


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

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


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip_vision_model"""


     def __init__(	self  ,       __A=1408  ,       __A=6144  ,       __A=39  ,       __A=16  ,       __A=224  ,       __A=14  ,       __A="gelu"  ,       __A=1E-6  ,       __A=0.0  ,       __A=1E-10  ,       __A=True  ,       **__A  ,       ):
        super().__init__(**__A    )

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


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       **__A    ):
        cls._set_token_in_kwargs(__A    )

        __a	,    __a					=  cls.get_config_dict(__A  ,       **__A    )

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""    ) == "instructblip":
           __a					=  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     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip_qformer"""


     def __init__(	self  ,       __A=30522  ,       __A=768  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=0.02  ,       __A=1E-12  ,       __A=0  ,       __A="absolute"  ,       __A=2  ,       __A=1408  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       **__A    )

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


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       **__A    ):
        cls._set_token_in_kwargs(__A    )

        __a	,    __a					=  cls.get_config_dict(__A  ,       **__A    )

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""    ) == "instructblip":
           __a					=  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     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip"""
     _lowerCamelCase   =      True


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=None  ,       __A=32  ,       **__A    ):
        super().__init__(**__A    )

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

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

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

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

        __a					=  self.text_config.tie_word_embeddings
        __a					=  self.text_config.is_encoder_decoder

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


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       __A  ,       __A  ,       **__A  ,       ):
        return cls(
            vision_config=vision_config.to_dict()  ,       qformer_config=qformer_config.to_dict()  ,       text_config=text_config.to_dict()  ,       **__A  ,       )


     def 	snake_case_  (	self    ):
        __a					=  copy.deepcopy(self.__dict__    )
        __a					=  self.vision_config.to_dict()
        __a					=  self.qformer_config.to_dict()
        __a					=  self.text_config.to_dict()
        __a					=  self.__class__.model_type
        return output







import math




def  a   (lowerCAmelCase__   ):

   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(lowerCAmelCase__   ) + 1   )		,				6   ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True




def  a   (lowerCAmelCase__ = 10_001   ):

   try:
      __a					=  int(lowerCAmelCase__   )
   except (TypeError, ValueError):
      raise TypeError("""Parameter nth must be int or castable to int."""   ) from None
   if nth <= 0:
      raise ValueError("""Parameter nth must be greater than or equal to one."""   )
   __a					=  []
   __a					=  2
   while len(lowerCAmelCase__   ) < nth:
      if is_prime(lowerCAmelCase__   ):
         primes.append(lowerCAmelCase__   )
         num += 1
      else:
         num += 1
   return primes[len(lowerCAmelCase__   ) - 1]


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



