code: 
  
  
  """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
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  if is_tf_available():
   import tensorflow as tf
  
  
  
  
  @dataclass
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				'''tf'''
     __UpperCAmelCase       					=				field(
         default=A_   ,metadata={'''help''': '''Name of TPU'''}   ,)
  
     __UpperCAmelCase       					=				field(
         default=A_   ,metadata={'''help''': '''Zone of TPU'''}   ,)
  
     __UpperCAmelCase       					=				field(
         default=A_   ,metadata={'''help''': '''Name of Cloud TPU-enabled project'''}   ,)
  
     __UpperCAmelCase       					=				field(
         default=1.0   ,metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}   ,)
  
     __UpperCAmelCase       					=				field(default=A_   ,metadata={'''help''': '''Whether to activate the XLA compilation or not'''}							)
  
  
  
  
  
     @cached_property
     def 		UpperCamelCase_     (       self)     ->     Tuple["tf.distribute.Strategy", int]:
        requires_backends(self			,      ["""tf"""])
        logger.info("""Tensorflow: setting up strategy""")
  
        _lowerCamelCase  :				Any      =		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:
           _lowerCamelCase  :				Dict      =		tf.distribute.OneDeviceStrategy(device="""/cpu:0""")
        else:
           try:
              if self.tpu_name:
                 _lowerCamelCase  :				Optional[Any]      =		tf.distribute.cluster_resolver.TPUClusterResolver(
                     self.tpu_name			,      zone=self.tpu_zone			,      project=self.gcp_project)
              else:
                 _lowerCamelCase  :				Dict      =		tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
              if self.tpu_name:
                 raise RuntimeError(F'Couldn\'t connect to TPU {self.tpu_name}!')
              else:
                 _lowerCamelCase  :				Any      =		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(SCREAMING_SNAKE_CASE)
              tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE)
  
              _lowerCamelCase  :				Union[str, Any]      =		tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE)
  
           elif len(SCREAMING_SNAKE_CASE) == 0:
              _lowerCamelCase  :				Optional[Any]      =		tf.distribute.OneDeviceStrategy(device="""/cpu:0""")
           elif len(SCREAMING_SNAKE_CASE) == 1:
              _lowerCamelCase  :				Dict      =		tf.distribute.OneDeviceStrategy(device="""/gpu:0""")
           elif len(SCREAMING_SNAKE_CASE) > 1:
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              _lowerCamelCase  :				Optional[Any]      =		tf.distribute.MirroredStrategy()
           else:
              raise ValueError("""Cannot find the proper strategy, please check your environment properties.""")
  
        return strategy
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     "tf.distribute.Strategy":
        requires_backends(self			,      ["""tf"""])
        return self._setup_strategy
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     int:
        requires_backends(self			,      ["""tf"""])
        return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       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.""")
        _lowerCamelCase  :				Union[str, Any]      =		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)     ->     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.""")
        _lowerCamelCase  :				Optional[int]      =		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)     ->     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."""			,      SCREAMING_SNAKE_CASE			,      )
        return self._setup_strategy.num_replicas_in_sync
  
  
style_context: 
  
  
  """simple docstring"""
  def 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			str	,   __snake_case       :			int	,   __snake_case       :			int	,   __snake_case       :			int	,   __snake_case       :			int	,   ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[Any]      =		list(__snake_case      )
     _lowerCamelCase  :				Optional[int]      =		list(__snake_case      )
     _lowerCamelCase  :				Union[str, Any]      =		len(__snake_case      )
     _lowerCamelCase  :				Optional[int]      =		len(__snake_case      )
  
     _lowerCamelCase  :				List[str]      =		[
         [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
     _lowerCamelCase  :				List[Any]      =		[
         ["""0""" for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
  
     for i in range(1	,   len_source_seq + 1      ):
        _lowerCamelCase  :				Optional[Any]      =		i * delete_cost
        _lowerCamelCase  :				Any      =		F'D{source_seq[i - 1]:c}'
  
     for i in range(1	,   len_destination_seq + 1      ):
        _lowerCamelCase  :				Dict      =		i * insert_cost
        _lowerCamelCase  :				List[str]      =		F'I{destination_seq[i - 1]:c}'
  
     for i in range(1	,   len_source_seq + 1      ):
        for j in range(1	,   len_destination_seq + 1      ):
           if source_seq[i - 1] == destination_seq[j - 1]:
              _lowerCamelCase  :				Union[str, Any]      =		costs[i - 1][j - 1] + copy_cost
              _lowerCamelCase  :				Tuple      =		F'C{source_seq[i - 1]:c}'
           else:
              _lowerCamelCase  :				Tuple      =		costs[i - 1][j - 1] + replace_cost
              _lowerCamelCase  :				List[str]      =		F'R{source_seq[i - 1]:c}' + str(destination_seq[j - 1]      )
  
           if costs[i - 1][j] + delete_cost < costs[i][j]:
              _lowerCamelCase  :				Any      =		costs[i - 1][j] + delete_cost
              _lowerCamelCase  :				Any      =		F'D{source_seq[i - 1]:c}'
  
           if costs[i][j - 1] + insert_cost < costs[i][j]:
              _lowerCamelCase  :				Dict      =		costs[i][j - 1] + insert_cost
              _lowerCamelCase  :				Union[str, Any]      =		F'I{destination_seq[j - 1]:c}'
  
     return costs, ops
  
  def 							_snake_case			(      __snake_case       :			list[list[str]]	,   __snake_case       :			int	,   __snake_case       :			int      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if i == 0 and j == 0:
        return []
     else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
           _lowerCamelCase  :				List[Any]      =		assemble_transformation(__snake_case	,   i - 1	,   j - 1      )
           seq.append(ops[i][j]      )
           return seq
        elif ops[i][j][0] == "D":
           _lowerCamelCase  :				Optional[Any]      =		assemble_transformation(__snake_case	,   i - 1	,   __snake_case      )
           seq.append(ops[i][j]      )
           return seq
        else:
           _lowerCamelCase  :				str      =		assemble_transformation(__snake_case	,   __snake_case	,   j - 1      )
           seq.append(ops[i][j]      )
           return seq
  
  
  if __name__ == "__main__":
   UpperCAmelCase ,     UpperCAmelCase   =	compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
   UpperCAmelCase   =	len(operations)
   UpperCAmelCase   =	len(operations[0])
   UpperCAmelCase   =	assemble_transformation(operations, m - 1, n - 1)
  
   UpperCAmelCase   =	list("""Python""")
   UpperCAmelCase   =	0
   UpperCAmelCase   =	0
  
   with open("""min_cost.txt""", """w""") as file:
    for op in sequence:
     print("""""".join(string))
  
     if op[0] == "C":
      file.write("""%-16s""" % """Copy %c""" % op[1])
      file.write("""\t\t\t""" + """""".join(string))
      file.write("""\r\n""")
  
      cost -= 1
     elif op[0] == "R":
      UpperCAmelCase   =	op[2]
  
      file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
      file.write("""\t\t""" + """""".join(string))
      file.write("""\r\n""")
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write("""%-16s""" % """Delete %c""" % op[1])
      file.write("""\t\t\t""" + """""".join(string))
      file.write("""\r\n""")
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write("""%-16s""" % """Insert %c""" % op[1])
      file.write("""\t\t\t""" + """""".join(string))
      file.write("""\r\n""")
  
      cost += 2
  
     i += 1
  
    print("""""".join(string))
    print("""Cost: """, cost)
  
    file.write("""\r\nMinimum cost: """ + str(cost))
  
  