


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









'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_      :  List[Any]				     =  2_9979_2458

# Symbols
lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_      :  Tuple				     =  symbols('ct x y z')



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     if velocity > c:
          raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""				)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError("""Speed must be greater than or equal to 1!"""				)

     return velocity / c



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return 1 / sqrt(1 - beta(UpperCamelCase__				) ** 2				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return np.array(
         [
             [gamma(UpperCamelCase__				), -gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), 0, 0],
             [-gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), gamma(UpperCamelCase__				), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					np.ndarray | None = None				):




     """simple docstring"""


     if event is None:
          a_     :							str     						=  np.array([ct, x, y, z]				)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(UpperCamelCase__				) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 lowerCAmelCase_      :  Union[str, Any]				     =  transform(2997_9245)
 print('Example of four vector: ')
 print(f"ct' = {four_vector[0]}")
 print(f"x' = {four_vector[1]}")
 print(f"y' = {four_vector[2]}")
 print(f"z' = {four_vector[3]}")

 # Substitute symbols with numerical values
 lowerCAmelCase_      :  Tuple				     =  {ct: c, x: 1, y: 1, z: 1}
 lowerCAmelCase_      :  Optional[int]				     =  [four_vector[i].subs(sub_dict) for i in range(4)]

 print(f"\n{numerical_vector}")
