





"""simple docstring"""


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
  pass

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


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}



class      __SCREAMING_SNAKE_CASE    (						UpperCamelCase_   ):
  SCREAMING_SNAKE_CASE__							:Any	     =					"align_text_model"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=3_0522					,   __a   :       Optional[int]=768					,   __a   :       Dict=12					,   __a   :       Any=12					,   __a   :       List[str]=3072					,   __a   :       Tuple="gelu"					,   __a   :       List[Any]=0.1					,   __a   :       List[str]=0.1					,   __a   :       Optional[Any]=512					,   __a   :       Optional[int]=2					,   __a   :       int=0.02					,   __a   :       Dict=1e-1_2					,   __a   :       Optional[int]=0					,   __a   :       List[Any]="absolute"					,   __a   :       str=True					,   **__a   :       Optional[Any]					,   )		->				Optional[Any]:
        super().__init__(**__A	)

        _UpperCamelCase      :	Any						=       vocab_size
        _UpperCamelCase      :	str						=       hidden_size
        _UpperCamelCase      :	str						=       num_hidden_layers
        _UpperCamelCase      :	List[str]						=       num_attention_heads
        _UpperCamelCase      :	Optional[int]						=       hidden_act
        _UpperCamelCase      :	Tuple						=       intermediate_size
        _UpperCamelCase      :	Optional[Any]						=       hidden_dropout_prob
        _UpperCamelCase      :	Dict						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Optional[Any]						=       max_position_embeddings
        _UpperCamelCase      :	List[str]						=       type_vocab_size
        _UpperCamelCase      :	Union[str, Any]						=       initializer_range
        _UpperCamelCase      :	Dict						=       layer_norm_eps
        _UpperCamelCase      :	Optional[int]						=       position_embedding_type
        _UpperCamelCase      :	Tuple						=       use_cache
        _UpperCamelCase      :	List[str]						=       pad_token_id


  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       Tuple					,   __a   :       Union[str, os.PathLike]					,   **__a   :       Optional[int]	)		->				List[Any]:
        cls._set_token_in_kwargs(__A	)

        _UpperCamelCase      :	Dict						=       cls.get_config_dict(__A					,   **__A	)

        # get the text config dict if we are loading from AlignConfig
        if config_dict.get("model_type"	) == "align":
              _UpperCamelCase      :	Any						=       config_dict["text_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      __SCREAMING_SNAKE_CASE    (						UpperCamelCase_   ):
  SCREAMING_SNAKE_CASE__							:int	     =					"align_vision_model"

  def __init__(		self   :       Union[str, Any]					,   __a   :       int = 3					,   __a   :       int = 600					,   __a   :       float = 2.0					,   __a   :       float = 3.1					,   __a   :       int = 8					,   __a   :       List[int] = [3, 3, 5, 3, 5, 5, 3]					,   __a   :       List[int] = [32, 16, 24, 40, 80, 112, 192]					,   __a   :       List[int] = [16, 24, 40, 80, 112, 192, 320]					,   __a   :       List[int] = []					,   __a   :       List[int] = [1, 2, 2, 2, 1, 2, 1]					,   __a   :       List[int] = [1, 2, 2, 3, 3, 4, 1]					,   __a   :       List[int] = [1, 6, 6, 6, 6, 6, 6]					,   __a   :       float = 0.25					,   __a   :       str = "swish"					,   __a   :       int = 2560					,   __a   :       str = "mean"					,   __a   :       float = 0.02					,   __a   :       float = 0.0_01					,   __a   :       float = 0.99					,   __a   :       float = 0.2					,   **__a   :       Optional[int]					,   )		->				Union[str, Any]:
        super().__init__(**__A	)

        _UpperCamelCase      :	Optional[Any]						=       num_channels
        _UpperCamelCase      :	str						=       image_size
        _UpperCamelCase      :	Optional[Any]						=       width_coefficient
        _UpperCamelCase      :	int						=       depth_coefficient
        _UpperCamelCase      :	List[str]						=       depth_divisor
        _UpperCamelCase      :	int						=       kernel_sizes
        _UpperCamelCase      :	str						=       in_channels
        _UpperCamelCase      :	Optional[Any]						=       out_channels
        _UpperCamelCase      :	Union[str, Any]						=       depthwise_padding
        _UpperCamelCase      :	Dict						=       strides
        _UpperCamelCase      :	Optional[int]						=       num_block_repeats
        _UpperCamelCase      :	Any						=       expand_ratios
        _UpperCamelCase      :	List[Any]						=       squeeze_expansion_ratio
        _UpperCamelCase      :	List[Any]						=       hidden_act
        _UpperCamelCase      :	Dict						=       hidden_dim
        _UpperCamelCase      :	str						=       pooling_type
        _UpperCamelCase      :	Any						=       initializer_range
        _UpperCamelCase      :	List[str]						=       batch_norm_eps
        _UpperCamelCase      :	Optional[Any]						=       batch_norm_momentum
        _UpperCamelCase      :	Any						=       drop_connect_rate
        _UpperCamelCase      :	Optional[int]						=       sum(__A	) * 4


  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       List[Any]					,   __a   :       Union[str, os.PathLike]					,   **__a   :       Dict	)		->				int:
        cls._set_token_in_kwargs(__A	)

        _UpperCamelCase      :	Any						=       cls.get_config_dict(__A					,   **__A	)

        # get the vision config dict if we are loading from AlignConfig
        if config_dict.get("model_type"	) == "align":
              _UpperCamelCase      :	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(__A					,   **__A	)





class      __SCREAMING_SNAKE_CASE    (						UpperCamelCase_   ):
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					"align"
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					True

  def __init__(		self   :       int					,   __a   :       List[str]=None					,   __a   :       Any=None					,   __a   :       Tuple=640					,   __a   :       int=1.0					,   __a   :       Tuple=0.02					,   **__a   :       Tuple					,   )		->				str:
        super().__init__(**__A	)

        if text_config is None:
              _UpperCamelCase      :	List[Any]						=       {}
              logger.info("text_config is None. Initializing the AlignTextConfig with default values."	)

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

        _UpperCamelCase      :	Any						=       AlignTextConfig(**__A	)
        _UpperCamelCase      :	List[str]						=       AlignVisionConfig(**__A	)

        _UpperCamelCase      :	List[str]						=       projection_dim
        _UpperCamelCase      :	Union[str, Any]						=       temperature_init_value
        _UpperCamelCase      :	int						=       initializer_range

  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       Optional[Any]					,   __a   :       AlignTextConfig					,   __a   :       AlignVisionConfig					,   **__a   :       Optional[int]	)		->				List[str]:
        return cls(text_config=text_config.to_dict()					,   vision_config=vision_config.to_dict()					,   **__A	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				str:
        _UpperCamelCase      :	Dict						=       copy.deepcopy(self.__dict__	)
        _UpperCamelCase      :	List[Any]						=       self.text_config.to_dict()
        _UpperCamelCase      :	Dict						=       self.vision_config.to_dict()
        _UpperCamelCase      :	Dict						=       self.__class__.model_type
        return output







"""simple docstring"""


lowerCamelCase__				  =   "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase__				  =   [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase__				  =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
