
"""simple docstring"""





import copy
import os
from typing import Union

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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  int						   =		'''bridgetower_vision_model'''




		def __init__(     self							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=3							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2_8_8							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=False							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_channels
				__SCREAMING_SNAKE_CASE            = patch_size
				__SCREAMING_SNAKE_CASE            = image_size
				__SCREAMING_SNAKE_CASE            = initializer_factor
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = stop_gradient
				__SCREAMING_SNAKE_CASE            = share_layernorm
				__SCREAMING_SNAKE_CASE            = remove_last_layer




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				if config_dict.get("""model_type""") == "bridgetower":
						__SCREAMING_SNAKE_CASE            = 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(lowerCAmelCase__							,  **lowerCAmelCase__)



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  int						   =		'''bridgetower_text_model'''




		def __init__(     self							,  lowerCAmelCase__=5_0_2_6_5							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=3_0_7_2							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_4							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=2							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=True							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = initializer_factor
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = position_embedding_type
				__SCREAMING_SNAKE_CASE            = use_cache
				__SCREAMING_SNAKE_CASE            = pad_token_id
				__SCREAMING_SNAKE_CASE            = bos_token_id
				__SCREAMING_SNAKE_CASE            = eos_token_id




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				if config_dict.get("""model_type""") == "bridgetower":
						__SCREAMING_SNAKE_CASE            = 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(lowerCAmelCase__							,  **lowerCAmelCase__)



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[str]						   =		'''bridgetower'''




		def __init__(     self							,  lowerCAmelCase__=True							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=False							,  lowerCAmelCase__="add"							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=6							,  lowerCAmelCase__=False							,  lowerCAmelCase__=False							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__							,  ):
				# TODO: remove this once the Hub files are updated.
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""text_config_dict"""							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""vision_config_dict"""							,  lowerCAmelCase__)

				super().__init__(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = share_cross_modal_transformer_layers
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = initializer_factor
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = share_link_tower_layers
				__SCREAMING_SNAKE_CASE            = link_tower_type
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = tie_word_embeddings
				__SCREAMING_SNAKE_CASE            = init_layernorm_from_vision_encoder

				if text_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.""")

				if vision_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.""")

				__SCREAMING_SNAKE_CASE            = BridgeTowerTextConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = BridgeTowerVisionConfig(**lowerCAmelCase__)




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				return cls(text_config=text_config.to_dict()							,  vision_config=vision_config.to_dict()							,  **lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = copy.deepcopy(self.__dict__)
				__SCREAMING_SNAKE_CASE            = self.text_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.vision_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.__class__.model_type
				return output








"""simple docstring"""







from __future__ import annotations




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None   ):




					'''simple docstring'''


					if start is None:
										__SCREAMING_SNAKE_CASE						   =				0

					if end is None:
										__SCREAMING_SNAKE_CASE						   =				len(lowerCAmelCase_   ) - 1

					if start >= end:
										return

					__SCREAMING_SNAKE_CASE						   =				(start + end) // 2

					slowsort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
					slowsort(lowerCAmelCase_  ,						mid + 1  ,						lowerCAmelCase_   )

					if sequence[end] < sequence[mid]:
										__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				sequence[mid], sequence[end]

					slowsort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						end - 1   )


if __name__ == "__main__":
		from doctest import testmod

		testmod()
