"""simple docstring"""






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


lowerCAmelCase__						:						Optional[Any]		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						List[Any]		  =			{
    """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 (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"cvt"







					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]=3					,lowerCamelCase__     :			int=[7, 3, 3]					,lowerCamelCase__     :			Optional[Any]=[4, 2, 2]					,lowerCamelCase__     :			int=[2, 1, 1]					,lowerCamelCase__     :			str=[64, 192, 384]					,lowerCamelCase__     :			Union[str, Any]=[1, 3, 6]					,lowerCamelCase__     :			Dict=[1, 2, 10]					,lowerCamelCase__     :			List[str]=[4.0, 4.0, 4.0]					,lowerCamelCase__     :			int=[0.0, 0.0, 0.0]					,lowerCamelCase__     :			Optional[Any]=[0.0, 0.0, 0.0]					,lowerCamelCase__     :			Any=[0.0, 0.0, 0.1]					,lowerCamelCase__     :			int=[True, True, True]					,lowerCamelCase__     :			Any=[False, False, True]					,lowerCamelCase__     :			Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"]					,lowerCamelCase__     :			int=[3, 3, 3]					,lowerCamelCase__     :			List[Any]=[1, 1, 1]					,lowerCamelCase__     :			List[Any]=[2, 2, 2]					,lowerCamelCase__     :			Any=[1, 1, 1]					,lowerCamelCase__     :			List[str]=[1, 1, 1]					,lowerCamelCase__     :			Optional[int]=0.0_2					,lowerCamelCase__     :			Optional[int]=1e-12					,**lowerCamelCase__     :			str					,):
									super().__init__(**lowerCamelCase__    )
									UpperCAmelCase__    =							num_channels
									UpperCAmelCase__    =							patch_sizes
									UpperCAmelCase__    =							patch_stride
									UpperCAmelCase__    =							patch_padding
									UpperCAmelCase__    =							embed_dim
									UpperCAmelCase__    =							num_heads
									UpperCAmelCase__    =							depth
									UpperCAmelCase__    =							mlp_ratio
									UpperCAmelCase__    =							attention_drop_rate
									UpperCAmelCase__    =							drop_rate
									UpperCAmelCase__    =							drop_path_rate
									UpperCAmelCase__    =							qkv_bias
									UpperCAmelCase__    =							cls_token
									UpperCAmelCase__    =							qkv_projection_method
									UpperCAmelCase__    =							kernel_qkv
									UpperCAmelCase__    =							padding_kv
									UpperCAmelCase__    =							stride_kv
									UpperCAmelCase__    =							padding_q
									UpperCAmelCase__    =							stride_q
									UpperCAmelCase__    =							initializer_range
									UpperCAmelCase__    =							layer_norm_eps




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__						:						int		  =			{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Union[str, Any]		  =			[
						    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TapasForMaskedLM',
						    'TapasForQuestionAnswering',
						    'TapasForSequenceClassification',
						    'TapasModel',
						    'TapasPreTrainedModel',
						    'load_tf_weights_in_tapas',
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Optional[Any]		  =			[
						    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFTapasForMaskedLM',
						    'TFTapasForQuestionAnswering',
						    'TFTapasForSequenceClassification',
						    'TFTapasModel',
						    'TFTapasPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
						from .tokenization_tapas import TapasTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tapas import (
												    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TapasForMaskedLM,
												    TapasForQuestionAnswering,
												    TapasForSequenceClassification,
												    TapasModel,
												    TapasPreTrainedModel,
												    load_tf_weights_in_tapas,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_tapas import (
												    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTapasForMaskedLM,
												    TFTapasForQuestionAnswering,
												    TFTapasForSequenceClassification,
												    TFTapasModel,
												    TFTapasPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase__						:						Dict		  =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



