



"""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


_lowerCAmelCase    : str							=							logging.get_logger(__name__)

_lowerCAmelCase    : Union[str, Any]							=							{
    "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      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			'swin'

      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			{
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self					:     Dict					,     A					:     Union[str, Any]=2_2_4					,     A					:     Tuple=4					,     A					:     int=3					,     A					:     Optional[Any]=9_6					,     A					:     Union[str, Any]=[2, 2, 6, 2]					,     A					:     Optional[Any]=[3, 6, 1_2, 2_4]					,     A					:     Optional[Any]=7					,     A					:     List[Any]=4.0					,     A					:     Dict=True					,     A					:     Union[str, Any]=0.0					,     A					:     List[str]=0.0					,     A					:     Tuple=0.1					,     A					:     Any="gelu"					,     A					:     Tuple=False					,     A					:     Union[str, Any]=0.02					,     A					:     Optional[Any]=1e-5					,     A					:     Optional[Any]=3_2					,     A					:     List[Any]=None					,     A					:     int=None					,     **A					:     int					,     ):
           super().__init__(**A						)

           _UpperCAmelCase      :     str        =   image_size
           _UpperCAmelCase      :     int        =   patch_size
           _UpperCAmelCase      :     Any        =   num_channels
           _UpperCAmelCase      :     Optional[Any]        =   embed_dim
           _UpperCAmelCase      :     Dict        =   depths
           _UpperCAmelCase      :     Any        =   len(A						)
           _UpperCAmelCase      :     Optional[Any]        =   num_heads
           _UpperCAmelCase      :     List[Any]        =   window_size
           _UpperCAmelCase      :     Optional[Any]        =   mlp_ratio
           _UpperCAmelCase      :     Dict        =   qkv_bias
           _UpperCAmelCase      :     int        =   hidden_dropout_prob
           _UpperCAmelCase      :     Any        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     List[str]        =   drop_path_rate
           _UpperCAmelCase      :     List[Any]        =   hidden_act
           _UpperCAmelCase      :     Union[str, Any]        =   use_absolute_embeddings
           _UpperCAmelCase      :     Tuple        =   layer_norm_eps
           _UpperCAmelCase      :     Any        =   initializer_range
           _UpperCAmelCase      :     List[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
           _UpperCAmelCase      :     Optional[Any]        =   int(embed_dim * 2 ** (len(A						) - 1)						)
           _UpperCAmelCase      :     Tuple        =   ["stem"] + [f'stage{idx}' for idx in range(1					,     len(A						) + 1						)]
           _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   get_aligned_output_features_output_indices(
               out_features=A					,     out_indices=A					,     stage_names=self.stage_names						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			version.parse('1.11'						)



      @property
      def 						snake_case_ (					self					:     str						):
           return OrderedDict(
               [
                   ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ]						)



      @property
      def 						snake_case_ (					self					:     int						):
           return 1e-4





"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							["RoFormerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[int]							=							[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[str]							=							[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

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