
"""simple docstring"""




from __future__ import annotations

import math

A__ :       Optional[Any]				= '2020.9.26'
A__ :       int				= 'xcodz-dot, cclaus, dhruvmanila'




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if not all(isinstance(_UpperCamelCase			,					(float, int)    ) for val in locals().values()    ):
    _lowercase:      List[str]   			=  f'''Input values must either be float or int: {list(locals().values()    )}'''
    raise TypeError(_UpperCamelCase    )
  _lowercase:      int   			=  ((x * distance) / (z + distance)) * scale
  _lowercase:      Dict   			=  ((y * distance) / (z + distance)) * scale
  return projected_x, projected_y




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if not isinstance(_UpperCamelCase			,					_UpperCamelCase    ):
    raise TypeError('''Axis must be a str'''    )
  _lowercase:      Optional[Any]   			=  locals()
  del input_variables["axis"]
  if not all(isinstance(_UpperCamelCase			,					(float, int)    ) for val in input_variables.values()    ):
    _lowercase:      List[str]   			=  (
        '''Input values except axis must either be float or int: '''
        f'''{list(input_variables.values()    )}'''
    )
    raise TypeError(_UpperCamelCase    )
  _lowercase:      str   			=  (angle % 360) / 450 * 180 / math.pi
  if axis == "z":
    _lowercase:      Optional[Any]   			=  x * math.cos(_UpperCamelCase    ) - y * math.sin(_UpperCamelCase    )
    _lowercase:      Tuple   			=  y * math.cos(_UpperCamelCase    ) + x * math.sin(_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  z
  elif axis == "x":
    _lowercase:      Any   			=  y * math.cos(_UpperCamelCase    ) - z * math.sin(_UpperCamelCase    )
    _lowercase:      Optional[int]   			=  z * math.cos(_UpperCamelCase    ) + y * math.sin(_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  x
  elif axis == "y":
    _lowercase:      Tuple   			=  x * math.cos(_UpperCamelCase    ) - z * math.sin(_UpperCamelCase    )
    _lowercase:      int   			=  z * math.cos(_UpperCamelCase    ) + x * math.sin(_UpperCamelCase    )
    _lowercase:      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) = }""")


"""simple docstring"""




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__ :       List[Any]				= logging.get_logger(__name__)

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


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''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_			,					)  ->						Tuple:





         """simple docstring"""




         super().__init__(**A_							)

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      str   			=  cls.get_config_dict(A_			,					**A_							)

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Dict   			=  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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_qformer'''
       def __init__(  self			,					A_=3_0522			,					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_			,					)  ->						Optional[int]:





         """simple docstring"""




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

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      Tuple   			=  cls.get_config_dict(A_			,					**A_							)

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Tuple   			=  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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip'''
       UpperCamelCase_ =		True
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_=32			,					**A_							)  ->						str:





         """simple docstring"""




         super().__init__(**A_							)

         if vision_config is None:
           _lowercase:      str   			=  {}
           logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''							)

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

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

         _lowercase:      int   			=  InstructBlipVisionConfig(**A_							)
         _lowercase:      Optional[Any]   			=  InstructBlipQFormerConfig(**A_							)
         _lowercase:      Optional[Any]   			=  text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         _lowercase:      Optional[Any]   			=  CONFIG_MAPPING[text_model_type](**A_							)

         _lowercase:      Tuple   			=  self.text_config.tie_word_embeddings
         _lowercase:      Dict   			=  self.text_config.is_encoder_decoder

         _lowercase:      List[Any]   			=  num_query_tokens
         _lowercase:      Any   			=  self.vision_config.hidden_size
         _lowercase:      List[Any]   			=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         _lowercase:      Union[str, Any]   			=  1.0
         _lowercase:      Dict   			=  0.02
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					A_			,					**A_			,					)  ->						str:





         """simple docstring"""




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



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




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