




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__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[int]     					=				c_float







    @property
    def    _lowercase					(   self					)					->				int:
           return sizeof(self.value					)




def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 0   )     ->			int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              lowerCamelCase       :				Dict      			= fixed_dimension

       dimension -= num_token_to_add
       return dimension

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			int:
       return num_parameters * dtype.size


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

       try:
              return AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE   )
       except (ValueError, OSError, KeyError):
              lowerCamelCase			,				lowerCamelCase       :				Tuple      			= None, None
              try:
                     lowerCamelCase       :				List[str]      			= AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
              except (OSError, KeyError):
                     pass
              try:
                     lowerCamelCase       :				Tuple      			= AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE   )
              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


SCREAMING_SNAKE_CASE__	:				Dict  =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       ['OneFormerImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OneFormerForUniversalSegmentation',
           'OneFormerModel',
           'OneFormerPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
       from .processing_oneformer import OneFormerProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_oneformer import OneFormerImageProcessor
       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_oneformer import (
                  ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OneFormerForUniversalSegmentation,
                  OneFormerModel,
                  OneFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				str  =       _LazyModule(__name__, globals()['__file__'], _import_structure)






