




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : Optional[Any]		= {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    : Optional[int]		= [
      "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RegNetForImageClassification",
      "RegNetModel",
      "RegNetPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    : Optional[int]		= [
      "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRegNetForImageClassification",
      "TFRegNetModel",
      "TFRegNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    : Tuple		= [
      "FlaxRegNetForImageClassification",
      "FlaxRegNetModel",
      "FlaxRegNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase    : Tuple		= _LazyModule(__name__, globals()["__file__"], _import_structure)













'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor