



'''simple docstring'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowercase_      :				Tuple   					=    logging.get_logger(__name__)

lowercase_      :				List[Any]   					=    {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''dpt'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=384 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=False ,				_lowerCAmelCase=True ,				_lowerCAmelCase=[2, 5, 8, 11] ,				_lowerCAmelCase="project" ,				_lowerCAmelCase=[4, 2, 1, 0.5] ,				_lowerCAmelCase=[96, 192, 384, 768] ,				_lowerCAmelCase=256 ,				_lowerCAmelCase=-1 ,				_lowerCAmelCase=False ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.4 ,				_lowerCAmelCase=255 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=[1, 1024, 24, 24] ,				_lowerCAmelCase=[0, 1] ,				_lowerCAmelCase=None ,				**_lowerCAmelCase ,				)    ->    Optional[int]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							is_hybrid

							if self.is_hybrid:
													if backbone_config is None:
																			logger.info("""Initializing the config with a `BiT` backbone."""						)
																			lowercase        =							{
																			    """global_padding""": """same""",
																			    """layer_type""": """bottleneck""",
																			    """depths""": [3, 4, 9],
																			    """out_features""": ["""stage1""", """stage2""", """stage3"""],
																			    """embedding_dynamic_padding""": True,
																			}
																			lowercase        =							BitConfig(**_lowerCAmelCase						)
													elif isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			logger.info("""Initializing the config with a `BiT` backbone."""						)
																			lowercase        =							BitConfig(**_lowerCAmelCase						)
													elif isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			lowercase        =							backbone_config
													else:
																			raise ValueError(
																			    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""						)

													lowercase        =							backbone_featmap_shape
													lowercase        =							neck_ignore_stages

													if readout_type != "project":
																			raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""						)
							else:
													lowercase        =							None
													lowercase        =							None
													lowercase        =							[]

							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias
							lowercase        =							backbone_out_indices
							if readout_type not in ["ignore", "add", "project"]:
													raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""						)
							lowercase        =							readout_type
							lowercase        =							reassemble_factors
							lowercase        =							neck_hidden_sizes
							lowercase        =							fusion_hidden_size
							lowercase        =							head_in_index
							lowercase        =							use_batch_norm_in_fusion_residual
							# auxiliary head attributes (semantic segmentation)
							lowercase        =							use_auxiliary_head
							lowercase        =							auxiliary_loss_weight
							lowercase        =							semantic_loss_ignore_index
							lowercase        =							semantic_classifier_dropout







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							copy.deepcopy(self.__dict__						)

							if output["backbone_config"] is not None:
													lowercase        =							self.backbone_config.to_dict()

							lowercase        =							self.__class__.model_type
							return output







'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
						import tensorflow as tf


lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)




@dataclass
class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					[
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]







	def __init__(    self ,				**_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






							for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																			lowercase        =							deprecated_arg[3:]
																			lowercase        =							not kwargs.pop(_lowerCAmelCase						)
																			logger.warning(
																			    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																			    F""" {positive_arg}={kwargs[positive_arg]}"""						)
							lowercase        =							kwargs.pop("""tpu_name""" ,				self.tpu_name						)
							lowercase        =							kwargs.pop("""device_idx""" ,				self.device_idx						)
							lowercase        =							kwargs.pop("""eager_mode""" ,				self.eager_mode						)
							lowercase        =							kwargs.pop("""use_xla""" ,				self.use_xla						)
							super().__init__(**_lowerCAmelCase						)

	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Name of TPU'''}			,   )
	__A			  =					field(
	    default=0			,   metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}			,   )
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Benchmark models in eager model.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={
	        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
	    }			,   )







	@cached_property
	def   _a   (    self						)    ->    Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							lowercase        =							None
							if self.tpu:
													try:
																			if self.tpu_name:
																									lowercase        =							tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name						)
																			else:
																									lowercase        =							tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																			lowercase        =							None
							return tpu







	@cached_property
	def   _a   (    self						)    ->    Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							if self.is_tpu:
													tf.config.experimental_connect_to_cluster(self._setup_tpu						)
													tf.tpu.experimental.initialize_tpu_system(self._setup_tpu						)

													lowercase        =							tf.distribute.TPUStrategy(self._setup_tpu						)
							else:
													# currently no multi gpu is allowed
													if self.is_gpu:
																			# TODO: Currently only single GPU is supported
																			tf.config.set_visible_devices(self.gpu_list[self.device_idx] ,				"""GPU"""						)
																			lowercase        =							tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""						)
													else:
																			tf.config.set_visible_devices([] ,				"""GPU"""						)  # disable GPU
																			lowercase        =							tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""						)

							return strategy







	@property
	def   _a   (    self						)    ->    bool:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							return self._setup_tpu is not None







	@property
	def   _a   (    self						)    ->    "tf.distribute.Strategy":
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							return self._setup_strategy







	@property
	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							return tf.config.list_physical_devices("""GPU"""						)







	@property
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							requires_backends(self ,				["""tf"""]						)
							if self.cuda:
													return len(self.gpu_list						)
							return 0







	@property
	def   _a   (    self						)    ->    bool:
							'''simple docstring'''






							return self.n_gpu > 0


