


"""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





"""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))

