

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_    						=				logging.get_logger(__name__)

if is_tf_available():
       import tensorflow as tf


@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: str														=  """tf"""
    __lowerCamelCase		: Optional[str]														=  field(
        default=__snake_case	, metadata={"""help""": """Name of TPU"""}	, )

    __lowerCamelCase		: Optional[str]														=  field(
        default=__snake_case	, metadata={"""help""": """Zone of TPU"""}	, )

    __lowerCamelCase		: Optional[str]														=  field(
        default=__snake_case	, 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=__snake_case	, metadata={"""help""": """Whether to activate the XLA compilation or not"""}  )





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

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

                   lowercase__					:     Any   = tf.distribute.TPUStrategy(a)

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

         return strategy





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





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





    @property
    def    snake_case_       (   self):
         return False  # TF Logging is handled by Keras not the Trainer





    @property
    def    snake_case_       (   self):
         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__					:     List[Any]   = self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas





    @property
    def    snake_case_       (   self):
         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__					:     Optional[Any]   = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas





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




from cva import destroyAllWindows, imread, imshow, waitKey

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]

     def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
          return min(SCREAMING_SNAKE_CASE_			,  255    )

     for i in range(SCREAMING_SNAKE_CASE_    ):
          for j in range(SCREAMING_SNAKE_CASE_    ):
               lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
               lowercase__					:     Dict   = [
                   normalize(SCREAMING_SNAKE_CASE_    ),
                   normalize(greyscale + factor    ),
                   normalize(greyscale + 2 * factor    ),
               ]

     return img


if __name__ == "__main__":
       # read original image
       snake_case_    						=				{
           percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'''Original image with sepia (factor: {percentage})''', img)

       waitKey(0)
       destroyAllWindows()

