



"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__):



			@register_to_config
			def __init__(			self							:				str   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Dict   ,	_lowercase							:				Dict   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				str   ,	_lowercase							:				Dict = False   ,	)							->					Tuple:
									super().__init__()

									snake_case   :			Optional[Any]						    = nn.Embedding(lowerCamelCase_   ,	lowerCamelCase_		)

									snake_case   :			Any						    = nn.Embedding(lowerCamelCase_   ,	lowerCamelCase_		)
									snake_case   :			Optional[Any]						    = False

									snake_case   :			str						    = nn.Dropout(p=lowerCamelCase_		)

									snake_case   :			List[Any]						    = TaConfig(
									    vocab_size=lowerCamelCase_   ,	d_model=lowerCamelCase_   ,	num_heads=lowerCamelCase_   ,	d_kv=lowerCamelCase_   ,	d_ff=lowerCamelCase_   ,	dropout_rate=lowerCamelCase_   ,	feed_forward_proj=lowerCamelCase_   ,	is_decoder=lowerCamelCase_   ,	is_encoder_decoder=lowerCamelCase_   ,	)

									snake_case   :			str						    = nn.ModuleList()
									for lyr_num in range(lowerCamelCase_		):
															snake_case   :			Tuple						    = TaBlock(lowerCamelCase_		)
															self.encoders.append(lowerCamelCase_		)

									snake_case   :			Union[str, Any]						    = TaLayerNorm(lowerCamelCase_		)
									snake_case   :			Any						    = nn.Dropout(p=lowerCamelCase_		)



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Dict		)							->					List[Any]:
									snake_case   :			List[Any]						    = self.token_embedder(lowerCamelCase_		)

									snake_case   :			Tuple						    = encoder_input_tokens.shape[1]
									snake_case   :			Any						    = torch.arange(lowerCamelCase_   ,	device=encoder_input_tokens.device		)
									x += self.position_encoding(lowerCamelCase_		)

									snake_case   :			List[Any]						    = self.dropout_pre(lowerCamelCase_		)

									# inverted the attention mask
									snake_case   :			Optional[int]						    = encoder_input_tokens.size()
									snake_case   :			Optional[Any]						    = self.get_extended_attention_mask(lowerCamelCase_   ,	lowerCamelCase_		)

									for lyr in self.encoders:
															snake_case   :			List[Any]						    = lyr(lowerCamelCase_   ,	lowerCamelCase_		)[0]
									snake_case   :			str						    = self.layer_norm(lowerCamelCase_		)

									return self.dropout_post(lowerCamelCase_		), encoder_inputs_mask






"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def   _A					(	_a	:						str     ,   _a	:						str				):



      """simple docstring"""






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



def   _A					(	_a	:						list[str]				):



      """simple docstring"""






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



def   _A					(	_a	:						int     ,   _a	:						Sequence[float]				):



      """simple docstring"""






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



def   _A					(	_a	:						str     ,   _a	:						str     ,   _a	:						int				):



      """simple docstring"""






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



def   _A					(	_a	:						list[list[int]]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      A				  =		[0] * len(_a				)
      for i in range(len(chart[0]				)				):
            A				  =		0
            A				  =		-1
            for j in range(len(_a				)				):
                  if chart[j][i] == 1:
                        count += 1
                        A				  =		j
            if count == 1:
                  A				  =		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				  =		0
                  temp.append(prime_implicants[i]				)
      while True:
            A				  =		0
            A				  =		-1
            A				  =		0
            for i in range(len(_a				)				):
                  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(_a				)				):
                              A				  =		0



def   _A					(	_a	:						list[str]     ,   _a	:						list[str]				):



      """simple docstring"""






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

      return chart



def   _A					(	):



      """simple docstring"""






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

      A				  =		check(_a				)
      print("""Prime Implicants are:"""				)
      print(_a				)
      A				  =		prime_implicant_chart(_a     ,   _a				)

      A				  =		selection(_a     ,   _a				)
      print("""Essential Prime Implicants are:"""				)
      print(_a				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




