"""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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    c_float







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return sizeof(self.value	)



def       A__    (	lowercase_     :	int      , lowercase_     :	int      , lowercase_     :	int = 0						)  ->							int:
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      __lowerCamelCase    		=					fixed_dimension

   dimension -= num_token_to_add
   return dimension



def       A__    (	lowercase_     :	int      , lowercase_     :	ParameterFormat						)  ->							int:
   return num_parameters * dtype.size



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

   try:
      return AutoProcessor.from_pretrained(lowercase_						)
   except (ValueError, OSError, KeyError):
      __lowerCamelCase		,__lowerCamelCase    		=					None, None
      try:
         __lowerCamelCase    		=					AutoTokenizer.from_pretrained(lowercase_						)
      except (OSError, KeyError):
         pass
      try:
         __lowerCamelCase    		=					AutoFeatureExtractor.from_pretrained(lowercase_						)
      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














"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

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


