




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__    ):
     _lowerCAmelCase		       =						"tf"
     _lowerCAmelCase		       =						field(
         default=a__	,		metadata={"help": "Name of TPU"}	,		)

     _lowerCAmelCase		       =						field(
         default=a__	,		metadata={"help": "Zone of TPU"}	,		)

     _lowerCAmelCase		       =						field(
         default=a__	,		metadata={"help": "Name of Cloud TPU-enabled project"}	,		)

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

     _lowerCAmelCase		       =						field(default=a__	,		metadata={"help": "Whether to activate the XLA compilation or not"}    )



     @cached_property
     def 				__magic_name__		(  self   :							Dict       ):
       requires_backends(self      ,		['''tf''']       )
       logger.info('''Tensorflow: setting up strategy'''       )

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

           a_		    =  tf.distribute.TPUStrategy(lowercase__       )

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

       return strategy



     @property
     def 				__magic_name__		(  self   :							Dict       ):
       requires_backends(self      ,		['''tf''']       )
       return self._setup_strategy



     @property
     def 				__magic_name__		(  self   :							List[str]       ):
       requires_backends(self      ,		['''tf''']       )
       return self._setup_strategy.num_replicas_in_sync



     @property
     def 				__magic_name__		(  self   :							Optional[Any]       ):
       return False  # TF Logging is handled by Keras not the Trainer



     @property
     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       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.'''       )
       a_		    =  self.per_gpu_train_batch_size or self.per_device_train_batch_size
       return per_device_batch_size * self.n_replicas



     @property
     def 				__magic_name__		(  self   :							str       ):
       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.'''       )
       a_		    =  self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
       return per_device_batch_size * self.n_replicas



     @property
     def 				__magic_name__		(  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.'''      ,		lowercase__      ,		)
       return self._setup_strategy.num_replicas_in_sync






from __future__ import annotations

from typing import Any






def 				UpperCAmelCase__  (							*_A			):






  """simple docstring"""


  if all(_check_not_integer(_A			) for m in matrix_s			):
    for i in matrix_s[1:]:
      _verify_matrix_sizes(matrix_s[0]      ,      _A			)
    return [[sum(_A			) for t in zip(*_A			)] for m in zip(*_A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if (
      _check_not_integer(_A			)
      and _check_not_integer(_A			)
      and _verify_matrix_sizes(_A      ,      _A			)
  ):
    return [[i - j for i, j in zip(*_A			)] for m in zip(_A      ,      _A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  return [[x * n for x in row] for row in matrix]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if _check_not_integer(_A			) and _check_not_integer(_A			):
    a_    , a_		    =  _verify_matrix_sizes(_A      ,      _A			)

  if cols[0] != rows[1]:
    a_		    =  (
        '''Cannot multiply matrix of dimensions '''
        f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
    )
    raise ValueError(_A			)
  return [
      [sum(m * n for m, n in zip(_A      ,      _A			)			) for j in zip(*_A			)] for i in matrix_a
  ]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  int(_A			)
  return [[int(row == column			) for column in range(_A			)] for row in range(_A			)]






def 				UpperCAmelCase__  (							_A      ,      _A = True			):






  """simple docstring"""


  if _check_not_integer(_A			):
    if return_map:
      return map(_A      ,      zip(*_A			)			)
    else:
      return list(map(_A      ,      zip(*_A			)			)			)
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  matrix[:row] + matrix[row + 1 :]
  return [row[:column] + row[column + 1 :] for row in minor]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if len(_A			) == 1:
    return matrix[0][0]

  return sum(
      x * determinant(minor(_A      ,      0      ,      _A			)			) * (-1) ** i
      for i, x in enumerate(matrix[0]			)			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  determinant(_A			)
  if det == 0:
    return None

  a_		    =  [
      [determinant(minor(_A      ,      _A      ,      _A			)			) for j in range(len(_A			)			)]
      for i in range(len(_A			)			)
  ]

  a_		    =  [
      [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
      for row in range(len(_A			)			)
  ]
  a_		    =  list(transpose(_A			)			)
  return scalar_multiply(_A      ,      1 / det			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return not isinstance(_A      ,      _A			) and not isinstance(matrix[0]      ,      _A			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return len(_A			), len(matrix[0]			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  _shape(_A			) + _shape(_A			)
  if shape[0] != shape[3] or shape[1] != shape[2]:
    a_		    =  (
        '''operands could not be broadcast together with shape '''
        f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
    )
    raise ValueError(_A			)
  return (shape[0], shape[2]), (shape[1], shape[3])






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  [[12, 10], [3, 9]]
  a_		    =  [[3, 4], [7, 4]]
  a_		    =  [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
  a_		    =  [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  print(f"Add Operation, {add(_A      ,      _A			) = } \n"			)
  print(
      f"Multiply Operation, {multiply(_A      ,      _A			) = } \n"      ,      )
  print(f"Identity: {identity(5			)}\n"			)
  print(f"Minor of {matrix_c} = {minor(_A      ,      1      ,      2			)} \n"			)
  print(f"Determinant of {matrix_b} = {determinant(_A			)} \n"			)
  print(f"Inverse of {matrix_d} = {inverse(_A			)}\n"			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()
