
"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase			:       Optional[int]									=				{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				[
          "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CanineForMultipleChoice",
          "CanineForQuestionAnswering",
          "CanineForSequenceClassification",
          "CanineForTokenClassification",
          "CanineLayer",
          "CanineModel",
          "CaninePreTrainedModel",
          "load_tf_weights_in_canine",
      ]


if TYPE_CHECKING:
      from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
      from .tokenization_canine import CanineTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_canine import (
                CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                CanineForMultipleChoice,
                CanineForQuestionAnswering,
                CanineForSequenceClassification,
                CanineForTokenClassification,
                CanineLayer,
                CanineModel,
                CaninePreTrainedModel,
                load_tf_weights_in_canine,
            )


else:
      import sys

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





"""simple docstring"""

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


UpperCamelCase			:       int									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"bit"
     lowercase		     =		["preactivation", "bottleneck"]
     lowercase		     =		["SAME", "VALID"]





     def __init__(			self			,  __UpperCAmelCase=3			,  __UpperCAmelCase=64			,  __UpperCAmelCase=[256, 512, 1024, 2048]			,  __UpperCAmelCase=[3, 4, 6, 3]			,  __UpperCAmelCase="preactivation"			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=1			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      if layer_type not in self.layer_types:
       raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
      if global_padding is not None:
       if global_padding.upper() in self.supported_padding:
        __UpperCamelCase       						= global_padding.upper()
       else:
        raise ValueError(F'Padding strategy {global_padding} not supported'   )
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embedding_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= layer_type
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= global_padding
      __UpperCamelCase       						= num_groups
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= embedding_dynamic_padding
      __UpperCamelCase       						= output_stride
      __UpperCamelCase       						= width_factor

      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )



