






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


__lowerCamelCase						= logging.get_logger(__name__)

__lowerCamelCase						= {
    '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      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''vit-hybrid'''




						def __init__(    self		,	lowercase=None		,	lowercase=768		,	lowercase=12		,	lowercase=12		,	lowercase=3_072		,	lowercase="gelu"		,	lowercase=0.0		,	lowercase=0.0		,	lowercase=0.02		,	lowercase=1e-12		,	lowercase=224		,	lowercase=1		,	lowercase=3		,	lowercase=[1, 1_024, 24, 24]		,	lowercase=True		,	**lowercase		,	)			->						Dict:
													super().__init__(**lowercase       )

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

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

													_a  :    Optional[Any]			=						backbone_featmap_shape
													_a  :    List[Any]			=						backbone_config
													_a  :    List[Any]			=						hidden_size
													_a  :    Optional[Any]			=						num_hidden_layers
													_a  :    List[Any]			=						num_attention_heads
													_a  :    List[Any]			=						intermediate_size
													_a  :    Any			=						hidden_act
													_a  :    str			=						hidden_dropout_prob
													_a  :    List[str]			=						attention_probs_dropout_prob
													_a  :    str			=						initializer_range
													_a  :    Dict			=						layer_norm_eps
													_a  :    Optional[Any]			=						image_size
													_a  :    List[str]			=						patch_size
													_a  :    Any			=						num_channels
													_a  :    Any			=						qkv_bias







						def      snake_case__(    self       )			->						Dict[str, any]:
													_a  :    Dict			=						copy.deepcopy(self.__dict__       )
													_a  :    List[str]			=						self.backbone_config.to_dict()
													_a  :    int			=						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,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= ['BartTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)