




"""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       						=	logging.get_logger(__name__)

__A       						=	{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class 	lowerCamelCase__			(     UpperCAmelCase__	):
			a__       :					List[Any]	    =   """instructblip_vision_model"""


			def __init__(   self ,       SCREAMING_SNAKE_CASE=1_408 ,       SCREAMING_SNAKE_CASE=6_144 ,       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__							)

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



			@classmethod
			def 							lowerCamelCase_ (   cls ,       SCREAMING_SNAKE_CASE ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__							)

					snake_case	, snake_case						:  List[str]    	= 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":
							snake_case						:  str    	= 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 	lowerCamelCase__			(     UpperCAmelCase__	):
			a__       :					Tuple	    =   """instructblip_qformer"""


			def __init__(   self ,       SCREAMING_SNAKE_CASE=30_522 ,       SCREAMING_SNAKE_CASE=768 ,       SCREAMING_SNAKE_CASE=12 ,       SCREAMING_SNAKE_CASE=12 ,       SCREAMING_SNAKE_CASE=3_072 ,       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=1_408 ,       **SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







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

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



			@classmethod
			def 							lowerCamelCase_ (   cls ,       SCREAMING_SNAKE_CASE ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__							)

					snake_case	, snake_case						:  Optional[Any]    	= 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":
							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 	lowerCamelCase__			(     UpperCAmelCase__	):
			a__       :					Tuple	    =   """instructblip"""
			a__       :					Dict	    =   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:
							snake_case						:  Tuple    	= {}
							logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."							)

					if qformer_config is None:
							snake_case						:  Union[str, Any]    	= {}
							logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."							)

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

					snake_case						:  Any    	= InstructBlipVisionConfig(**SCREAMING_SNAKE_CASE__							)
					snake_case						:  Tuple    	= InstructBlipQFormerConfig(**SCREAMING_SNAKE_CASE__							)
					snake_case						:  Optional[int]    	= text_config["model_type"] if "model_type" in text_config else "opt"
					snake_case						:  str    	= CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE__							)

					snake_case						:  Tuple    	= self.text_config.tie_word_embeddings
					snake_case						:  List[str]    	= self.text_config.is_encoder_decoder

					snake_case						:  str    	= num_query_tokens
					snake_case						:  Tuple    	= self.vision_config.hidden_size
					snake_case						:  int    	= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
					snake_case						:  Optional[Any]    	= 1.0
					snake_case						:  List[str]    	= 0.02


			@classmethod
			def 							lowerCamelCase_ (   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 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  int    	= copy.deepcopy(self.__dict__							)
					snake_case						:  Dict    	= self.vision_config.to_dict()
					snake_case						:  List[Any]    	= self.qformer_config.to_dict()
					snake_case						:  Optional[Any]    	= self.text_config.to_dict()
					snake_case						:  str    	= 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)
_A			:      List[Any]					      =				299792458

# Symbols
_A      ,     _A      ,     _A      ,     _A			:      Union[str, Any]					      =				symbols('''ct x y z''')







def      UpperCamelCase_						(					snake_case_       :       float      )					->     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      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       float      )					->     np.ndarray:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       float						,		snake_case_       :       np.ndarray | None = None      )					->     np.ndarray:






 '''simple docstring'''

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

 return transformation_matrix(snake_case_      ) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 _A			:      str					      =				transform(29979245)
 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
 _A			:      int					      =				{ct: c, x: 1, y: 1, z: 1}
 _A			:      Any					      =				[four_vector[i].subs(sub_dict) for i in range(4)]

 print(f'\n{numerical_vector}')






