



"""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


snake_case_				     =       logging.get_logger(__name__)







@dataclass
class       A_			(     __SCREAMING_SNAKE_CASE				):



						"""simple docstring"""
						__UpperCamelCase						=							[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(					self :List[Any]   ,  **lowercase_ :Optional[Any]					)				->			Dict:
												for deprecated_arg in self.deprecated_args:
																		if deprecated_arg in kwargs:
																								UpperCAmelCase             = deprecated_arg[3:]
																								UpperCAmelCase             = not kwargs.pop(lowercase_					)
																								logger.warning(
																								    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																								    f""" {positive_arg}={kwargs[positive_arg]}"""					)
												UpperCAmelCase             = kwargs.pop('tpu_name'   ,  self.tpu_name					)
												UpperCAmelCase             = kwargs.pop('device_idx'   ,  self.device_idx					)
												UpperCAmelCase             = kwargs.pop('eager_mode'   ,  self.eager_mode					)
												UpperCAmelCase             = kwargs.pop('use_xla'   ,  self.use_xla					)
												super().__init__(**lowercase_					)

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






						@cached_property
						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Union[str, Any]:
												requires_backends(self   ,  ['tf']					)
												UpperCAmelCase             = None
												if self.tpu:
																		try:
																								if self.tpu_name:
																														UpperCAmelCase             = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name					)
																								else:
																														UpperCAmelCase             = tf.distribute.cluster_resolver.TPUClusterResolver()
																		except ValueError:
																								UpperCAmelCase             = None
												return tpu






						@cached_property
						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Optional[Any]:
												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					)

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

												return strategy






						@property
						def 	UpperCAmelCase__     (					self :List[str]					)				->			Any:
												requires_backends(self   ,  ['tf']					)
												return self._setup_tpu is not None






						@property
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Optional[int]:
												requires_backends(self   ,  ['tf']					)
												return self._setup_strategy






						@property
						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												requires_backends(self   ,  ['tf']					)
												return tf.config.list_physical_devices('GPU'					)






						@property
						def 	UpperCAmelCase__     (					self :int					)				->			List[Any]:
												requires_backends(self   ,  ['tf']					)
												if self.cuda:
																		return len(self.gpu_list					)
												return 0






						@property
						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Optional[Any]:
												return self.n_gpu > 0







import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""segformer"""
      def __init__(      self    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=[2, 2, 2, 2]    ,  __magic_name__=[8, 4, 2, 1]    ,  __magic_name__=[3_2, 6_4, 1_6_0, 2_5_6]    ,  __magic_name__=[7, 3, 3, 3]    ,  __magic_name__=[4, 2, 2, 2]    ,  __magic_name__=[1, 2, 5, 8]    ,  __magic_name__=[4, 4, 4, 4]    ,  __magic_name__="gelu"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=0.02    ,  __magic_name__=0.1    ,  __magic_name__=1e-6    ,  __magic_name__=2_5_6    ,  __magic_name__=2_5_5    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
        warnings.warn(
            """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
            """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,  __magic_name__    ,  )

       lowerCamelCase		:	Optional[Any]							=      num_channels
       lowerCamelCase		:	str							=      num_encoder_blocks
       lowerCamelCase		:	Any							=      depths
       lowerCamelCase		:	List[Any]							=      sr_ratios
       lowerCamelCase		:	int							=      hidden_sizes
       lowerCamelCase		:	Union[str, Any]							=      patch_sizes
       lowerCamelCase		:	Optional[Any]							=      strides
       lowerCamelCase		:	Dict							=      mlp_ratios
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      classifier_dropout_prob
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	Dict							=      drop_path_rate
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	Any							=      decoder_hidden_size
       lowerCamelCase		:	str							=      kwargs.get("""reshape_last_stage"""    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      semantic_loss_ignore_index





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




