'''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


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

if is_tf_available():
 import tensorflow as tf






@dataclass
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  lowerCamelCase_		:Optional[Any]						    =		'''tf'''
  lowerCamelCase_		:Optional[str]						    =		field(
      default=lowerCamelCase_						,	metadata={'''help''': '''Name of TPU'''}						,	)

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

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

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

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


  @cached_property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       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 _UpperCamelCase     ( self						):




       '''simple docstring'''

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


  @property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       requires_backends(self      ,		['tf']						)
       return self._setup_strategy.num_replicas_in_sync


  @property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       return False  # TF Logging is handled by Keras not the Trainer


  @property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       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_   )






