

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_							:					Union[str, Any]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					List[str]		 =   {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[Any]     					=				'''blip_2_vision_model'''




	def __init__(     self						:     Optional[Any]					,      __A						:     List[Any]=1408					,      __A						:     Optional[Any]=6144					,      __A						:     int=39					,      __A						:     Tuple=16					,      __A						:     Tuple=224					,      __A						:     Any=14					,      __A						:     Any="gelu"					,      __A						:     Any=0.0_0_0_0_1					,      __A						:     Dict=0.0					,      __A						:     Tuple=1e-1_0					,      __A						:     Any=True					,      **__A						:     int					,      ):
		super().__init__(**__A							)

		__A :			Dict		    =			hidden_size
		__A :			int		    =			intermediate_size
		__A :			List[Any]		    =			num_hidden_layers
		__A :			Tuple		    =			num_attention_heads
		__A :			List[Any]		    =			patch_size
		__A :			Any		    =			image_size
		__A :			Dict		    =			initializer_range
		__A :			List[Any]		    =			attention_dropout
		__A :			Optional[Any]		    =			layer_norm_eps
		__A :			Dict		    =			hidden_act
		__A :			str		    =			qkv_bias




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Any					,      __A						:     Union[str, os.PathLike]					,      **__A						:     Any							):
		cls._set_token_in_kwargs(__A							)

		__A    ,				__A :			Optional[Any]		    =			cls.get_config_dict(__A					,      **__A							)

		# get the vision config dict if we are loading from Blip2Config
		if config_dict.get("""model_type"""							) == "blip-2":
			__A :			str		    =			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(__A					,      **__A							)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				'''blip_2_qformer'''




	def __init__(     self						:     Union[str, Any]					,      __A						:     List[Any]=3_0522					,      __A						:     Union[str, Any]=768					,      __A						:     Optional[Any]=12					,      __A						:     List[str]=12					,      __A						:     Optional[int]=3072					,      __A						:     Dict="gelu"					,      __A						:     Optional[int]=0.1					,      __A						:     Any=0.1					,      __A						:     List[str]=512					,      __A						:     Optional[Any]=0.0_2					,      __A						:     List[Any]=1e-1_2					,      __A						:     List[str]=0					,      __A						:     Optional[Any]="absolute"					,      __A						:     Any=2					,      __A						:     Union[str, Any]=1408					,      **__A						:     Optional[Any]					,      ):
		super().__init__(pad_token_id=__A					,      **__A							)

		__A :			List[Any]		    =			vocab_size
		__A :			int		    =			hidden_size
		__A :			Dict		    =			num_hidden_layers
		__A :			Union[str, Any]		    =			num_attention_heads
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Tuple		    =			intermediate_size
		__A :			Tuple		    =			hidden_dropout_prob
		__A :			Any		    =			attention_probs_dropout_prob
		__A :			int		    =			max_position_embeddings
		__A :			List[Any]		    =			initializer_range
		__A :			Optional[Any]		    =			layer_norm_eps
		__A :			Optional[int]		    =			position_embedding_type
		__A :			List[str]		    =			cross_attention_frequency
		__A :			List[Any]		    =			encoder_hidden_size




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Union[str, Any]					,      __A						:     Union[str, os.PathLike]					,      **__A						:     List[Any]							):
		cls._set_token_in_kwargs(__A							)

		__A    ,				__A :			Any		    =			cls.get_config_dict(__A					,      **__A							)

		# get the qformer config dict if we are loading from Blip2Config
		if config_dict.get("""model_type"""							) == "blip-2":
			__A :			int		    =			config_dict["""qformer_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(__A					,      **__A							)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				'''blip-2'''
	_lowercase		:   Any     					=				True




	def __init__(     self						:     Tuple					,      __A						:     Dict=None					,      __A						:     str=None					,      __A						:     int=None					,      __A						:     Union[str, Any]=32					,      **__A						:     int							):
		super().__init__(**__A							)

		if vision_config is None:
			__A :			List[Any]		    =			{}
			logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""							)

		if qformer_config is None:
			__A :			Union[str, Any]		    =			{}
			logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""							)

		if text_config is None:
			__A :			Optional[Any]		    =			{}
			logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""							)

		__A :			Any		    =			BlipaVisionConfig(**__A							)
		__A :			Tuple		    =			BlipaQFormerConfig(**__A							)
		__A :			Any		    =			text_config["""model_type"""] if """model_type""" in text_config else """opt"""
		__A :			str		    =			CONFIG_MAPPING[text_model_type](**__A							)

		__A :			List[Any]		    =			self.text_config.tie_word_embeddings
		__A :			int		    =			self.text_config.is_encoder_decoder

		__A :			Union[str, Any]		    =			num_query_tokens
		__A :			int		    =			self.vision_config.hidden_size
		__A :			List[str]		    =			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
		__A :			Optional[int]		    =			1.0
		__A :			Optional[Any]		    =			0.0_2




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Optional[int]					,      __A						:     BlipaVisionConfig					,      __A						:     BlipaQFormerConfig					,      __A						:     PretrainedConfig					,      **__A						:     str					,      ):
		return cls(
		    vision_config=vision_config.to_dict()					,      qformer_config=qformer_config.to_dict()					,      text_config=text_config.to_dict()					,      **__A					,      )




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Union[str, Any]		    =			copy.deepcopy(self.__dict__							)
		__A :			int		    =			self.vision_config.to_dict()
		__A :			Optional[Any]		    =			self.qformer_config.to_dict()
		__A :			Optional[Any]		    =			self.text_config.to_dict()
		__A :			Any		    =			self.__class__.model_type
		return output








from __future__ import annotations

from collections.abc import Sequence
from typing import Literal
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str       )					->				str | Literal[False]:
	__A :			Tuple		    =			list(a__       )
	__A :			Optional[int]		    =			list(a__       )
	__A :			int		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count += 1
			__A :			int		    =			"""_"""
	if count > 1:
		return False
	else:
		return "".join(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]       )					->				list[str]:
	__A :			Optional[Any]		    =			[]
	while True:
		__A :			Tuple		    =			["""$"""] * len(a__       )
		__A :			Union[str, Any]		    =			[]
		for i in range(len(a__       )       ):
			for j in range(i + 1			,len(a__       )       ):
				__A :			int		    =			compare_string(binary[i]			,binary[j]       )
				if k is False:
					__A :			List[str]		    =			"""*"""
					__A :			Any		    =			"""*"""
					temp.append("""X"""       )
		for i in range(len(a__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(a__       ) == 0:
			return pi
		__A :			Optional[Any]		    =			list(set(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Sequence[float]       )					->				list[str]:
	__A :			List[str]		    =			[]
	for minterm in minterms:
		__A :			List[Any]		    =			""""""
		for _ in range(a__       ):
			__A :			Union[str, Any]		    =			str(minterm % 2       ) + string
			minterm //= 2
		temp.append(a__       )
	return temp
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   int       )					->				bool:
	__A :			Optional[Any]		    =			list(a__       )
	__A :			Tuple		    =			list(a__       )
	__A :			Any		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[list[int]]			,a__    :   list[str]       )					->				list[str]:
	__A :			Optional[int]		    =			[]
	__A :			Tuple		    =			[0] * len(a__       )
	for i in range(len(chart[0]       )       ):
		__A :			str		    =			0
		__A :			Any		    =			-1
		for j in range(len(a__       )       ):
			if chart[j][i] == 1:
				count += 1
				__A :			Optional[Any]		    =			j
		if count == 1:
			__A :			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__       )       ):
						__A :			List[str]		    =			0
			temp.append(prime_implicants[i]       )
	while True:
		__A :			Optional[Any]		    =			0
		__A :			Any		    =			-1
		__A :			int		    =			0
		for i in range(len(a__       )       ):
			__A :			List[Any]		    =			chart[i].count(1       )
			if count_n > max_n:
				__A :			Dict		    =			count_n
				__A :			Tuple		    =			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 :			Union[str, Any]		    =			0
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]			,a__    :   list[str]       )					->				list[list[int]]:
	__A :			Any		    =			[[0 for x in range(len(a__       )       )] for x in range(len(a__       )       )]
	for i in range(len(a__       )       ):
		__A :			List[Any]		    =			prime_implicants[i].count("""_"""       )
		for j in range(len(a__       )       ):
			if is_for_table(prime_implicants[i]			,binary[j]			,a__       ):
				__A :			Union[str, Any]		    =			1

	return chart
def 							__SCREAMING_SNAKE_CASE     (   )					->				None:
	__A :			Any		    =			int(input("""Enter the no. of variables\n"""       )       )
	__A :			List[str]		    =			[
	    float(a__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	__A :			Dict		    =			decimal_to_binary(a__			,a__       )

	__A :			Union[str, Any]		    =			check(a__       )
	print("""Prime Implicants are:"""       )
	print(a__       )
	__A :			Optional[Any]		    =			prime_implicant_chart(a__			,a__       )

	__A :			Any		    =			selection(a__			,a__       )
	print("""Essential Prime Implicants are:"""       )
	print(a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





