



'''simple docstring'''





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


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "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        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"cvt"




    def __init__(       self   :	Tuple					,	snake_case_   :	Union[str, Any]=3					,	snake_case_   :	Optional[int]=[7, 3, 3]					,	snake_case_   :	str=[4, 2, 2]					,	snake_case_   :	Tuple=[2, 1, 1]					,	snake_case_   :	Dict=[64, 192, 384]					,	snake_case_   :	Optional[Any]=[1, 3, 6]					,	snake_case_   :	List[Any]=[1, 2, 10]					,	snake_case_   :	Union[str, Any]=[4.0, 4.0, 4.0]					,	snake_case_   :	Any=[0.0, 0.0, 0.0]					,	snake_case_   :	Optional[int]=[0.0, 0.0, 0.0]					,	snake_case_   :	Union[str, Any]=[0.0, 0.0, 0.1]					,	snake_case_   :	List[Any]=[True, True, True]					,	snake_case_   :	Optional[int]=[False, False, True]					,	snake_case_   :	Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]					,	snake_case_   :	List[str]=[3, 3, 3]					,	snake_case_   :	List[str]=[1, 1, 1]					,	snake_case_   :	List[str]=[2, 2, 2]					,	snake_case_   :	Optional[int]=[1, 1, 1]					,	snake_case_   :	Optional[Any]=[1, 1, 1]					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-1_2					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_sizes
          snake_case__    :       Optional[Any]       		= patch_stride
          snake_case__    :       List[str]       		= patch_padding
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Any       		= num_heads
          snake_case__    :       Any       		= depth
          snake_case__    :       Optional[int]       		= mlp_ratio
          snake_case__    :       Any       		= attention_drop_rate
          snake_case__    :       Optional[int]       		= drop_rate
          snake_case__    :       Any       		= drop_path_rate
          snake_case__    :       Any       		= qkv_bias
          snake_case__    :       Union[str, Any]       		= cls_token
          snake_case__    :       Union[str, Any]       		= qkv_projection_method
          snake_case__    :       Any       		= kernel_qkv
          snake_case__    :       Any       		= padding_kv
          snake_case__    :       Union[str, Any]       		= stride_kv
          snake_case__    :       int       		= padding_q
          snake_case__    :       Optional[Any]       		= stride_q
          snake_case__    :       Optional[Any]       		= initializer_range
          snake_case__    :       int       		= layer_norm_eps





'''simple docstring'''





from typing import TYPE_CHECKING

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


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
