


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






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






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






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     str					=					[]
			for minterm in minterms:
						SCREAMING_SNAKE_CASE_		:     int					=					''
						for _ in range(A__				):
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					str(minterm % 2				) + string
									minterm //= 2
						temp.append(A__				)
			return temp






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					list(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					list(A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					0
			for i in range(len(A__				)				):
						if lista[i] != lista[i]:
									count_n += 1
			return count_n == count






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






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					[[0 for x in range(len(A__				)				)] for x in range(len(A__				)				)]
			for i in range(len(A__				)				):
						SCREAMING_SNAKE_CASE_		:     Tuple					=					prime_implicants[i].count('_'				)
						for j in range(len(A__				)				):
									if is_for_table(prime_implicants[i],      binary[j],      A__				):
												SCREAMING_SNAKE_CASE_		:     int					=					1

			return chart






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     int					=					int(input('Enter the no. of variables\n'				)				)
			SCREAMING_SNAKE_CASE_		:     int					=					[
			    float(A__				)
			    for x in input(
			        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'				).split()
			]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					decimal_to_binary(A__,      A__				)

			SCREAMING_SNAKE_CASE_		:     str					=					check(A__				)
			print('Prime Implicants are:'				)
			print(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					prime_implicant_chart(A__,      A__				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					selection(A__,      A__				)
			print('Essential Prime Implicants are:'				)
			print(A__				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__			:      Tuple								=logging.get_logger(__name__)

lowerCAmelCase__			:      Optional[int]								={
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git_vision_model"""






							def __init__(      self			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__="quick_gelu"			,						lowerCAmelCase__=1E-5			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_channels
										SCREAMING_SNAKE_CASE_		:     Tuple					=					patch_size
										SCREAMING_SNAKE_CASE_		:     Any					=					image_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     List[str]					=					attention_dropout
										SCREAMING_SNAKE_CASE_		:     Any					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										cls._set_token_in_kwargs(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					cls.get_config_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get('model_type'       ) == "git":
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					config_dict['vision_config']

										if "model_type" in config_dict and hasattr(cls			,						'model_type'       ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
													    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

										return cls.from_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git"""






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3_0_5_2_2			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1E-12			,						lowerCAmelCase__=0			,						lowerCAmelCase__="absolute"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=1_0_1			,						lowerCAmelCase__=1_0_2			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						pad_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if vision_config is None:
													SCREAMING_SNAKE_CASE_		:     Dict					=					{}
													logger.info('vision_config is None. initializing the GitVisionConfig with default values.'       )

										SCREAMING_SNAKE_CASE_		:     str					=					GitVisionConfig(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Any					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Any					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     int					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     int					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     List[str]					=					position_embedding_type
										SCREAMING_SNAKE_CASE_		:     List[str]					=					use_cache
										SCREAMING_SNAKE_CASE_		:     Any					=					tie_word_embeddings
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_image_with_embedding

										SCREAMING_SNAKE_CASE_		:     List[str]					=					bos_token_id
										SCREAMING_SNAKE_CASE_		:     str					=					eos_token_id






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					copy.deepcopy(self.__dict__       )
										SCREAMING_SNAKE_CASE_		:     str					=					self.vision_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.__class__.model_type
										return output





