


'''simple docstring'''
import copy
import os
from typing import Union

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


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						="bridgetower_vision_model"






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






						@classmethod
						def 				_UpperCAmelCase       (  cls							:      Optional[Any] ,  lowerCAmelCase							:      Union[str, os.PathLike] ,  **lowerCAmelCase							:      List[Any]	):
													A_    ,					A_			    =		cls.get_config_dict(lowerCAmelCase ,  **lowerCAmelCase	)

													if config_dict.get("model_type"	) == "bridgetower":
																				A_			    =		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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[Any]   						="bridgetower_text_model"






						def __init__(  self							:      Tuple ,  lowerCAmelCase							:      Tuple=5_02_65 ,  lowerCAmelCase							:      List[str]=7_68 ,  lowerCAmelCase							:      int=12 ,  lowerCAmelCase							:      List[Any]=12 ,  lowerCAmelCase							:      Optional[Any]=1 ,  lowerCAmelCase							:      Optional[Any]=30_72 ,  lowerCAmelCase							:      Dict="gelu" ,  lowerCAmelCase							:      Union[str, Any]=0.1 ,  lowerCAmelCase							:      Dict=0.1 ,  lowerCAmelCase							:      Tuple=5_14 ,  lowerCAmelCase							:      str=1 ,  lowerCAmelCase							:      Dict=1e-05 ,  lowerCAmelCase							:      List[Any]=1 ,  lowerCAmelCase							:      List[str]=0 ,  lowerCAmelCase							:      Optional[Any]=2 ,  lowerCAmelCase							:      Any="absolute" ,  lowerCAmelCase							:      List[str]=True ,  **lowerCAmelCase							:      Any ,  ):
													super().__init__(**lowerCAmelCase	)

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






						@classmethod
						def 				_UpperCAmelCase       (  cls							:      int ,  lowerCAmelCase							:      Union[str, os.PathLike] ,  **lowerCAmelCase							:      List[Any]	):
													A_    ,					A_			    =		cls.get_config_dict(lowerCAmelCase ,  **lowerCAmelCase	)

													if config_dict.get("model_type"	) == "bridgetower":
																				A_			    =		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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       str   						="bridgetower"






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      List[Any]=True ,  lowerCAmelCase							:      Union[str, Any]="gelu" ,  lowerCAmelCase							:      str=7_68 ,  lowerCAmelCase							:      str=1 ,  lowerCAmelCase							:      str=1e-05 ,  lowerCAmelCase							:      str=False ,  lowerCAmelCase							:      Dict="add" ,  lowerCAmelCase							:      Optional[Any]=12 ,  lowerCAmelCase							:      Union[str, Any]=6 ,  lowerCAmelCase							:      str=False ,  lowerCAmelCase							:      Optional[int]=False ,  lowerCAmelCase							:      List[str]=None ,  lowerCAmelCase							:      List[Any]=None ,  **lowerCAmelCase							:      Dict ,  ):
													# TODO: remove this once the Hub files are updated.
													A_			    =		kwargs.pop("text_config_dict" ,  lowerCAmelCase	)
													A_			    =		kwargs.pop("vision_config_dict" ,  lowerCAmelCase	)

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

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

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

													A_			    =		BridgeTowerTextConfig(**lowerCAmelCase	)
													A_			    =		BridgeTowerVisionConfig(**lowerCAmelCase	)






						@classmethod
						def 				_UpperCAmelCase       (  cls							:      List[Any] ,  lowerCAmelCase							:      BridgeTowerTextConfig ,  lowerCAmelCase							:      BridgeTowerVisionConfig ,  **lowerCAmelCase							:      List[str]	):
													return cls(text_config=text_config.to_dict() ,  vision_config=vision_config.to_dict() ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		copy.deepcopy(self.__dict__	)
													A_			    =		self.text_config.to_dict()
													A_			    =		self.vision_config.to_dict()
													A_			    =		self.__class__.model_type
													return output









'''simple docstring'''
import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline







@pytest.mark.integration
def       a_				(	):
							with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
														with pytest.raises(UpperCamelCase_ ):
																					requests.request("GET"						,  "https://huggingface.co" )
														with pytest.raises(requests.exceptions.ConnectTimeout ):
																					requests.request("GET"						,  "https://huggingface.co"						,  timeout=1.0 )







@pytest.mark.integration
def       a_				(	):
							with offline(OfflineSimulationMode.CONNECTION_FAILS ):
														with pytest.raises(requests.exceptions.ConnectionError ):
																					requests.request("GET"						,  "https://huggingface.co" )







def       a_				(	):
							with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
														with pytest.raises(UpperCamelCase_ ):
																					http_head("https://huggingface.co" )





