





import copy
import os
from typing import Union

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


__A				:    List[str]       =   logging.get_logger(__name__)

__A				:    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 	A_       (a_			):
	UpperCAmelCase__					=			'''bridgetower_vision_model'''
	def __init__(    self							,					_A=7_6_8							,					_A=1_2							,					_A=3							,					_A=1_6							,					_A=2_8_8							,					_A=1							,					_A=1E-05							,					_A=False							,					_A=True							,					_A=False							,					**_A							,					):






				'''simple docstring'''







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







	@classmethod
	def      _lowercase				(    cls							,					_A							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				cls.get_config_dict(_A							,					**_A		)

				if config_dict.get('''model_type'''		) == "bridgetower":
							UpperCAmelCase   		=				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_       (a_			):
	UpperCAmelCase__					=			'''bridgetower_text_model'''
	def __init__(    self							,					_A=5_0_2_6_5							,					_A=7_6_8							,					_A=1_2							,					_A=1_2							,					_A=1							,					_A=3_0_7_2							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=5_1_4							,					_A=1							,					_A=1E-05							,					_A=1							,					_A=0							,					_A=2							,					_A="absolute"							,					_A=True							,					**_A							,					):






				'''simple docstring'''







				super().__init__(**_A		)

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







	@classmethod
	def      _lowercase				(    cls							,					_A							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				cls.get_config_dict(_A							,					**_A		)

				if config_dict.get('''model_type'''		) == "bridgetower":
							UpperCAmelCase   		=				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_       (a_			):
	UpperCAmelCase__					=			'''bridgetower'''
	def __init__(    self							,					_A=True							,					_A="gelu"							,					_A=7_6_8							,					_A=1							,					_A=1E-05							,					_A=False							,					_A="add"							,					_A=1_2							,					_A=6							,					_A=False							,					_A=False							,					_A=None							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				kwargs.pop('''text_config_dict'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''vision_config_dict'''							,					_A		)

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

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

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

				UpperCAmelCase   		=				BridgeTowerTextConfig(**_A		)
				UpperCAmelCase   		=				BridgeTowerVisionConfig(**_A		)
	@classmethod
	def      _lowercase				(    cls							,					_A							,					_A							,					**_A		):






				'''simple docstring'''







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







	def      _lowercase				(    self		):






				'''simple docstring'''







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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				:    Dict       =   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Any       =   [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				__A				:    int       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



