




"""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


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

if is_tf_available():
  import tensorflow as tf

@dataclass
class 							snake_case_ (				a_ ):







   """simple docstring"""




   A_										=					'tf'
   A_										=					field(
       default=a_  ,   metadata={'''help''': '''Name of TPU'''}  ,   )

   A_										=					field(
       default=a_  ,   metadata={'''help''': '''Zone of TPU'''}  ,   )

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

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

   A_										=					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''')

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

               UpperCamelCase										=  tf.distribute.TPUStrategy(_snake_case)

           elif len(_snake_case) == 0:
               UpperCamelCase										=  tf.distribute.OneDeviceStrategy(device='''/cpu:0''')
           elif len(_snake_case) == 1:
               UpperCamelCase										=  tf.distribute.OneDeviceStrategy(device='''/gpu:0''')
           elif len(_snake_case) > 1:
               # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
               UpperCamelCase										=  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)							->      int:
       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.''')
       UpperCamelCase										=  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.''')
       UpperCamelCase										=  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.'''					,				_snake_case					,				)
       return self._setup_strategy.num_replicas_in_sync





"""simple docstring"""







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_      =					ascii_letters + ' \t\n'





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  os.path.split(os.path.realpath(_lowercase						)						)
    UpperCamelCase										=  {}
    with open(path[0] + '''/dictionary.txt'''						) as dictionary_file:
        for word in dictionary_file.read().split('''\n'''						):
            UpperCamelCase										=  None
    return english_words


SCREAMING_SNAKE_CASE_      =					load_dictionary()





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  message.upper()
    UpperCamelCase										=  remove_non_letters(_lowercase						)
    UpperCamelCase										=  message.split()
    UpperCamelCase										=  len([word for word in possible_words if word in ENGLISH_WORDS]						)
    return float(_lowercase						) / len(_lowercase						)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE						)





def     __snake_case   (							_lowercase ,_lowercase = 20 ,_lowercase = 85						):


    """simple docstring"""







    UpperCamelCase										=  get_english_count(_lowercase						) * 100 >= word_percentage
    UpperCamelCase										=  len(remove_non_letters(_lowercase						)						)
    UpperCamelCase										=  (float(_lowercase						) / len(_lowercase						)) * 100
    UpperCamelCase										=  message_letters_percentage >= letter_percentage
    return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()