				     =0
														for i in range(len(__A						)						):
																					lowerCamelCase_				     =chart[i].count(1						)
																					if count_n > max_n:
																												lowerCamelCase_				     =count_n
																												lowerCamelCase_				     =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						)						):
																																			lowerCamelCase_				     =0




def     __UpperCamelCase			(	_A :				Any	,   _A :				Optional[int]						)							->Any:



							"""simple docstring"""
							lowerCamelCase_				     =[[0 for x in range(len(__A						)						)] for x in range(len(__A						)						)]
							for i in range(len(__A						)						):
														lowerCamelCase_				     =prime_implicants[i].count("""_"""						)
														for j in range(len(__A						)						):
																					if is_for_table(prime_implicants[i]	,   binary[j]	,   __A						):
																												lowerCamelCase_				     =1

							return chart




def     __UpperCamelCase			(	)							->List[str]:



							"""simple docstring"""
							lowerCamelCase_				     =int(input("""Enter the no. of variables\n"""						)						)
							lowerCamelCase_				     =[
							    float(__A						)
							    for x in input(
							        """Enter the decimal representation of Minterms \'Spaces Separated\'\n"""						).split()
							]
							lowerCamelCase_				     =decimal_to_binary(__A	,   __A						)

							lowerCamelCase_				     =check(__A						)
							print("""Prime Implicants are:"""						)
							print(__A						)
							lowerCamelCase_				     =prime_implicant_chart(__A	,   __A						)

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





'''simple docstring'''





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     if isinstance(__A  ,	__A):
          _a      =  np.full((len(__A), sequence_length, 2)  ,	__A)
     else:
          _a      =  np.full((len(__A), sequence_length)  ,	__A)

     for i, tensor in enumerate(__A):
          if padding_side == "right":
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]
          else:
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]

     return out_tensor.tolist()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  ord(__A)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     _a      =  unicodedata.category(__A)
     if cat.startswith('''P'''):
          return True
     return False






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       PreTrainedTokenizerBase
 __lowerCamelCase    :       Union[bool, str, PaddingStrategy]          =       True
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       int          =       -100
 __lowerCamelCase    :       str          =       "pt"



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      import torch

      _a      =  '''label''' if '''label''' in features[0].keys() else '''labels'''
      _a      =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =  self.tokenizer.pad(
          A ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='''pt''' if labels is None else None ,   )

      if labels is None:
           return batch

      _a      =  torch.tensor(batch['''entity_ids''']       ).shape[1]
      _a      =  self.tokenizer.padding_side
      if padding_side == "right":
           _a      =  [
               list(A       ) + [self.label_pad_token_id] * (sequence_length - len(A       )) for label in labels
           ]
      else:
           _a      =  [
               [self.label_pad_token_id] * (sequence_length - len(A       )) + list(A       ) for label in labels
           ]

      _a      =  [feature['''ner_tags'''] for feature in features]
      _a      =  padding_tensor(A ,   -1 ,   A ,   A       )
      _a      =  [feature['''original_entity_spans'''] for feature in features]
      _a      =  padding_tensor(A ,   (-1, -1) ,   A ,   A       )
      _a      =  {k: torch.tensor(A ,   dtype=torch.intaa       ) for k, v in batch.items()}

      return batch



