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


__lowerCamelCase    :				str							     =	logging.get_logger(__name__)







@dataclass
class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							[
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]




   def __init__(					self    :       str ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






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

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




   @cached_property
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






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




   @cached_property
   def 			__a		(					self    :       Dict   ):



     """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   )

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

     return strategy




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




   @property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






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




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




   @property
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     return self.n_gpu > 0

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     if self.label_column not in features:
       raise ValueError(f"""Column {self.label_column} is not present in features."""   )
     if not isinstance(features[self.label_column] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
