
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   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      c_float




      @property
      def 						snake_case_     ( self						:    List[str]			):
          return sizeof(self.value			)






def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase = 0		)	->  int:
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        __lowercase						:     List[str]		       =    fixed_dimension

    dimension -= num_token_to_add
    return dimension


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  int:
    return num_parameters * dtype.size





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

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




from typing import TYPE_CHECKING

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


__lowerCAmelCase   :     Optional[int]      =						{"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     List[Any]      =						[
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     Tuple      =						["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 __lowerCAmelCase   :     Tuple      =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


