




'''simple docstring'''





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


__lowerCAmelCase	:       str     =	logging.get_logger(__name__)

__lowerCAmelCase	:       Dict     =	{
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}




class 					snake_case__							(__lowerCAmelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      """cvt"""





				def __init__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Any=3      ,   __lowerCamelCase       :  List[str]=[7, 3, 3]      ,   __lowerCamelCase       :  Dict=[4, 2, 2]      ,   __lowerCamelCase       :  List[Any]=[2, 1, 1]      ,   __lowerCamelCase       :  Union[str, Any]=[64, 1_92, 3_84]      ,   __lowerCamelCase       :  Optional[int]=[1, 3, 6]      ,   __lowerCamelCase       :  Dict=[1, 2, 10]      ,   __lowerCamelCase       :  List[Any]=[4.0, 4.0, 4.0]      ,   __lowerCamelCase       :  str=[0.0, 0.0, 0.0]      ,   __lowerCamelCase       :  Any=[0.0, 0.0, 0.0]      ,   __lowerCamelCase       :  List[str]=[0.0, 0.0, 0.1]      ,   __lowerCamelCase       :  int=[True, True, True]      ,   __lowerCamelCase       :  Tuple=[False, False, True]      ,   __lowerCamelCase       :  str=["dw_bn", "dw_bn", "dw_bn"]      ,   __lowerCamelCase       :  List[Any]=[3, 3, 3]      ,   __lowerCamelCase       :  Tuple=[1, 1, 1]      ,   __lowerCamelCase       :  List[str]=[2, 2, 2]      ,   __lowerCamelCase       :  List[str]=[1, 1, 1]      ,   __lowerCamelCase       :  int=[1, 1, 1]      ,   __lowerCamelCase       :  Optional[Any]=0.02      ,   __lowerCamelCase       :  int=1e-12      ,   **__lowerCamelCase       :  List[str]      ,   )      ->  List[Any]:
							super().__init__(**_UpperCamelCase							)
							a					=    num_channels
							a					=    patch_sizes
							a					=    patch_stride
							a					=    patch_padding
							a					=    embed_dim
							a					=    num_heads
							a					=    depth
							a					=    mlp_ratio
							a					=    attention_drop_rate
							a					=    drop_rate
							a					=    drop_path_rate
							a					=    qkv_bias
							a					=    cls_token
							a					=    qkv_projection_method
							a					=    kernel_qkv
							a					=    padding_kv
							a					=    stride_kv
							a					=    padding_q
							a					=    stride_q
							a					=    initializer_range
							a					=    layer_norm_eps












from typing import TYPE_CHECKING

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


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCAmelCase	:       Union[str, Any]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






