

from math import ceil






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Dict:
	__lowerCamelCase				:				Optional[Any]					       = list(range(0	, lowerCamelCase__			)			)

	__lowerCamelCase				:				Optional[Any]					       = [item for sublist in list(device_map.values()			) for item in sublist]

	# Duplicate check
	__lowerCamelCase				:				Tuple					       = []
	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
	__lowerCamelCase				:				Tuple					       = [i for i in blocks if i not in device_map_blocks]
	__lowerCamelCase				:				int					       = [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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[Any]:
	__lowerCamelCase				:				Dict					       = list(range(lowerCamelCase__			)			)
	__lowerCamelCase				:				Union[str, Any]					       = int(ceil(n_layers / len(lowerCamelCase__			)			)			)
	__lowerCamelCase				:				Tuple					       = [layers[i : i + n_blocks] for i in range(0	, lowerCamelCase__	, lowerCamelCase__			)]

	return dict(zip(lowerCamelCase__	, lowerCamelCase__			)			)








from typing import TYPE_CHECKING

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


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

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


