import copy
import os
from typing import Union

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


__UpperCamelCase		:       Optional[int]									=     logging.get_logger(__name__)

__UpperCamelCase		:       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 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""bridgetower_vision_model"""


						def __init__(       self	:				Optional[int]    ,		UpperCamelCase__	:				List[Any]=768    ,		UpperCamelCase__	:				List[Any]=12    ,		UpperCamelCase__	:				List[str]=3    ,		UpperCamelCase__	:				str=16    ,		UpperCamelCase__	:				Any=288    ,		UpperCamelCase__	:				Optional[int]=1    ,		UpperCamelCase__	:				Optional[Any]=1E-05    ,		UpperCamelCase__	:				Union[str, Any]=False    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				str=False    ,		**UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





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



						@classmethod
						def        __A       (       cls	:				List[str]    ,		UpperCamelCase__	:				Union[str, os.PathLike]    ,		**UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							cls.get_config_dict(UpperCamelCase__    ,		**UpperCamelCase__						)

												if config_dict.get('''model_type'''						) == "bridgetower":
																		SCREAMING_SNAKE_CASE							:    Union[str, 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(UpperCamelCase__    ,		**UpperCamelCase__						)






class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""bridgetower_text_model"""


						def __init__(       self	:				str    ,		UpperCamelCase__	:				Optional[int]=5_0265    ,		UpperCamelCase__	:				Any=768    ,		UpperCamelCase__	:				List[Any]=12    ,		UpperCamelCase__	:				Dict=12    ,		UpperCamelCase__	:				Union[str, Any]=1    ,		UpperCamelCase__	:				List[Any]=3072    ,		UpperCamelCase__	:				Dict="gelu"    ,		UpperCamelCase__	:				List[Any]=0.1    ,		UpperCamelCase__	:				List[str]=0.1    ,		UpperCamelCase__	:				List[Any]=514    ,		UpperCamelCase__	:				List[Any]=1    ,		UpperCamelCase__	:				Union[str, Any]=1E-05    ,		UpperCamelCase__	:				Optional[Any]=1    ,		UpperCamelCase__	:				Union[str, Any]=0    ,		UpperCamelCase__	:				Union[str, Any]=2    ,		UpperCamelCase__	:				str="absolute"    ,		UpperCamelCase__	:				List[Any]=True    ,		**UpperCamelCase__	:				str    ,		):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__						)

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



						@classmethod
						def        __A       (       cls	:				Dict    ,		UpperCamelCase__	:				Union[str, os.PathLike]    ,		**UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							cls.get_config_dict(UpperCamelCase__    ,		**UpperCamelCase__						)

												if config_dict.get('''model_type'''						) == "bridgetower":
																		SCREAMING_SNAKE_CASE							:    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(UpperCamelCase__    ,		**UpperCamelCase__						)






class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""bridgetower"""


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				int=True    ,		UpperCamelCase__	:				Dict="gelu"    ,		UpperCamelCase__	:				Tuple=768    ,		UpperCamelCase__	:				str=1    ,		UpperCamelCase__	:				Dict=1E-05    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				Optional[Any]="add"    ,		UpperCamelCase__	:				Optional[int]=12    ,		UpperCamelCase__	:				Tuple=6    ,		UpperCamelCase__	:				Optional[int]=False    ,		UpperCamelCase__	:				Tuple=False    ,		UpperCamelCase__	:				Any=None    ,		UpperCamelCase__	:				Any=None    ,		**UpperCamelCase__	:				Optional[Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							kwargs.pop('''text_config_dict'''    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							kwargs.pop('''vision_config_dict'''    ,		UpperCamelCase__						)

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

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

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

												SCREAMING_SNAKE_CASE							:    Any							=							BridgeTowerTextConfig(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    str							=							BridgeTowerVisionConfig(**UpperCamelCase__						)


						@classmethod
						def        __A       (       cls	:				Optional[int]    ,		UpperCamelCase__	:				BridgeTowerTextConfig    ,		UpperCamelCase__	:				BridgeTowerVisionConfig    ,		**UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





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



						def        __A       (       self	:				int						):





												'''simple docstring'''





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






import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





