




"""simple docstring"""


import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer


@require_tf
class _lowerCamelCase    (					unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Tuple   )			->						Tuple:

         """simple docstring"""
         self.assertEqual(len(UpperCamelCase   )   ,     len(UpperCamelCase   )   )
         for a, b in zip(UpperCamelCase   ,     UpperCamelCase   ):
               self.assertAlmostEqual(UpperCamelCase   ,     UpperCamelCase   ,     delta=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	GradientAccumulator()
         accumulator([tf.constant([1.0, 2.0]   )]   )
         accumulator([tf.constant([-2.0, 1.0]   )]   )
         accumulator([tf.constant([-1.0, 2.0]   )]   )
         with self.assertRaises(UpperCamelCase   ):
               accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
         self.assertEqual(accumulator.step   ,     3   )
         self.assertEqual(len(accumulator.gradients   )   ,     1   )
         self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()   ,     [-2.0, 5.0]   ,     tol=1E-2   )
         accumulator.reset()
         self.assertEqual(accumulator.step   ,     0   )
         self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()   ,     [0.0, 0.0]   ,     tol=1E-2   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	None
         ops.enable_eager_execution_internal()
         lowerCAmelCase__      :			Union[str, Any]   =	tf.config.list_physical_devices("""CPU"""   )
         if len(UpperCamelCase   ) == 1:
               tf.config.set_logical_device_configuration(
                   physical_devices[0]   ,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
         lowerCAmelCase__      :			Dict   =	tf.config.list_logical_devices(device_type="""CPU"""   )
         lowerCAmelCase__      :			Optional[Any]   =	tf.distribute.MirroredStrategy(devices=devices[:2]   )

         with strategy.scope():
               lowerCAmelCase__      :			List[Any]   =	GradientAccumulator()
               lowerCAmelCase__      :			List[str]   =	tf.Variable([4.0, 3.0]   )
               lowerCAmelCase__      :			Optional[Any]   =	create_optimizer(5E-5   ,     10   ,     5   )
               lowerCAmelCase__      :			List[Any]   =	tf.Variable([0.0, 0.0]   ,     trainable=UpperCamelCase   )

         def accumulate_on_replica(UpperCamelCase       :		Optional[int]   ):
               accumulator([gradient]   )

         def apply_on_replica():
               optimizer.apply_gradients(list(zip(accumulator.gradients   ,     [variable]   )   )   )

         @tf.function
         def accumulate(UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Tuple   ):
               with strategy.scope():
                     lowerCAmelCase__      :			str   =	strategy.experimental_local_results(UpperCamelCase   )
                     local_variables[0].assign(UpperCamelCase   )
                     local_variables[1].assign(UpperCamelCase   )
                     strategy.run(UpperCamelCase   ,     args=(gradient_placeholder,)   )

         @tf.function
         def apply_grad():
               with strategy.scope():
                     strategy.run(UpperCamelCase   )

         def _check_local_values(UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		str   ):
               lowerCAmelCase__      :			List[Any]   =	strategy.experimental_local_results(accumulator._gradients[0]   )
               self.assertListAlmostEqual(values[0].value()   ,     UpperCamelCase   ,     tol=1E-2   )
               self.assertListAlmostEqual(values[1].value()   ,     UpperCamelCase   ,     tol=1E-2   )

         accumulate([1.0, 2.0]   ,     [-1.0, 1.0]   )
         accumulate([3.0, -1.0]   ,     [-1.0, -1.0]   )
         accumulate([-2.0, 2.0]   ,     [3.0, -2.0]   )
         self.assertEqual(accumulator.step   ,     3   )
         _check_local_values([2.0, 3.0]   ,     [1.0, -2.0]   )
         apply_grad()
         self.assertListAlmostEqual(variable.value()   ,     [4.0, 3.0]   ,     tol=1E-2   )
         accumulator.reset()
         self.assertEqual(accumulator.step   ,     0   )
         _check_local_values([0.0, 0.0]   ,     [0.0, 0.0]   )











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

    # print entropy
      print(f"""{round(-1 * my_fir_sum				):.1f}"""				)

      # two len string
      lowerCAmelCase__      :			Tuple   =	sum(two_char_strings.values()				)
      lowerCAmelCase__      :			str   =	0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  lowerCAmelCase__      :			Optional[int]   =	cha + cha
                  if sequence in two_char_strings:
                        lowerCAmelCase__      :			int   =	two_char_strings[sequence]
                        lowerCAmelCase__      :			str   =	int(__UpperCAmelCase				) / all_sum
                        my_sec_sum += prob * math.loga(__UpperCAmelCase				)

    # print second entropy
      print(f"""{round(-1 * my_sec_sum				):.1f}"""				)

      # print the difference between them
      print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)				):.1f}"""				)



def 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0    ,		len(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    Any:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
  main()





