






"""simple docstring"""




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   :						Union[str, Any]							=  logging.get_logger(__name__)

__UpperCamelCase   :						List[Any]							=  {
    """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 					__SCREAMING_SNAKE_CASE(		lowerCAmelCase__		):
							_UpperCAmelCase	      =    "vit-hybrid"
							def __init__(       self: List[Any]    ,		UpperCamelCase: Tuple=None    ,		UpperCamelCase: int=7_68    ,		UpperCamelCase: Any=12    ,		UpperCamelCase: Tuple=12    ,		UpperCamelCase: List[str]=30_72    ,		UpperCamelCase: Dict="gelu"    ,		UpperCamelCase: Dict=0.0    ,		UpperCamelCase: Union[str, Any]=0.0    ,		UpperCamelCase: List[str]=0.02    ,		UpperCamelCase: List[str]=1e-12    ,		UpperCamelCase: str=2_24    ,		UpperCamelCase: List[Any]=1    ,		UpperCamelCase: List[str]=3    ,		UpperCamelCase: Any=[1, 10_24, 24, 24]    ,		UpperCamelCase: Any=True    ,		**UpperCamelCase: List[Any]    ,		)						->						Any:
											super().__init__(**_UpperCAmelCase  )

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

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

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


							def lowerCAmelCase_	(       self: Optional[int]  )						->						Dict[str, any]:
											snake_case__													=       copy.deepcopy(self.__dict__  )
											snake_case__													=       self.backbone_config.to_dict()
											snake_case__													=       self.__class__.model_type
											return output












from typing import TYPE_CHECKING

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


__UpperCamelCase   :						int							=  {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Optional[Any]							=  ["""OpenAIGPTTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						List[str]							=  [
							    """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """OpenAIGPTDoubleHeadsModel""",
							    """OpenAIGPTForSequenceClassification""",
							    """OpenAIGPTLMHeadModel""",
							    """OpenAIGPTModel""",
							    """OpenAIGPTPreTrainedModel""",
							    """load_tf_weights_in_openai_gpt""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Dict							=  [
							    """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFOpenAIGPTDoubleHeadsModel""",
							    """TFOpenAIGPTForSequenceClassification""",
							    """TFOpenAIGPTLMHeadModel""",
							    """TFOpenAIGPTMainLayer""",
							    """TFOpenAIGPTModel""",
							    """TFOpenAIGPTPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
							from .tokenization_openai import OpenAIGPTTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_openai_fast import OpenAIGPTTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_openai import (
														    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    OpenAIGPTDoubleHeadsModel,
														    OpenAIGPTForSequenceClassification,
														    OpenAIGPTLMHeadModel,
														    OpenAIGPTModel,
														    OpenAIGPTPreTrainedModel,
														    load_tf_weights_in_openai_gpt,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_openai import (
														    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFOpenAIGPTDoubleHeadsModel,
														    TFOpenAIGPTForSequenceClassification,
														    TFOpenAIGPTLMHeadModel,
														    TFOpenAIGPTMainLayer,
														    TFOpenAIGPTModel,
														    TFOpenAIGPTPreTrainedModel,
														)

else:
							import sys

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




