


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


UpperCAmelCase__            =							logging.get_logger(__name__)

if is_tf_available():
  import tensorflow as tf







@dataclass
class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''tf'''
     SCREAMING_SNAKE_CASE__				      =      field(
         default=lowerCAmelCase       ,							metadata={'''help''': '''Name of TPU'''}       ,							)

     SCREAMING_SNAKE_CASE__				      =      field(
         default=lowerCAmelCase       ,							metadata={'''help''': '''Zone of TPU'''}       ,							)

     SCREAMING_SNAKE_CASE__				      =      field(
         default=lowerCAmelCase       ,							metadata={'''help''': '''Name of Cloud TPU-enabled project'''}       ,							)

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

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





     @cached_property
     def 					__A (self	)					->       Tuple["tf.distribute.Strategy", int]:
      requires_backends(self		,					['''tf''']	)
      logger.info('''Tensorflow: setting up strategy'''	)

      _lowercase											=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:
       _lowercase											=tf.distribute.OneDeviceStrategy(device='''/cpu:0'''	)
      else:
       try:
        if self.tpu_name:
         _lowercase											=tf.distribute.cluster_resolver.TPUClusterResolver(
             self.tpu_name		,					zone=self.tpu_zone		,					project=self.gcp_project	)
        else:
         _lowercase											=tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
        if self.tpu_name:
         raise RuntimeError(f"Couldn't connect to TPU {self.tpu_name}!"	)
        else:
         _lowercase											=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(UpperCAmelCase	)
        tf.tpu.experimental.initialize_tpu_system(UpperCAmelCase	)

        _lowercase											=tf.distribute.TPUStrategy(UpperCAmelCase	)

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

      return strategy





     @property
     def 					__A (self	)					->       "tf.distribute.Strategy":
      requires_backends(self		,					['''tf''']	)
      return self._setup_strategy





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





     @property
     def 					__A (self	)					->       Union[str, Any]:
      return False  # TF Logging is handled by Keras not the Trainer





     @property
     def 					__A (self	)					->       int:
      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.'''	)
      _lowercase											=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	)					->       int:
      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.'''	)
      _lowercase											=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	)					->       int:
      requires_backends(self		,					['''tf''']	)
      warnings.warn(
          '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'''		,					UpperCAmelCase		,					)
      return self._setup_strategy.num_replicas_in_sync





UpperCAmelCase__            =							[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      bool:







 """simple docstring"""


 return 0 <= row < rows and 0 <= col < cols
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											=len(__snake_case						)
 _lowercase											=len(mat[0]						)
 if is_safe(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						) and (row, col) not in seen and mat[row][col] == 1:
  seen.add((row, col)						)
  return (
      1
      + depth_first_search(row + 1					,						__snake_case					,						__snake_case					,						__snake_case						)
      + depth_first_search(row - 1					,						__snake_case					,						__snake_case					,						__snake_case						)
      + depth_first_search(__snake_case					,						col + 1					,						__snake_case					,						__snake_case						)
      + depth_first_search(__snake_case					,						col - 1					,						__snake_case					,						__snake_case						)
  )
 else:
  return 0
def     UpperCAmelCase_ (							__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											=set()

 _lowercase											=0
 for row, line in enumerate(__snake_case						):
  for col, item in enumerate(__snake_case						):
   if item == 1 and (row, col) not in seen:
    # Maximizing the area
    _lowercase											=max(__snake_case					,						depth_first_search(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)						)
 return max_area


if __name__ == "__main__":
  import doctest

  print(find_max_area(matrix))  # Output -> 6
  doctest.testmod()

