
'''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 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Optional[Any]								=		c_float




	@property
	def 	__A      (						self				: int      ):
			return sizeof(self.value      )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE = 0  ):
		# < 0 is possible if using a dynamic axis
		if dimension <= 0:
				lowerCAmelCase_   :       Any    							=fixed_dimension

		dimension -= num_token_to_add
		return dimension







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		return num_parameters * dtype.size







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

		try:
				return AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE  )
		except (ValueError, OSError, KeyError):
				lowerCAmelCase_							,		lowerCAmelCase_   :       Optional[Any]    							=None, None
				try:
						lowerCAmelCase_   :       str    							=AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  )
				except (OSError, KeyError):
						pass
				try:
						lowerCAmelCase_   :       List[str]    							=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








'''simple docstring'''






from typing import TYPE_CHECKING

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


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''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

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






