
"""simple docstring"""







import copy
import os
from typing import Union

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


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    """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 			A__	(     _lowerCamelCase):
		A_						:	List[str]           =     'bridgetower_vision_model'

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=7_68	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=16	,		_SCREAMING_SNAKE_CASE=2_88	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=1E-05	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=False	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				hidden_size
			__lowerCAmelCase      :						Tuple						      =				num_hidden_layers
			__lowerCAmelCase      :						Optional[int]						      =				num_channels
			__lowerCAmelCase      :						Union[str, Any]						      =				patch_size
			__lowerCAmelCase      :						Union[str, Any]						      =				image_size
			__lowerCAmelCase      :						Any						      =				initializer_factor
			__lowerCAmelCase      :						Any						      =				layer_norm_eps
			__lowerCAmelCase      :						List[Any]						      =				stop_gradient
			__lowerCAmelCase      :						Any						      =				share_layernorm
			__lowerCAmelCase      :						Dict						      =				remove_last_layer







		@classmethod
		def 							__lowerCamelCase							(				cls	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				cls.get_config_dict(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

			if config_dict.get('model_type'			) == "bridgetower":
				__lowerCAmelCase      :						List[str]						      =				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(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)




class 			A__	(     _lowerCamelCase):
		A_						:	int           =     'bridgetower_text_model'

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=5_02_65	,		_SCREAMING_SNAKE_CASE=7_68	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=30_72	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=5_14	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=1E-05	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=0	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE="absolute"	,		_SCREAMING_SNAKE_CASE=True	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(**_SCREAMING_SNAKE_CASE			)

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







		@classmethod
		def 							__lowerCamelCase							(				cls	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				cls.get_config_dict(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

			if config_dict.get('model_type'			) == "bridgetower":
				__lowerCAmelCase      :						Optional[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(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)




class 			A__	(     _lowerCamelCase):
		A_						:	Any           =     'bridgetower'

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=7_68	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=1E-05	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE="add"	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=6	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE	,		):
			# TODO: remove this once the Hub files are updated.
			__lowerCAmelCase      :						Optional[Any]						      =				kwargs.pop('text_config_dict'	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				kwargs.pop('vision_config_dict'	,		_SCREAMING_SNAKE_CASE			)

			super().__init__(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				share_cross_modal_transformer_layers
			__lowerCAmelCase      :						Optional[Any]						      =				hidden_act
			__lowerCAmelCase      :						Any						      =				hidden_size
			__lowerCAmelCase      :						Dict						      =				initializer_factor
			__lowerCAmelCase      :						Any						      =				layer_norm_eps
			__lowerCAmelCase      :						Dict						      =				share_link_tower_layers
			__lowerCAmelCase      :						int						      =				link_tower_type
			__lowerCAmelCase      :						Optional[int]						      =				num_attention_heads
			__lowerCAmelCase      :						Tuple						      =				num_hidden_layers
			__lowerCAmelCase      :						Dict						      =				tie_word_embeddings
			__lowerCAmelCase      :						Union[str, Any]						      =				init_layernorm_from_vision_encoder

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

			if vision_config is None:
				__lowerCAmelCase      :						List[str]						      =				{}
				logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'			)

			__lowerCAmelCase      :						Union[str, Any]						      =				BridgeTowerTextConfig(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				BridgeTowerVisionConfig(**_SCREAMING_SNAKE_CASE			)

		@classmethod
		def 							__lowerCamelCase							(				cls	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			return cls(text_config=text_config.to_dict()	,		vision_config=vision_config.to_dict()	,		**_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self.__dict__			)
			__lowerCAmelCase      :						Any						      =				self.text_config.to_dict()
			__lowerCAmelCase      :						Any						      =				self.vision_config.to_dict()
			__lowerCAmelCase      :						List[str]						      =				self.__class__.model_type
			return output

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)