

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							str					)	->					str | Literal[False]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list[str]					)	->					list[str]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							Sequence[float]					)	->					list[str]:




				'''simple docstring'''





				A__						=	[]
				for minterm in minterms:
								A__						=	""
								for _ in range(__lowerCAmelCase					):
												A__						=	str(minterm % 2					) + string
												minterm //= 2
								temp.append(__lowerCAmelCase					)
				return temp



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							int					)	->					bool:




				'''simple docstring'''





				A__						=	list(__lowerCAmelCase					)
				A__						=	list(__lowerCAmelCase					)
				A__						=	0
				for i in range(len(__lowerCAmelCase					)					):
								if lista[i] != lista[i]:
												count_n += 1
				return count_n == count



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list[list[int]]		,				SCREAMING_SNAKE_CASE_:							list[str]					)	->					list[str]:




				'''simple docstring'''





				A__						=	[]
				A__						=	[0] * len(__lowerCAmelCase					)
				for i in range(len(chart[0]					)					):
								A__						=	0
								A__						=	-1
								for j in range(len(__lowerCAmelCase					)					):
												if chart[j][i] == 1:
																count += 1
																A__						=	j
								if count == 1:
												A__						=	1
				for i in range(len(__lowerCAmelCase					)					):
								if select[i] == 1:
												for j in range(len(chart[0]					)					):
																if chart[i][j] == 1:
																				for k in range(len(__lowerCAmelCase					)					):
																								A__						=	0
												temp.append(prime_implicants[i]					)
				while True:
								A__						=	0
								A__						=	-1
								A__						=	0
								for i in range(len(__lowerCAmelCase					)					):
												A__						=	chart[i].count(1					)
												if count_n > max_n:
																A__						=	count_n
																A__						=	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(__lowerCAmelCase					)					):
																				A__						=	0



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list[str]		,				SCREAMING_SNAKE_CASE_:							list[str]					)	->					list[list[int]]:




				'''simple docstring'''





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

				return chart



def     lowerCAmelCase__							(		)	->					None:




				'''simple docstring'''





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

				A__						=	check(__lowerCAmelCase					)
				print("Prime Implicants are:"					)
				print(__lowerCAmelCase					)
				A__						=	prime_implicant_chart(__lowerCAmelCase		,				__lowerCAmelCase					)

				A__						=	selection(__lowerCAmelCase		,				__lowerCAmelCase					)
				print("Essential Prime Implicants are:"					)
				print(__lowerCAmelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[int]												=    logging.getLogger(__name__)

@dataclass
class    lowerCAmelCase__     :
		a__       :						str
		a__       :						List[str]
		a__       :						Optional[List[str]]





@dataclass
class    lowerCAmelCase__     :
		a__       :						List[int]
		a__       :						List[int]
		a__       :						Optional[List[int]]        =		None
		a__       :						Optional[List[int]]        =		None





class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Optional[Any]        =		"""train"""
		a__       :						Optional[int]        =		"""dev"""
		a__       :						Dict        =		"""test"""





class    lowerCAmelCase__     :






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[Split, str]  )     ->							List[InputExample]:
						raise NotImplementedError






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						raise NotImplementedError







		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							List[InputExample]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							List[str]="[CLS]"							,			SCREAMING_SNAKE_CASE__		:							Tuple=1							,			SCREAMING_SNAKE_CASE__		:							str="[SEP]"							,			SCREAMING_SNAKE_CASE__		:							List[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Tuple=0							,			SCREAMING_SNAKE_CASE__		:							int=0							,			SCREAMING_SNAKE_CASE__		:							str=-1_00							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=0							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			)     ->							List[InputFeatures]:
						__lowerCamelCase    					=	{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__  )}

						__lowerCamelCase    					=	[]
						for ex_index, example in enumerate(SCREAMING_SNAKE_CASE__  ):
										if ex_index % 1_00_00 == 0:
														logger.info('''Writing example %d of %d'''							,			SCREAMING_SNAKE_CASE__							,			len(SCREAMING_SNAKE_CASE__  )  )

										__lowerCamelCase    					=	[]
										__lowerCamelCase    					=	[]
										for word, label in zip(example.words							,			example.labels  ):
														__lowerCamelCase    					=	tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )

														# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
														if len(SCREAMING_SNAKE_CASE__  ) > 0:
																		tokens.extend(SCREAMING_SNAKE_CASE__  )
																		# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																		label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(SCREAMING_SNAKE_CASE__  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
										__lowerCamelCase    					=	tokenizer.num_special_tokens_to_add()
										if len(SCREAMING_SNAKE_CASE__  ) > max_seq_length - special_tokens_count:
														__lowerCamelCase    					=	tokens[: (max_seq_length - special_tokens_count)]
														__lowerCamelCase    					=	label_ids[: (max_seq_length - special_tokens_count)]

										# The convention in BERT is:
										# (a) For sequence pairs:
										#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
										#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
										# (b) For single sequences:
										#  tokens:   [CLS] the dog is hairy . [SEP]
										#  type_ids:   0   0   0   0  0     0   0
										#
										# Where "type_ids" are used to indicate whether this is the first
										# sequence or the second sequence. The embedding vectors for `type=0` and
										# `type=1` were learned during pre-training and are added to the wordpiece
										# embedding vector (and position vector). This is not *strictly* necessary
										# since the [SEP] token unambiguously separates the sequences, but it makes
										# it easier for the model to learn the concept of sequences.
										#
										# For classification tasks, the first vector (corresponding to [CLS]) is
										# used as the "sentence vector". Note that this only makes sense because
										# the entire model is fine-tuned.
										tokens += [sep_token]
										label_ids += [pad_token_label_id]
										if sep_token_extra:
														# roberta uses an extra separator b/w pairs of sentences
														tokens += [sep_token]
														label_ids += [pad_token_label_id]
										__lowerCamelCase    					=	[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE__  )

										if cls_token_at_end:
														tokens += [cls_token]
														label_ids += [pad_token_label_id]
														segment_ids += [cls_token_segment_id]
										else:
														__lowerCamelCase    					=	[cls_token] + tokens
														__lowerCamelCase    					=	[pad_token_label_id] + label_ids
														__lowerCamelCase    					=	[cls_token_segment_id] + segment_ids

										__lowerCamelCase    					=	tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )

										# The mask has 1 for real tokens and 0 for padding tokens. Only real
										# tokens are attended to.
										__lowerCamelCase    					=	[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE__  )

										# Zero-pad up to the sequence length.
										__lowerCamelCase    					=	max_seq_length - len(SCREAMING_SNAKE_CASE__  )
										if pad_on_left:
														__lowerCamelCase    					=	([pad_token] * padding_length) + input_ids
														__lowerCamelCase    					=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
														__lowerCamelCase    					=	([pad_token_segment_id] * padding_length) + segment_ids
														__lowerCamelCase    					=	([pad_token_label_id] * padding_length) + label_ids
										else:
														input_ids += [pad_token] * padding_length
														input_mask += [0 if mask_padding_with_zero else 1] * padding_length
														segment_ids += [pad_token_segment_id] * padding_length
														label_ids += [pad_token_label_id] * padding_length

										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length
										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length
										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length
										assert len(SCREAMING_SNAKE_CASE__  ) == max_seq_length

										if ex_index < 5:
														logger.info('''*** Example ***'''  )
														logger.info('''guid: %s'''							,			example.guid  )
														logger.info('''tokens: %s'''							,			''' '''.join([str(SCREAMING_SNAKE_CASE__  ) for x in tokens]  )  )
														logger.info('''input_ids: %s'''							,			''' '''.join([str(SCREAMING_SNAKE_CASE__  ) for x in input_ids]  )  )
														logger.info('''input_mask: %s'''							,			''' '''.join([str(SCREAMING_SNAKE_CASE__  ) for x in input_mask]  )  )
														logger.info('''segment_ids: %s'''							,			''' '''.join([str(SCREAMING_SNAKE_CASE__  ) for x in segment_ids]  )  )
														logger.info('''label_ids: %s'''							,			''' '''.join([str(SCREAMING_SNAKE_CASE__  ) for x in label_ids]  )  )

										if "token_type_ids" not in tokenizer.model_input_names:
														__lowerCamelCase    					=	None

										features.append(
										    InputFeatures(
										        input_ids=SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			token_type_ids=SCREAMING_SNAKE_CASE__							,			label_ids=SCREAMING_SNAKE_CASE__  )  )
						return features


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					class    lowerCAmelCase__     (	__lowercase			):
							a__       :						List[InputFeatures]
							a__       :						int        =		nn.CrossEntropyLoss().ignore_index






							def __init__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							Union[str, Any]:
											# Load data features from cache or dataset file
											__lowerCamelCase    					=	os.path.join(
											    SCREAMING_SNAKE_CASE__							,			'''cached_{}_{}_{}'''.format(mode.value							,			tokenizer.__class__.__name__							,			str(SCREAMING_SNAKE_CASE__  )  )							,			)

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											__lowerCamelCase    					=	cached_features_file + '''.lock'''
											with FileLock(SCREAMING_SNAKE_CASE__  ):
															if os.path.exists(SCREAMING_SNAKE_CASE__  ) and not overwrite_cache:
																			logger.info(f'''Loading features from cached file {cached_features_file}'''  )
																			__lowerCamelCase    					=	torch.load(SCREAMING_SNAKE_CASE__  )
															else:
																			logger.info(f'''Creating features from dataset file at {data_dir}'''  )
																			__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
																			# TODO clean up all this to leverage built-in features of tokenizers
																			__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
																			    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			cls_token_at_end=bool(model_type in ['''xlnet''']  )							,			cls_token=tokenizer.cls_token							,			cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0							,			sep_token=tokenizer.sep_token							,			sep_token_extra=SCREAMING_SNAKE_CASE__							,			pad_on_left=bool(tokenizer.padding_side == '''left'''  )							,			pad_token=tokenizer.pad_token_id							,			pad_token_segment_id=tokenizer.pad_token_type_id							,			pad_token_label_id=self.pad_token_label_id							,			)
																			logger.info(f'''Saving features into cached file {cached_features_file}'''  )
																			torch.save(self.features							,			SCREAMING_SNAKE_CASE__  )






							def __len__(		self		:							Dict  )     ->							str:
											return len(self.features  )







							def __getitem__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							InputFeatures:
											return self.features[i]


if is_tf_available():
					import tensorflow as tf

					class    lowerCAmelCase__     :
							a__       :						List[InputFeatures]
							a__       :						int        =		-100






							def __init__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							List[Any]:
											__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
											# TODO clean up all this to leverage built-in features of tokenizers
											__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
											    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			cls_token_at_end=bool(model_type in ['''xlnet''']  )							,			cls_token=tokenizer.cls_token							,			cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0							,			sep_token=tokenizer.sep_token							,			sep_token_extra=SCREAMING_SNAKE_CASE__							,			pad_on_left=bool(tokenizer.padding_side == '''left'''  )							,			pad_token=tokenizer.pad_token_id							,			pad_token_segment_id=tokenizer.pad_token_type_id							,			pad_token_label_id=self.pad_token_label_id							,			)

											def gen():
															for ex in self.features:
																			if ex.token_type_ids is None:
																							yield (
																							    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																							    ex.label_ids,
																							)
																			else:
																							yield (
																							    {
																							        "input_ids": ex.input_ids,
																							        "attention_mask": ex.attention_mask,
																							        "token_type_ids": ex.token_type_ids,
																							    },
																							    ex.label_ids,
																							)

											if "token_type_ids" not in tokenizer.model_input_names:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE__							,			({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)							,			(
															        {'''input_ids''': tf.TensorShape([None]  ), '''attention_mask''': tf.TensorShape([None]  )},
															        tf.TensorShape([None]  ),
															    )							,			)
											else:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE__							,			({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)							,			(
															        {
															            '''input_ids''': tf.TensorShape([None]  ),
															            '''attention_mask''': tf.TensorShape([None]  ),
															            '''token_type_ids''': tf.TensorShape([None]  ),
															        },
															        tf.TensorShape([None]  ),
															    )							,			)






							def  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
											__lowerCamelCase    					=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

											return self.dataset






							def __len__(		self		:							List[Any]  )     ->							Any:
											return len(self.features  )







							def __getitem__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							InputFeatures:
											return self.features[i]



