




"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      =    {
    '''configuration_blenderbot_small''': [
        '''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotSmallConfig''',
        '''BlenderbotSmallOnnxConfig''',
    ],
    '''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BlenderbotSmallForCausalLM''',
         '''BlenderbotSmallForConditionalGeneration''',
         '''BlenderbotSmallModel''',
         '''BlenderbotSmallPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''TFBlenderbotSmallForConditionalGeneration''',
         '''TFBlenderbotSmallModel''',
         '''TFBlenderbotSmallPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''FlaxBlenderbotSmallForConditionalGeneration''',
         '''FlaxBlenderbotSmallModel''',
         '''FlaxBlenderbotSmallPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_blenderbot_small import (
         BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotSmallConfig,
         BlenderbotSmallOnnxConfig,
     )
     from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot_small import (
              BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotSmallForCausalLM,
              BlenderbotSmallForConditionalGeneration,
              BlenderbotSmallModel,
              BlenderbotSmallPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot_small import (
              TFBlenderbotSmallForConditionalGeneration,
              TFBlenderbotSmallModel,
              TFBlenderbotSmallPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot_small import (
              FlaxBlenderbotSmallForConditionalGeneration,
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallPreTrainedModel,
          )

else:
     import sys

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








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = "swin"

 lowercase__			 = {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(    self:		Any,				a_:		List[str]=224,				a_:		List[Any]=4,				a_:		List[Any]=3,				a_:		Dict=96,				a_:		List[str]=[2, 2, 6, 2],				a_:		int=[3, 6, 12, 24],				a_:		int=7,				a_:		str=4.0,				a_:		Optional[Any]=True,				a_:		Dict=0.0,				a_:		List[Any]=0.0,				a_:		List[str]=0.1,				a_:		Union[str, Any]="gelu",				a_:		Dict=False,				a_:		Union[str, Any]=0.02,				a_:		Optional[int]=1E-5,				a_:		Optional[int]=32,				a_:		Tuple=None,				a_:		Union[str, Any]=None,				**a_:		Any,				):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		Any								=  image_size
   _snake_case							:		List[Any]								=  patch_size
   _snake_case							:		Tuple								=  num_channels
   _snake_case							:		str								=  embed_dim
   _snake_case							:		Union[str, Any]								=  depths
   _snake_case							:		int								=  len(a_		)
   _snake_case							:		Union[str, Any]								=  num_heads
   _snake_case							:		List[str]								=  window_size
   _snake_case							:		str								=  mlp_ratio
   _snake_case							:		Union[str, Any]								=  qkv_bias
   _snake_case							:		Dict								=  hidden_dropout_prob
   _snake_case							:		str								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  drop_path_rate
   _snake_case							:		Optional[int]								=  hidden_act
   _snake_case							:		str								=  use_absolute_embeddings
   _snake_case							:		Tuple								=  layer_norm_eps
   _snake_case							:		List[Any]								=  initializer_range
   _snake_case							:		Optional[Any]								=  encoder_stride
   # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   _snake_case							:		Any								=  int(embed_dim * 2 ** (len(a_		) - 1)		)
   _snake_case							:		Any								=  ["""stem"""] + [f"stage{idx}" for idx in range(1,				len(a_		) + 1		)]
   _snake_case		, _snake_case							:		List[str]								=  get_aligned_output_features_output_indices(
       out_features=a_,				out_indices=a_,				stage_names=self.stage_names		)







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = version.parse("1.11"			)


 @property
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]		)


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return 1E-4


