





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 		UpperCamelCase__			(				A__       ):



					'''simple docstring'''

					__snake_case   :     Dict		     =      "bridgetower_vision_model"






					def __init__(							self						:   List[str]						,lowerCamelCase__						:   Optional[int]=768						,lowerCamelCase__						:   int=12						,lowerCamelCase__						:   Optional[int]=3						,lowerCamelCase__						:   str=16						,lowerCamelCase__						:   Optional[int]=288						,lowerCamelCase__						:   List[Any]=1						,lowerCamelCase__						:   List[str]=1e-0_5						,lowerCamelCase__						:   Dict=False						,lowerCamelCase__						:   str=True						,lowerCamelCase__						:   Optional[int]=False						,**lowerCamelCase__						:   str						,)					->		List[str]:




											'''simple docstring'''


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






					@classmethod
					def     SCREAMING_SNAKE_CASE__  (							cls						:   int						,lowerCamelCase__						:   Union[str, os.PathLike]						,**lowerCamelCase__						:   Optional[Any]	)					->		"PretrainedConfig":




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						cls.get_config_dict(__snake_case						,**__snake_case	)

											if config_dict.get("""model_type"""	) == "bridgetower":
																	SCREAMING_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(__snake_case						,**__snake_case	)

class 		UpperCamelCase__			(				A__       ):



					'''simple docstring'''

					__snake_case   :     List[Any]		     =      "bridgetower_text_model"






					def __init__(							self						:   int						,lowerCamelCase__						:   Dict=50265						,lowerCamelCase__						:   List[Any]=768						,lowerCamelCase__						:   Any=12						,lowerCamelCase__						:   Any=12						,lowerCamelCase__						:   str=1						,lowerCamelCase__						:   int=3072						,lowerCamelCase__						:   int="gelu"						,lowerCamelCase__						:   Optional[int]=0.1						,lowerCamelCase__						:   Tuple=0.1						,lowerCamelCase__						:   Any=514						,lowerCamelCase__						:   Tuple=1						,lowerCamelCase__						:   Tuple=1e-0_5						,lowerCamelCase__						:   Union[str, Any]=1						,lowerCamelCase__						:   str=0						,lowerCamelCase__						:   List[str]=2						,lowerCamelCase__						:   Optional[int]="absolute"						,lowerCamelCase__						:   int=True						,**lowerCamelCase__						:   Dict						,)					->		List[Any]:




											'''simple docstring'''


											super().__init__(**__snake_case	)

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






					@classmethod
					def     SCREAMING_SNAKE_CASE__  (							cls						:   int						,lowerCamelCase__						:   Union[str, os.PathLike]						,**lowerCamelCase__						:   Dict	)					->		"PretrainedConfig":




											'''simple docstring'''


											SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						cls.get_config_dict(__snake_case						,**__snake_case	)

											if config_dict.get("""model_type"""	) == "bridgetower":
																	SCREAMING_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(__snake_case						,**__snake_case	)

class 		UpperCamelCase__			(				A__       ):



					'''simple docstring'''

					__snake_case   :     Any		     =      "bridgetower"






					def __init__(							self						:   List[str]						,lowerCamelCase__						:   List[Any]=True						,lowerCamelCase__						:   str="gelu"						,lowerCamelCase__						:   Union[str, Any]=768						,lowerCamelCase__						:   Any=1						,lowerCamelCase__						:   Any=1e-0_5						,lowerCamelCase__						:   Tuple=False						,lowerCamelCase__						:   List[str]="add"						,lowerCamelCase__						:   Tuple=12						,lowerCamelCase__						:   Union[str, Any]=6						,lowerCamelCase__						:   Optional[Any]=False						,lowerCamelCase__						:   Tuple=False						,lowerCamelCase__						:   Any=None						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   List[Any]						,)					->		List[str]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						kwargs.pop("""text_config_dict"""						,__snake_case	)
											SCREAMING_SNAKE_CASE		=						kwargs.pop("""vision_config_dict"""						,__snake_case	)

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

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

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

											SCREAMING_SNAKE_CASE		=						BridgeTowerTextConfig(**__snake_case	)
											SCREAMING_SNAKE_CASE		=						BridgeTowerVisionConfig(**__snake_case	)






					@classmethod
					def     SCREAMING_SNAKE_CASE__  (							cls						:   int						,lowerCamelCase__						:   BridgeTowerTextConfig						,lowerCamelCase__						:   BridgeTowerVisionConfig						,**lowerCamelCase__						:   Tuple	)					->		str:




											'''simple docstring'''


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






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		List[Any]:




											'''simple docstring'''


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

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


