"""simple docstring"""
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 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  """bridgetower_vision_model"""

				def __init__(			self						,      lowerCAmelCase=7_68						,      lowerCAmelCase=12						,      lowerCAmelCase=3						,      lowerCAmelCase=16						,      lowerCAmelCase=2_88						,      lowerCAmelCase=1						,      lowerCAmelCase=1E-05						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase=False						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_channels
										snake_case        =     patch_size
										snake_case        =     image_size
										snake_case        =     initializer_factor
										snake_case        =     layer_norm_eps
										snake_case        =     stop_gradient
										snake_case        =     share_layernorm
										snake_case        =     remove_last_layer

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										if config_dict.get('model_type'      ) == "bridgetower":
																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 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  """bridgetower_text_model"""

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






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

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

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										if config_dict.get('model_type'      ) == "bridgetower":
																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 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      str     					=  """bridgetower"""

				def __init__(			self						,      lowerCAmelCase=True						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=7_68						,      lowerCAmelCase=1						,      lowerCAmelCase=1E-05						,      lowerCAmelCase=False						,      lowerCAmelCase="add"						,      lowerCAmelCase=12						,      lowerCAmelCase=6						,      lowerCAmelCase=False						,      lowerCAmelCase=False						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     kwargs.pop('text_config_dict'						,      lowerCAmelCase      )
										snake_case        =     kwargs.pop('vision_config_dict'						,      lowerCAmelCase      )

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

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

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

										snake_case        =     BridgeTowerTextConfig(**lowerCAmelCase      )
										snake_case        =     BridgeTowerVisionConfig(**lowerCAmelCase      )

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     copy.deepcopy(self.__dict__      )
										snake_case        =     self.text_config.to_dict()
										snake_case        =     self.vision_config.to_dict()
										snake_case        =     self.__class__.model_type
										return output



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecAudioForAudioFrameClassification",
					    "Data2VecAudioForCTC",
					    "Data2VecAudioForSequenceClassification",
					    "Data2VecAudioForXVector",
					    "Data2VecAudioModel",
					    "Data2VecAudioPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecTextForCausalLM",
					    "Data2VecTextForMaskedLM",
					    "Data2VecTextForMultipleChoice",
					    "Data2VecTextForQuestionAnswering",
					    "Data2VecTextForSequenceClassification",
					    "Data2VecTextForTokenClassification",
					    "Data2VecTextModel",
					    "Data2VecTextPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecVisionForImageClassification",
					    "Data2VecVisionForMaskedImageModeling",
					    "Data2VecVisionForSemanticSegmentation",
					    "Data2VecVisionModel",
					    "Data2VecVisionPreTrainedModel",
					]

if is_tf_available():
					SCREAMING_SNAKE_CASE__   =					[
					    "TFData2VecVisionForImageClassification",
					    "TFData2VecVisionForSemanticSegmentation",
					    "TFData2VecVisionModel",
					    "TFData2VecVisionPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


