

import copy
import os
from typing import Union

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


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""git_vision_model"""




  def __init__(       self							,     lowerCAmelCase=768							,     lowerCAmelCase=3_072							,     lowerCAmelCase=12							,     lowerCAmelCase=12							,     lowerCAmelCase=3							,     lowerCAmelCase=224							,     lowerCAmelCase=16							,     lowerCAmelCase="quick_gelu"							,     lowerCAmelCase=1e-5							,     lowerCAmelCase=0.0							,     lowerCAmelCase=0.02							,     **lowerCAmelCase							,     )						->				List[str]:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase      )

        _lowercase			 =hidden_size
        _lowercase			 =intermediate_size
        _lowercase			 =num_hidden_layers
        _lowercase			 =num_attention_heads
        _lowercase			 =num_channels
        _lowercase			 =patch_size
        _lowercase			 =image_size
        _lowercase			 =initializer_range
        _lowercase			 =attention_dropout
        _lowercase			 =layer_norm_eps
        _lowercase			 =hidden_act




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				"PretrainedConfig":
        '''simple docstring'''






        cls._set_token_in_kwargs(lowerCAmelCase      )

        _lowercase , _lowercase			 =cls.get_config_dict(lowerCAmelCase							,     **lowerCAmelCase      )

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get('model_type'      ) == "git":
              _lowercase			 =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 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""git"""




  def __init__(       self							,     lowerCAmelCase=None							,     lowerCAmelCase=30_522							,     lowerCAmelCase=768							,     lowerCAmelCase=6							,     lowerCAmelCase=12							,     lowerCAmelCase=3_072							,     lowerCAmelCase="gelu"							,     lowerCAmelCase=0.1							,     lowerCAmelCase=0.1							,     lowerCAmelCase=1_024							,     lowerCAmelCase=0.02							,     lowerCAmelCase=1e-12							,     lowerCAmelCase=0							,     lowerCAmelCase="absolute"							,     lowerCAmelCase=True							,     lowerCAmelCase=False							,     lowerCAmelCase=101							,     lowerCAmelCase=102							,     lowerCAmelCase=None							,     **lowerCAmelCase							,     )						->				Union[str, Any]:
        '''simple docstring'''






        super().__init__(bos_token_id=lowerCAmelCase							,     eos_token_id=lowerCAmelCase							,     pad_token_id=lowerCAmelCase							,     **lowerCAmelCase      )

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

        _lowercase			 =GitVisionConfig(**lowerCAmelCase      )
        _lowercase			 =vocab_size
        _lowercase			 =hidden_size
        _lowercase			 =num_hidden_layers
        _lowercase			 =num_attention_heads
        _lowercase			 =hidden_act
        _lowercase			 =intermediate_size
        _lowercase			 =hidden_dropout_prob
        _lowercase			 =attention_probs_dropout_prob
        _lowercase			 =max_position_embeddings
        _lowercase			 =initializer_range
        _lowercase			 =layer_norm_eps
        _lowercase			 =position_embedding_type
        _lowercase			 =use_cache
        _lowercase			 =tie_word_embeddings
        _lowercase			 =num_image_with_embedding

        _lowercase			 =bos_token_id
        _lowercase			 =eos_token_id




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =copy.deepcopy(self.__dict__      )
        _lowercase			 =self.vision_config.to_dict()
        _lowercase			 =self.__class__.model_type
        return output





from math import pow, sqrt
def   a   (    *A__			:     float  )					->   bool:





      """simple docstring"""



      _lowercase			 =len(A__  ) > 0 and all(value > 0.0 for value in values  )
      return result
def   a   (    A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__  )
          else ValueError('Input Error: Molar mass values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,     2  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )







def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,     2  ) / molar_mass   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )


