






"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""LEDTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

  UpperCamelCase					=     _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


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       "swiftformer"

    def __init__(  self:			Tuple     ,    UpperCamelCase:			int=3     ,    UpperCamelCase:			Union[str, Any]=[3, 3, 6, 4]     ,    UpperCamelCase:			List[str]=[48, 56, 1_12, 2_20]     ,    UpperCamelCase:			List[Any]=4     ,    UpperCamelCase:			List[Any]=[True, True, True, True]     ,    UpperCamelCase:			str="gelu"     ,    UpperCamelCase:			Tuple=3     ,    UpperCamelCase:			Dict=2     ,    UpperCamelCase:			int=1     ,    UpperCamelCase:			List[Any]=0.0     ,    UpperCamelCase:			Any=True     ,    UpperCamelCase:			List[Any]=1e-5     ,    UpperCamelCase:			List[Any]=1e-5     ,    **UpperCamelCase:			Any     ,    ):






         """simple docstring"""


         super().__init__(**UpperCamelCase							)
         A__				      =	num_channels
         A__				      =	depths
         A__				      =	embed_dims
         A__				      =	mlp_ratio
         A__				      =	downsamples
         A__				      =	hidden_act
         A__				      =	down_patch_size
         A__				      =	down_stride
         A__				      =	down_pad
         A__				      =	drop_path_rate
         A__				      =	use_layer_scale
         A__				      =	layer_scale_init_value
         A__				      =	batch_norm_eps



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       version.parse("1.11"					)

    @property
    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


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





    @property
    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         return 1e-4






