
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				int						=    list(lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    list(lowerCamelCase    )
				UpperCamelCase_:				Any						=    0
				for i in range(len(lowerCamelCase    )    ):
								if lista[i] != lista[i]:
												count += 1
												UpperCamelCase_:				Optional[Any]						=    """_"""
				if count > 1:
								return False
				else:
								return "".join(lowerCamelCase    )







def 							A__       (    lowerCamelCase    )	->						List[Any]:
				UpperCamelCase_:				str						=    []
				while True:
								UpperCamelCase_:				int						=    ["""$"""] * len(lowerCamelCase    )
								UpperCamelCase_:				Optional[Any]						=    []
								for i in range(len(lowerCamelCase    )    ):
												for j in range(i + 1  ,     len(lowerCamelCase    )    ):
																UpperCamelCase_:				Dict						=    compare_string(binary[i]  ,     binary[j]    )
																if k is False:
																				UpperCamelCase_:				Tuple						=    """*"""
																				UpperCamelCase_:				int						=    """*"""
																				temp.append("""X"""    )
								for i in range(len(lowerCamelCase    )    ):
												if checka[i] == "$":
																pi.append(binary[i]    )
								if len(lowerCamelCase    ) == 0:
												return pi
								UpperCamelCase_:				Optional[int]						=    list(set(lowerCamelCase    )    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				int						=    []
				for minterm in minterms:
								UpperCamelCase_:				List[Any]						=    """"""
								for _ in range(lowerCamelCase    ):
												UpperCamelCase_:				str						=    str(minterm % 2    ) + string
												minterm //= 2
								temp.append(lowerCamelCase    )
				return temp







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						int:
				UpperCamelCase_:				List[str]						=    list(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    list(lowerCamelCase    )
				UpperCamelCase_:				Any						=    0
				for i in range(len(lowerCamelCase    )    ):
								if lista[i] != lista[i]:
												count_n += 1
				return count_n == count







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Any:
				UpperCamelCase_:				List[Any]						=    []
				UpperCamelCase_:				List[Any]						=    [0] * len(lowerCamelCase    )
				for i in range(len(chart[0]    )    ):
								UpperCamelCase_:				List[Any]						=    0
								UpperCamelCase_:				List[str]						=    -1
								for j in range(len(lowerCamelCase    )    ):
												if chart[j][i] == 1:
																count += 1
																UpperCamelCase_:				Any						=    j
								if count == 1:
												UpperCamelCase_:				Union[str, Any]						=    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    )    ):
																								UpperCamelCase_:				Tuple						=    0
												temp.append(prime_implicants[i]    )
				while True:
								UpperCamelCase_:				Dict						=    0
								UpperCamelCase_:				List[Any]						=    -1
								UpperCamelCase_:				Union[str, Any]						=    0
								for i in range(len(lowerCamelCase    )    ):
												UpperCamelCase_:				List[str]						=    chart[i].count(1    )
												if count_n > max_n:
																UpperCamelCase_:				str						=    count_n
																UpperCamelCase_:				Optional[Any]						=    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    )    ):
																				UpperCamelCase_:				Dict						=    0







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:
				UpperCamelCase_:				Union[str, Any]						=    [[0 for x in range(len(lowerCamelCase    )    )] for x in range(len(lowerCamelCase    )    )]
				for i in range(len(lowerCamelCase    )    ):
								UpperCamelCase_:				Optional[int]						=    prime_implicants[i].count("""_"""    )
								for j in range(len(lowerCamelCase    )    ):
												if is_for_table(prime_implicants[i]  ,     binary[j]  ,     lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    1

				return chart







def 							A__       (    )	->						Optional[int]:
				UpperCamelCase_:				Optional[int]						=    int(input("""Enter the no. of variables\n"""    )    )
				UpperCamelCase_:				Optional[Any]						=    [
				    float(lowerCamelCase    )
				    for x in input(
				        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""    ).split()
				]
				UpperCamelCase_:				List[str]						=    decimal_to_binary(lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				int						=    check(lowerCamelCase    )
				print("""Prime Implicants are:"""    )
				print(lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    prime_implicant_chart(lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				Dict						=    selection(lowerCamelCase  ,     lowerCamelCase    )
				print("""Essential Prime Implicants are:"""    )
				print(lowerCamelCase    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()













"""simple docstring"""


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__magic_name__       :					List[str]						=			logging.get_logger(__name__)

__magic_name__       :					Any						=			{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[int]										=  """gptj"""
			__lowerCAmelCase     :				Optional[Any]										=  {
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}







			def __init__(      self						,   _A=5_0_4_0_0						,   _A=2_0_4_8						,   _A=4_0_9_6						,   _A=2_8						,   _A=1_6						,   _A=6_4						,   _A=None						,   _A="gelu_new"						,   _A=0.0						,   _A=0.0						,   _A=0.0						,   _A=1e-5						,   _A=0.02						,   _A=True						,   _A=5_0_2_5_6						,   _A=5_0_2_5_6						,   _A=False						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			vocab_size
									UpperCamelCase				:		Any				   =			n_positions
									UpperCamelCase				:		List[str]				   =			n_embd
									UpperCamelCase				:		List[str]				   =			n_layer
									UpperCamelCase				:		Optional[int]				   =			n_head
									UpperCamelCase				:		int				   =			n_inner
									UpperCamelCase				:		Optional[Any]				   =			rotary_dim
									UpperCamelCase				:		Optional[int]				   =			activation_function
									UpperCamelCase				:		str				   =			resid_pdrop
									UpperCamelCase				:		Union[str, Any]				   =			embd_pdrop
									UpperCamelCase				:		Optional[Any]				   =			attn_pdrop
									UpperCamelCase				:		Optional[int]				   =			layer_norm_epsilon
									UpperCamelCase				:		Any				   =			initializer_range
									UpperCamelCase				:		Optional[int]				   =			use_cache

									UpperCamelCase				:		List[Any]				   =			bos_token_id
									UpperCamelCase				:		List[str]				   =			eos_token_id

									super().__init__(
									    bos_token_id=_A						,   eos_token_id=_A						,   tie_word_embeddings=_A						,   **_A    )






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = "default"						,   _A = None						,   _A = False						,   ):






									'''simple docstring'''







									super().__init__(_A						,   task=_A						,   patching_specs=_A						,   use_past=_A    )
									if not getattr(self._config						,   """pad_token_id"""						,   _A    ):
															# TODO: how to do that better?
															UpperCamelCase				:		Optional[Any]				   =			0







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
									if self.use_past:
															self.fill_with_past_key_values_(_A						,   direction="""inputs"""    )
															UpperCamelCase				:		Any				   =			{0: """batch""", 1: """past_sequence + sequence"""}
									else:
															UpperCamelCase				:		List[Any]				   =			{0: """batch""", 1: """sequence"""}

									return common_inputs







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._config.n_layer







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._config.n_head







			def   _a      (      self						,   _A						,   _A = -1						,   _A = -1						,   _A = False						,   _A = None						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			super(_A						,   self    ).generate_dummy_inputs(
									    _A						,   batch_size=_A						,   seq_length=_A						,   is_pair=_A						,   framework=_A    )

									# We need to order the input in the way they appears in the forward()
									UpperCamelCase				:		Optional[Any]				   =			OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
															else:
																					import torch

																					UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			common_inputs["""input_ids"""].shape
																					# Not using the same length for past_key_values
																					UpperCamelCase				:		Dict				   =			seqlen + 2
																					UpperCamelCase				:		Union[str, Any]				   =			(
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					UpperCamelCase				:		List[Any]				   =			[
																					    (torch.zeros(_A    ), torch.zeros(_A    )) for _ in range(self.num_layers    )
																					]

									UpperCamelCase				:		str				   =			common_inputs["""attention_mask"""]
									if self.use_past:
															UpperCamelCase				:		Any				   =			ordered_inputs["""attention_mask"""].dtype
															UpperCamelCase				:		List[Any]				   =			torch.cat(
															    [ordered_inputs["""attention_mask"""], torch.ones(_A						,   _A						,   dtype=_A    )]						,   dim=1    )

									return ordered_inputs







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return 1_3



