






import copy
import os
from typing import Union

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


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

snake_case__      :	Tuple       	=      {
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}


class 						_a       (  lowerCamelCase__    ):






  """simple docstring"""





  A_						=   """blip_text_model"""




  def __init__(     self		,		_UpperCAmelCase=30524		,		_UpperCAmelCase=768		,		_UpperCAmelCase=768		,		_UpperCAmelCase=3072		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=8		,		_UpperCAmelCase=512		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0		,		_UpperCAmelCase=102		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		**_UpperCAmelCase		,		)			-> Dict:
     super().__init__(
         pad_token_id=__lowerCamelCase		,		bos_token_id=__lowerCamelCase		,		eos_token_id=__lowerCamelCase		,		sep_token_id=__lowerCamelCase		,		**__lowerCamelCase		,		)

     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     encoder_hidden_size
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     projection_dim
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     max_position_embeddings
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     is_decoder
     UpperCamelCase_									=     use_cache




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> "PretrainedConfig":
     cls._set_token_in_kwargs(__lowerCamelCase )

     UpperCamelCase_									=     cls.get_config_dict(__lowerCamelCase		,		**__lowerCamelCase )

     # get the text config dict if we are loading from BlipConfig
     if config_dict.get('model_type' ) == "blip":
        UpperCamelCase_									=     config_dict['''text_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 						_a       (  lowerCamelCase__    ):






  """simple docstring"""





  A_						=   """blip_vision_model"""




  def __init__(     self		,		_UpperCAmelCase=768		,		_UpperCAmelCase=3072		,		_UpperCAmelCase=512		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=384		,		_UpperCAmelCase=16		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=1e-10		,		**_UpperCAmelCase		,		)			-> List[Any]:
     super().__init__(**__lowerCamelCase )

     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     projection_dim
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     attention_dropout
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     hidden_act




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> "PretrainedConfig":
     cls._set_token_in_kwargs(__lowerCamelCase )

     UpperCamelCase_									=     cls.get_config_dict(__lowerCamelCase		,		**__lowerCamelCase )

     # get the vision config dict if we are loading from BlipConfig
     if config_dict.get('model_type' ) == "blip":
        UpperCamelCase_									=     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 						_a       (  lowerCamelCase__    ):






  """simple docstring"""





  A_						=   """blip"""
  A_						=   True




  def __init__(     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=512		,		_UpperCAmelCase=2.6_5_9_2		,		_UpperCAmelCase=256		,		**_UpperCAmelCase		,		)			-> Dict:
     super().__init__(**__lowerCamelCase )

     if text_config is None:
        UpperCamelCase_									=     {}
        logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.' )

     if vision_config is None:
        UpperCamelCase_									=     {}
        logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.' )

     UpperCamelCase_									=     BlipTextConfig(**__lowerCamelCase )
     UpperCamelCase_									=     BlipVisionConfig(**__lowerCamelCase )

     UpperCamelCase_									=     self.vision_config.hidden_size

     UpperCamelCase_									=     projection_dim
     UpperCamelCase_									=     logit_scale_init_value
     UpperCamelCase_									=     1.0
     UpperCamelCase_									=     0.0_2
     UpperCamelCase_									=     image_text_hidden_size




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> Optional[int]:
     return cls(text_config=text_config.to_dict()		,		vision_config=vision_config.to_dict()		,		**__lowerCamelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     copy.deepcopy(self.__dict__ )
     UpperCamelCase_									=     self.text_config.to_dict()
     UpperCamelCase_									=     self.vision_config.to_dict()
     UpperCamelCase_									=     self.__class__.model_type
     return output













snake_case__      :	List[Any]       	=      """Tobias Carryer"""

from time import time


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=int(time() ) )			-> Tuple:  # noqa: B008
     UpperCamelCase_									=     multiplier
     UpperCamelCase_									=     increment
     UpperCamelCase_									=     modulo
     UpperCamelCase_									=     seed




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     (self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__      :	List[Any]       	=      LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
 while True:
  print(lcg.next_number())





