

import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    '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  (	UpperCAmelCase				):
						_lowercase    						=			"bridgetower_vision_model"


						def __init__(       self						,					A_=768						,					A_=12						,					A_=3						,					A_=16						,					A_=288						,					A_=1						,					A_=1e-05						,					A_=False						,					A_=True						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Any 		=    hidden_size
										_UpperCAmelCase       :		Optional[int] 		=    num_hidden_layers
										_UpperCAmelCase       :		Union[str, Any] 		=    num_channels
										_UpperCAmelCase       :		int 		=    patch_size
										_UpperCAmelCase       :		Any 		=    image_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_factor
										_UpperCAmelCase       :		int 		=    layer_norm_eps
										_UpperCAmelCase       :		Optional[int] 		=    stop_gradient
										_UpperCAmelCase       :		Tuple 		=    share_layernorm
										_UpperCAmelCase       :		str 		=    remove_last_layer





						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.get_config_dict(A_						,					**A_	)

										if config_dict.get("model_type"	) == "bridgetower":
														_UpperCAmelCase       :		List[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 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bridgetower_text_model"


						def __init__(       self						,					A_=50265						,					A_=768						,					A_=12						,					A_=12						,					A_=1						,					A_=3072						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=514						,					A_=1						,					A_=1e-05						,					A_=1						,					A_=0						,					A_=2						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

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





						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    cls.get_config_dict(A_						,					**A_	)

										if config_dict.get("model_type"	) == "bridgetower":
														_UpperCAmelCase       :		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(A_						,					**A_	)







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bridgetower"


						def __init__(       self						,					A_=True						,					A_="gelu"						,					A_=768						,					A_=1						,					A_=1e-05						,					A_=False						,					A_="add"						,					A_=12						,					A_=6						,					A_=False						,					A_=False						,					A_=None						,					A_=None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    kwargs.pop("text_config_dict"						,					A_	)
										_UpperCAmelCase       :		List[str] 		=    kwargs.pop("vision_config_dict"						,					A_	)

										super().__init__(**A_	)
										_UpperCAmelCase       :		Any 		=    share_cross_modal_transformer_layers
										_UpperCAmelCase       :		int 		=    hidden_act
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		str 		=    initializer_factor
										_UpperCAmelCase       :		Any 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    share_link_tower_layers
										_UpperCAmelCase       :		str 		=    link_tower_type
										_UpperCAmelCase       :		List[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Dict 		=    num_hidden_layers
										_UpperCAmelCase       :		List[str] 		=    tie_word_embeddings
										_UpperCAmelCase       :		Any 		=    init_layernorm_from_vision_encoder

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

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

										_UpperCAmelCase       :		List[Any] 		=    BridgeTowerTextConfig(**A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    BridgeTowerVisionConfig(**A_	)


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_						,					A_						,					**A_	):







										'''simple docstring'''


										return cls(text_config=text_config.to_dict()						,					vision_config=vision_config.to_dict()						,					**A_	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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





from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


