






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


a__       =     logging.get_logger(__name__)

a__       =     {
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  '''instructblip_vision_model'''



				def __init__(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Dict=1_4_0_8			,		UpperCamelCase__    :    Any=6_1_4_4			,		UpperCamelCase__    :    Tuple=3_9			,		UpperCamelCase__    :    List[Any]=1_6			,		UpperCamelCase__    :    Dict=2_2_4			,		UpperCamelCase__    :    Any=1_4			,		UpperCamelCase__    :    List[str]="gelu"			,		UpperCamelCase__    :    List[str]=1E-6			,		UpperCamelCase__    :    List[Any]=0.0			,		UpperCamelCase__    :    Dict=1E-10			,		UpperCamelCase__    :    Optional[Any]=True			,		**UpperCamelCase__    :    Union[str, Any]			,		):






											'''simple docstring'''







											super().__init__(**UpperCamelCase__)

											snake_case__	  =       hidden_size
											snake_case__	  =       intermediate_size
											snake_case__	  =       num_hidden_layers
											snake_case__	  =       num_attention_heads
											snake_case__	  =       patch_size
											snake_case__	  =       image_size
											snake_case__	  =       initializer_range
											snake_case__	  =       attention_dropout
											snake_case__	  =       layer_norm_eps
											snake_case__	  =       hidden_act
											snake_case__	  =       qkv_bias



				@classmethod
				def 					__magic_name__							(      cls    :    int			,		UpperCamelCase__    :    Union[str, os.PathLike]			,		**UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







											cls._set_token_in_kwargs(UpperCamelCase__)

											snake_case__  , snake_case__	  =       cls.get_config_dict(UpperCamelCase__			,		**UpperCamelCase__)

											# get the vision config dict if we are loading from InstructBlipConfig
											if config_dict.get("""model_type""") == "instructblip":
																		snake_case__	  =       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(UpperCamelCase__			,		**UpperCamelCase__)


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Any      =  '''instructblip_qformer'''



				def __init__(      self    :    Optional[Any]			,		UpperCamelCase__    :    str=3_0_5_2_2			,		UpperCamelCase__    :    List[Any]=7_6_8			,		UpperCamelCase__    :    Union[str, Any]=1_2			,		UpperCamelCase__    :    List[Any]=1_2			,		UpperCamelCase__    :    Dict=3_0_7_2			,		UpperCamelCase__    :    int="gelu"			,		UpperCamelCase__    :    Any=0.1			,		UpperCamelCase__    :    Optional[Any]=0.1			,		UpperCamelCase__    :    Union[str, Any]=5_1_2			,		UpperCamelCase__    :    Optional[int]=0.02			,		UpperCamelCase__    :    Union[str, Any]=1E-12			,		UpperCamelCase__    :    Tuple=0			,		UpperCamelCase__    :    Optional[int]="absolute"			,		UpperCamelCase__    :    List[str]=2			,		UpperCamelCase__    :    List[Any]=1_4_0_8			,		**UpperCamelCase__    :    int			,		):






											'''simple docstring'''







											super().__init__(pad_token_id=UpperCamelCase__			,		**UpperCamelCase__)

											snake_case__	  =       vocab_size
											snake_case__	  =       hidden_size
											snake_case__	  =       num_hidden_layers
											snake_case__	  =       num_attention_heads
											snake_case__	  =       hidden_act
											snake_case__	  =       intermediate_size
											snake_case__	  =       hidden_dropout_prob
											snake_case__	  =       attention_probs_dropout_prob
											snake_case__	  =       max_position_embeddings
											snake_case__	  =       initializer_range
											snake_case__	  =       layer_norm_eps
											snake_case__	  =       position_embedding_type
											snake_case__	  =       cross_attention_frequency
											snake_case__	  =       encoder_hidden_size



				@classmethod
				def 					__magic_name__							(      cls    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, os.PathLike]			,		**UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







											cls._set_token_in_kwargs(UpperCamelCase__)

											snake_case__  , snake_case__	  =       cls.get_config_dict(UpperCamelCase__			,		**UpperCamelCase__)

											# get the qformer config dict if we are loading from InstructBlipConfig
											if config_dict.get("""model_type""") == "instructblip":
																		snake_case__	  =       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(UpperCamelCase__			,		**UpperCamelCase__)


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Dict      =  '''instructblip'''
				_lowercase				:   str      =  True



				def __init__(      self    :    int			,		UpperCamelCase__    :    int=None			,		UpperCamelCase__    :    Union[str, Any]=None			,		UpperCamelCase__    :    List[str]=None			,		UpperCamelCase__    :    Any=3_2			,		**UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







											super().__init__(**UpperCamelCase__)

											if vision_config is None:
																		snake_case__	  =       {}
																		logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""")

											if qformer_config is None:
																		snake_case__	  =       {}
																		logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""")

											if text_config is None:
																		snake_case__	  =       {}
																		logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""")

											snake_case__	  =       InstructBlipVisionConfig(**UpperCamelCase__)
											snake_case__	  =       InstructBlipQFormerConfig(**UpperCamelCase__)
											snake_case__	  =       text_config["""model_type"""] if """model_type""" in text_config else """opt"""
											snake_case__	  =       CONFIG_MAPPING[text_model_type](**UpperCamelCase__)

											snake_case__	  =       self.text_config.tie_word_embeddings
											snake_case__	  =       self.text_config.is_encoder_decoder

											snake_case__	  =       num_query_tokens
											snake_case__	  =       self.vision_config.hidden_size
											snake_case__	  =       self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
											snake_case__	  =       1.0
											snake_case__	  =       0.02



				@classmethod
				def 					__magic_name__							(      cls    :    Optional[Any]			,		UpperCamelCase__    :    InstructBlipVisionConfig			,		UpperCamelCase__    :    InstructBlipQFormerConfig			,		UpperCamelCase__    :    PretrainedConfig			,		**UpperCamelCase__    :    List[Any]			,		):






											'''simple docstring'''







											return cls(
											    vision_config=vision_config.to_dict()			,		qformer_config=qformer_config.to_dict()			,		text_config=text_config.to_dict()			,		**UpperCamelCase__			,		)



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       copy.deepcopy(self.__dict__)
											snake_case__	  =       self.vision_config.to_dict()
											snake_case__	  =       self.qformer_config.to_dict()
											snake_case__	  =       self.text_config.to_dict()
											snake_case__	  =       self.__class__.model_type
											return output








import math
from collections.abc import Iterator
from itertools import takewhile

def _UpperCAmelCase   (	a						:	int ):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5  ,					int(math.sqrt(a ) + 1 )  ,					6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def _UpperCAmelCase   (	):
							snake_case__	  =       2
							while True:
														if is_prime(a ):
																					yield num
														num += 1

def _UpperCAmelCase   (	a						:	int = 200_0000 ):
							return sum(takewhile(lambda a : x < n  ,					prime_generator() ) )


if __name__ == "__main__":
	print(F'''{solution() = }''')
