




import copy

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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """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 			a__    (					snake_case__      ):
						_a							:    Optional[Any]					=    """dpt"""






						def __init__(   self		,			_A=7_6_8		,			_A=1_2		,			_A=1_2		,			_A=3_0_7_2		,			_A="gelu"		,			_A=0.0		,			_A=0.0		,			_A=0.02		,			_A=1E-1_2		,			_A=3_8_4		,			_A=1_6		,			_A=3		,			_A=False		,			_A=True		,			_A=[2, 5, 8, 1_1]		,			_A="project"		,			_A=[4, 2, 1, 0.5]		,			_A=[9_6, 1_9_2, 3_8_4, 7_6_8]		,			_A=2_5_6		,			_A=-1		,			_A=False		,			_A=True		,			_A=0.4		,			_A=2_5_5		,			_A=0.1		,			_A=[1, 1_0_2_4, 2_4, 2_4]		,			_A=[0, 1]		,			_A=None		,			**_A		,			):




											"""simple docstring"""



											super().__init__(**_A			)

											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       is_hybrid

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

																__lowerCAmelCase				   =       backbone_featmap_shape
																__lowerCAmelCase				   =       neck_ignore_stages

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

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       copy.deepcopy(self.__dict__			)

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

											__lowerCAmelCase				   =       self.__class__.model_type
											return output










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


UpperCamelCase__    			=			logging.get_logger(__name__)
@dataclass
class 			a__    (					snake_case__      ):
						_a							:    List[str]					=    [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(   self		,			**_A			):




											"""simple docstring"""



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

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






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""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			)

																__lowerCAmelCase				   =       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"			)
																					__lowerCAmelCase				   =       tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""			)
																else:
																					tf.config.set_visible_devices([]		,			"GPU"			)  # disable GPU
																					__lowerCAmelCase				   =       tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""			)

											return strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.n_gpu > 0




