



import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase :				Any							=logging.get_logger(__name__)

UpperCAmelCase :				List[str]							={
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """vit-hybrid"""

				def __init__(			self  ,			snake_case__=None  ,			snake_case__=768  ,			snake_case__=12  ,			snake_case__=12  ,			snake_case__=3072  ,			snake_case__="gelu"  ,			snake_case__=0.0  ,			snake_case__=0.0  ,			snake_case__=0.02  ,			snake_case__=1e-12  ,			snake_case__=224  ,			snake_case__=1  ,			snake_case__=3  ,			snake_case__=[1, 1024, 24, 24]  ,			snake_case__=True  ,			**snake_case__  ,			):

									'''simple docstring'''


									super().__init__(**snake_case__     )

									if backbone_config is None:
														logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."     )
														UpperCamelCase_			= {
														    "global_padding": "same",
														    "layer_type": "bottleneck",
														    "depths": [3, 4, 9],
														    "out_features": ["stage3"],
														    "embedding_dynamic_padding": True,
														}

									if isinstance(snake_case__  ,			snake_case__     ):
														if "model_type" in backbone_config:
																			UpperCamelCase_			= CONFIG_MAPPING[backbone_config["model_type"]]
														else:
																			logger.info(
																			    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."     )
																			UpperCamelCase_			= BitConfig
														UpperCamelCase_			= backbone_config_class(**snake_case__     )

									UpperCamelCase_			= backbone_featmap_shape
									UpperCamelCase_			= backbone_config
									UpperCamelCase_			= hidden_size
									UpperCamelCase_			= num_hidden_layers
									UpperCamelCase_			= num_attention_heads
									UpperCamelCase_			= intermediate_size
									UpperCamelCase_			= hidden_act
									UpperCamelCase_			= hidden_dropout_prob
									UpperCamelCase_			= attention_probs_dropout_prob
									UpperCamelCase_			= initializer_range
									UpperCamelCase_			= layer_norm_eps
									UpperCamelCase_			= image_size
									UpperCamelCase_			= patch_size
									UpperCamelCase_			= num_channels
									UpperCamelCase_			= qkv_bias

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= copy.deepcopy(self.__dict__     )
									UpperCamelCase_			= self.backbone_config.to_dict()
									UpperCamelCase_			= self.__class__.model_type
									return output





from typing import TYPE_CHECKING

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


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
