




"""simple docstring"""




import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


__A       :						List[str]												=			logging.get_logger(__name__)

if is_tf_available():
  import tensorflow as tf
@dataclass
class 							lowerCamelCase						(       lowerCamelCase_ ):
   lowercase				:     Optional[Any]            = '''tf'''
   lowercase				:     Optional[str]            = field(
       default=lowerCamelCase_		,     metadata={'help': 'Name of TPU'}		,     )

   lowercase				:     Optional[str]            = field(
       default=lowerCamelCase_		,     metadata={'help': 'Zone of TPU'}		,     )

   lowercase				:     Optional[str]            = field(
       default=lowerCamelCase_		,     metadata={'help': 'Name of Cloud TPU-enabled project'}		,     )

   lowercase				:     float            = field(
       default=1.0		,     metadata={'help': 'Power for the Polynomial decay LR scheduler.'}		,     )

   lowercase				:     bool            = field(default=lowerCamelCase_		,     metadata={'help': 'Whether to activate the XLA compilation or not'} )







   @cached_property
   def 					a_							(							self       ):
       requires_backends(self			,					["""tf"""]       )
       logger.info("""Tensorflow: setting up strategy"""       )

       UpperCamelCase :      Dict      				=							tf.config.list_physical_devices("""GPU"""       )

       # Set to float16 at first
       if self.fpaa:
           tf.keras.mixed_precision.set_global_policy("""mixed_float16"""       )

       if self.no_cuda:
           UpperCamelCase :      Union[str, Any]      				=							tf.distribute.OneDeviceStrategy(device="""/cpu:0"""       )
       else:
           try:
               if self.tpu_name:
                   UpperCamelCase :      List[str]      				=							tf.distribute.cluster_resolver.TPUClusterResolver(
                       self.tpu_name			,					zone=self.tpu_zone			,					project=self.gcp_project       )
               else:
                   UpperCamelCase :      Optional[Any]      				=							tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               if self.tpu_name:
                   raise RuntimeError(f'Couldn\'t connect to TPU {self.tpu_name}!'       )
               else:
                   UpperCamelCase :      str      				=							None

           if tpu:
               # Set to bfloat16 in case of TPU
               if self.fpaa:
                   tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""       )

               tf.config.experimental_connect_to_cluster(snake_case_       )
               tf.tpu.experimental.initialize_tpu_system(snake_case_       )

               UpperCamelCase :      List[str]      				=							tf.distribute.TPUStrategy(snake_case_       )

           elif len(snake_case_       ) == 0:
               UpperCamelCase :      List[Any]      				=							tf.distribute.OneDeviceStrategy(device="""/cpu:0"""       )
           elif len(snake_case_       ) == 1:
               UpperCamelCase :      Any      				=							tf.distribute.OneDeviceStrategy(device="""/gpu:0"""       )
           elif len(snake_case_       ) > 1:
               # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
               UpperCamelCase :      int      				=							tf.distribute.MirroredStrategy()
           else:
               raise ValueError("""Cannot find the proper strategy, please check your environment properties."""       )

       return strategy







   @property
   def 					a_							(							self       ):
       requires_backends(self			,					["""tf"""]       )
       return self._setup_strategy







   @property
   def 					a_							(							self       ):
       requires_backends(self			,					["""tf"""]       )
       return self._setup_strategy.num_replicas_in_sync







   @property
   def 					a_							(							self       ):
       return False  # TF Logging is handled by Keras not the Trainer







   @property
   def 					a_							(							self       ):

       if self.per_gpu_train_batch_size:
           logger.warning(
               """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
               """version. Using `--per_device_train_batch_size` is preferred."""       )
       UpperCamelCase :      Dict      				=							self.per_gpu_train_batch_size or self.per_device_train_batch_size
       return per_device_batch_size * self.n_replicas







   @property
   def 					a_							(							self       ):

       if self.per_gpu_eval_batch_size:
           logger.warning(
               """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
               """version. Using `--per_device_eval_batch_size` is preferred."""       )
       UpperCamelCase :      Union[str, Any]      				=							self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
       return per_device_batch_size * self.n_replicas







   @property
   def 					a_							(							self       ):
       requires_backends(self			,					["""tf"""]       )
       warnings.warn(
           """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""			,					snake_case_			,					)
       return self._setup_strategy.num_replicas_in_sync





'''simple docstring'''


from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize






class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						    =		1
  lowerCamelCase_		:List[Any]						    =		2
  lowerCamelCase_		:Optional[Any]						    =		3


  def __repr__( self						):




       '''simple docstring'''

       return F'''{self.__class__.__name__}.{self.name}'''







def 					_lowerCamelCase    (      lowerCamelCase_					:							tuple[int, int]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   Any   =      point
     return degrees(atana(y - miny	,     x - minx   )   )


def 					_lowerCamelCase    (      lowerCamelCase_					:							tuple[int, int]	,     lowerCamelCase_					:							tuple[int, int]	,     lowerCamelCase_					:							tuple[int, int]   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      starting
     UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      via
     UpperCAmelCase_   ,			UpperCAmelCase_							:   Dict   =      target
     UpperCAmelCase_							:   Optional[Any]   =      degrees(atana(ya - ya	,     xa - xa   )   )
     via_angle %= 360
     UpperCAmelCase_							:   Optional[Any]   =      degrees(atana(ya - ya	,     xa - xa   )   )
     target_angle %= 360
     # t-
     #  \ \
     #   \ v
     #    \|
     #     s
     # via_angle is always lower than target_angle, if direction is left.
     # If they are same, it means they are on a same line of convex hull.
     if target_angle > via_angle:
          return Direction.left
     elif target_angle == via_angle:
          return Direction.straight
     else:
          return Direction.right





def 					_lowerCamelCase    (      lowerCamelCase_					:							list[tuple[int, int]]   ):



     """simple docstring"""





     if len(lowerCamelCase_   ) <= 2:
          # There is no convex hull
          raise ValueError('graham_scan: argument must contain more than 3 points.'   )
     if len(lowerCamelCase_   ) == 3:
          return points
     # find the lowest and the most left point
     UpperCAmelCase_							:   Dict   =      0
     UpperCAmelCase_   ,			UpperCAmelCase_							:   Union[str, Any]   =      maxsize, maxsize
     for i, point in enumerate(lowerCamelCase_   ):
          UpperCAmelCase_							:   Union[str, Any]   =      point[0]
          UpperCAmelCase_							:   List[Any]   =      point[1]
          if y < miny:
               UpperCAmelCase_							:   Dict   =      y
               UpperCAmelCase_							:   Optional[int]   =      x
               UpperCAmelCase_							:   Optional[Any]   =      i
          if y == miny and x < minx:
               UpperCAmelCase_							:   Union[str, Any]   =      x
               UpperCAmelCase_							:   Dict   =      i

    # remove the lowest and the most left point from points for preparing for sort
     points.pop(lowerCamelCase_   )

     UpperCAmelCase_							:   List[str]   =      sorted(lowerCamelCase_	,     key=lambda lowerCamelCase_   : angle_comparer(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )   )
     # This insert actually costs complexity,
     # and you should instead add (minx, miny) into stack later.
     # I'm using insert just for easy understanding.
     sorted_points.insert(0	,     (minx, miny)   )

     UpperCAmelCase_							:   deque[tuple[int, int]]   =      deque()
     stack.append(sorted_points[0]   )
     stack.append(sorted_points[1]   )
     stack.append(sorted_points[2]   )
     # In any ways, the first 3 points line are towards left.
     # Because we sort them the angle from minx, miny.
     UpperCAmelCase_							:   List[Any]   =      Direction.left

     for i in range(3	,     len(lowerCamelCase_   )   ):
          while True:
               UpperCAmelCase_							:   Optional[Any]   =      stack[-2]
               UpperCAmelCase_							:   Optional[int]   =      stack[-1]
               UpperCAmelCase_							:   str   =      sorted_points[i]
               UpperCAmelCase_							:   Union[str, Any]   =      check_direction(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

               if next_direction == Direction.left:
                    UpperCAmelCase_							:   Union[str, Any]   =      Direction.left
                    break
               if next_direction == Direction.straight:
                    if current_direction == Direction.left:
                         # We keep current_direction as left.
                         # Because if the straight line keeps as straight,
                         # we want to know if this straight line is towards left.
                         break
                    elif current_direction == Direction.right:
                         # If the straight line is towards right,
                         # every previous points on those straigh line is not convex hull.
                         stack.pop()
               if next_direction == Direction.right:
                    stack.pop()
          stack.append(sorted_points[i]   )
     return list(lowerCamelCase_   )






