



from math import ceil






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int				)       ->							Tuple:

							'''simple docstring'''






							A      						=    list(range(0			,	lowerCAmelCase__				)				)

							A      						=    [item for sublist in list(device_map.values()				) for item in sublist]

							# Duplicate check
							A      						=    []
							for i in device_map_blocks:
														if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
																					duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
							A      						=    [i for i in blocks if i not in device_map_blocks]
							A      						=    [i for i in device_map_blocks if i not in blocks]

							if len(lowerCAmelCase__				) != 0:
														raise ValueError(
														    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
														    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
							if len(lowerCAmelCase__				) != 0:
														raise ValueError(
														    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
														    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
							if len(lowerCAmelCase__				) != 0:
														raise ValueError(
														    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
														    + str(lowerCAmelCase__				)				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	str				)       ->							Any:

							'''simple docstring'''






							A      						=    list(range(lowerCAmelCase__				)				)
							A      						=    int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
							A      						=    [layers[i : i + n_blocks] for i in range(0			,	lowerCAmelCase__			,	lowerCAmelCase__				)]

							return dict(zip(lowerCAmelCase__			,	lowerCAmelCase__				)				)






import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        warnings.warn(
            '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use GLPNImageProcessor instead.'''				,							_UpperCamelCase				,							)
        super().__init__(*_UpperCamelCase				,							**_UpperCamelCase					)