




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str | Literal[False]:
						_UpperCAmelCase      =			list(snake_case       )
						_UpperCAmelCase      =			list(snake_case       )
						_UpperCAmelCase      =			0
						for i in range(len(snake_case       )       ):
												if lista[i] != lista[i]:
																		count += 1
																		_UpperCAmelCase      =			"""_"""
						if count > 1:
												return False
						else:
												return "".join(snake_case       )




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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				list[str]:
						_UpperCAmelCase      =			[]
						for minterm in minterms:
												_UpperCAmelCase      =			""""""
												for _ in range(snake_case       ):
																		_UpperCAmelCase      =			str(minterm % 2       ) + string
																		minterm //= 2
												temp.append(snake_case       )
						return temp




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			list(snake_case       )
						_UpperCAmelCase      =			list(snake_case       )
						_UpperCAmelCase      =			0
						for i in range(len(snake_case       )       ):
												if lista[i] != lista[i]:
																		count_n += 1
						return count_n == count




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				list[str]:
						_UpperCAmelCase      =			[]
						_UpperCAmelCase      =			[0] * len(snake_case       )
						for i in range(len(chart[0]       )       ):
												_UpperCAmelCase      =			0
												_UpperCAmelCase      =			-1
												for j in range(len(snake_case       )       ):
																		if chart[j][i] == 1:
																								count += 1
																								_UpperCAmelCase      =			j
												if count == 1:
																		_UpperCAmelCase      =			1
						for i in range(len(snake_case       )       ):
												if select[i] == 1:
																		for j in range(len(chart[0]       )       ):
																								if chart[i][j] == 1:
																														for k in range(len(snake_case       )       ):
																																				_UpperCAmelCase      =			0
																		temp.append(prime_implicants[i]       )
						while True:
												_UpperCAmelCase      =			0
												_UpperCAmelCase      =			-1
												_UpperCAmelCase      =			0
												for i in range(len(snake_case       )       ):
																		_UpperCAmelCase      =			chart[i].count(1       )
																		if count_n > max_n:
																								_UpperCAmelCase      =			count_n
																								_UpperCAmelCase      =			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(snake_case       )       ):
																														_UpperCAmelCase      =			0




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				list[list[int]]:
						_UpperCAmelCase      =			[[0 for x in range(len(snake_case       )       )] for x in range(len(snake_case       )       )]
						for i in range(len(snake_case       )       ):
												_UpperCAmelCase      =			prime_implicants[i].count("""_"""       )
												for j in range(len(snake_case       )       ):
																		if is_for_table(prime_implicants[i]			,     binary[j]			,     snake_case       ):
																								_UpperCAmelCase      =			1

						return chart




def        _SCREAMING_SNAKE_CASE      (		)						->				None:
						_UpperCAmelCase      =			int(input("""Enter the no. of variables\n"""       )       )
						_UpperCAmelCase      =			[
						    float(snake_case       )
						    for x in input(
						        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
						]
						_UpperCAmelCase      =			decimal_to_binary(snake_case			,     snake_case       )

						_UpperCAmelCase      =			check(snake_case       )
						print("""Prime Implicants are:"""       )
						print(snake_case       )
						_UpperCAmelCase      =			prime_implicant_chart(snake_case			,     snake_case       )

						_UpperCAmelCase      =			selection(snake_case			,     snake_case       )
						print("""Essential Prime Implicants are:"""       )
						print(snake_case       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

a													=			{
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

a													=			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_INIT_CONFIGURATION
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       BertTokenizer






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE="[UNK]"						,		_SCREAMING_SNAKE_CASE="[SEP]"						,		_SCREAMING_SNAKE_CASE="[PAD]"						,		_SCREAMING_SNAKE_CASE="[CLS]"						,		_SCREAMING_SNAKE_CASE="[MASK]"						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(
								    _SCREAMING_SNAKE_CASE						,		tokenizer_file=_SCREAMING_SNAKE_CASE						,		do_lower_case=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		sep_token=_SCREAMING_SNAKE_CASE						,		pad_token=_SCREAMING_SNAKE_CASE						,		cls_token=_SCREAMING_SNAKE_CASE						,		mask_token=_SCREAMING_SNAKE_CASE						,		tokenize_chinese_chars=_SCREAMING_SNAKE_CASE						,		strip_accents=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)

								_UpperCAmelCase      =			json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
								if (
								    normalizer_state.get("""lowercase"""						,		_SCREAMING_SNAKE_CASE			) != do_lower_case
								    or normalizer_state.get("""strip_accents"""						,		_SCREAMING_SNAKE_CASE			) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""						,		_SCREAMING_SNAKE_CASE			) != tokenize_chinese_chars
								):
														_UpperCAmelCase      =			getattr(_SCREAMING_SNAKE_CASE						,		normalizer_state.pop("""type"""			)			)
														_UpperCAmelCase      =			do_lower_case
														_UpperCAmelCase      =			strip_accents
														_UpperCAmelCase      =			tokenize_chinese_chars
														_UpperCAmelCase      =			normalizer_class(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			do_lower_case






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			[self.sep_token_id]
								_UpperCAmelCase      =			[self.cls_token_id]
								if token_ids_a is None:
														return len(cls + token_ids_a + sep			) * [0]
								return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
								return tuple(_SCREAMING_SNAKE_CASE			)