

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


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

lowerCAmelCase     :   str						     =  {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Any       =  '''instructblip_vision_model'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=1408 ,					_SCREAMING_SNAKE_CASE=6144 ,					_SCREAMING_SNAKE_CASE=39 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE=224 ,					_SCREAMING_SNAKE_CASE=14 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=1e-6 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=1e-10 ,					_SCREAMING_SNAKE_CASE=True ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)

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



  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     cls.get_config_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get('model_type'					) == "instructblip":
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  '''instructblip_qformer'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=3_0522 ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=512 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1e-12 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE="absolute" ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=1408 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







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

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



  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  str           =     cls.get_config_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get('model_type'					) == "instructblip":
              SCREAMING_SNAKE_CASE_				:  Optional[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(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[Any]       =  '''instructblip'''
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  True

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=32 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)

        if vision_config is None:
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {}
              logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

        if qformer_config is None:
              SCREAMING_SNAKE_CASE_				:  str           =     {}
              logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

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

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     InstructBlipVisionConfig(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     InstructBlipQFormerConfig(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     text_config['model_type'] if 'model_type' in text_config else 'opt'
        SCREAMING_SNAKE_CASE_				:  Tuple           =     CONFIG_MAPPING[text_model_type](**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.text_config.tie_word_embeddings
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.text_config.is_encoder_decoder

        SCREAMING_SNAKE_CASE_				:  int           =     num_query_tokens
        SCREAMING_SNAKE_CASE_				:  Any           =     self.vision_config.hidden_size
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        SCREAMING_SNAKE_CASE_				:  Dict           =     1.0
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     0.02

  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







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



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     copy.deepcopy(self.__dict__					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.vision_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.qformer_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.text_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  str           =     self.__class__.model_type
        return output









from __future__ import annotations

import math

lowerCAmelCase     :   List[str]						     =  '2020.9.26'
lowerCAmelCase     :   List[Any]						     =  'xcodz-dot, cclaus, dhruvmanila'

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      if not all(isinstance(a					,   (float, int)  ) for val in locals().values()  ):
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     f"Input values must either be float or int: {list(locals().values()  )}"
            raise TypeError(a  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ((x * distance) / (z + distance)) * scale
      SCREAMING_SNAKE_CASE_				:  int           =     ((y * distance) / (z + distance)) * scale
      return projected_x, projected_y

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      if not isinstance(a					,   a  ):
            raise TypeError('Axis must be a str'  )
      SCREAMING_SNAKE_CASE_				:  Any           =     locals()
      del input_variables["axis"]
      if not all(isinstance(a					,   (float, int)  ) for val in input_variables.values()  ):
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     (
                'Input values except axis must either be float or int: '
                f"{list(input_variables.values()  )}"
            )
            raise TypeError(a  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     (angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
      if axis == "z":
            SCREAMING_SNAKE_CASE_				:  List[str]           =     x * math.cos(a  ) - y * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     y * math.cos(a  ) + x * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     z
      elif axis == "x":
            SCREAMING_SNAKE_CASE_				:  Any           =     y * math.cos(a  ) - z * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     z * math.cos(a  ) + y * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  str           =     x
      elif axis == "y":
            SCREAMING_SNAKE_CASE_				:  str           =     x * math.cos(a  ) - z * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  List[str]           =     z * math.cos(a  ) + x * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Any           =     y
      else:
            raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''  )

      return new_x, new_y, new_z


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(F'{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }')
    print(F'{rotate(1.0, 2.0, 3.0, "y", 90.0) = }')






