



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 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  List[Any],   __lowercase   :  Any,   __lowercase   :  Any,   __lowercase   :  Any  ):
							self.assertEqual(len(__lowercase  ),   len(__lowercase  )  )
							for a, b in zip(__lowercase,   __lowercase  ):
										self.assertAlmostEqual(__lowercase,   __lowercase,   delta=__lowercase  )




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			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(__lowercase  ):
										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 							A__ (  self   :  Any  ):
							lowercase__   					=			None
							ops.enable_eager_execution_internal()
							lowercase__   					=			tf.config.list_physical_devices("CPU"  )
							if len(__lowercase  ) == 1:
										tf.config.set_logical_device_configuration(
										    physical_devices[0],   [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]  )
							lowercase__   					=			tf.config.list_logical_devices(device_type="CPU"  )
							lowercase__   					=			tf.distribute.MirroredStrategy(devices=devices[:2]  )

							with strategy.scope():
										lowercase__   					=			GradientAccumulator()
										lowercase__   					=			tf.Variable([4.0, 3.0]  )
										lowercase__					,    lowercase__   					=			create_optimizer(5e-5,   10,   5  )
										lowercase__   					=			tf.Variable([0.0, 0.0],   trainable=__lowercase  )

							def accumulate_on_replica(__lowercase   :  Optional[Any]  ):
										accumulator([gradient]  )

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

							@tf.function
							def accumulate(__lowercase   :  int,   __lowercase   :  Dict  ):
										with strategy.scope():
													lowercase__   					=			strategy.experimental_local_results(__lowercase  )
													local_variables[0].assign(__lowercase  )
													local_variables[1].assign(__lowercase  )
													strategy.run(__lowercase,   args=(gradient_placeholder,)  )

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

							def _check_local_values(__lowercase   :  int,   __lowercase   :  str  ):
										lowercase__   					=			strategy.experimental_local_results(accumulator._gradients[0]  )
										self.assertListAlmostEqual(values[0].value(),   __lowercase,   tol=1e-2  )
										self.assertListAlmostEqual(values[1].value(),   __lowercase,   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]  )









from __future__ import annotations

from typing import TypedDict
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str
				UpperCamelCase__			:   int



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)

			return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE_					)					)]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)
			if not s:
						raise ValueError("The parameter s must not be empty."					)

			lowercase__   					=			all_rotations(SCREAMING_SNAKE_CASE_					)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			lowercase__   					=			{
			    "bwt_string": "".join([word[-1] for word in rotations]					),
			    "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE_					),
			}
			return response



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter bwt_string type must be str."					)
			if not bwt_string:
						raise ValueError("The parameter bwt_string must not be empty."					)
			try:
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			except ValueError:
						raise TypeError(
						    "The parameter idx_original_string type must be int or passive"
						    " of cast to int."					)
			if idx_original_string < 0:
						raise ValueError("The parameter idx_original_string must not be lower than 0."					)
			if idx_original_string >= len(SCREAMING_SNAKE_CASE_					):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

			lowercase__   					=			[""] * len(SCREAMING_SNAKE_CASE_					)
			for _ in range(len(SCREAMING_SNAKE_CASE_					)					):
						for i in range(len(SCREAMING_SNAKE_CASE_					)					):
									lowercase__   					=			bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							lowercase_						=       """Provide a string that I will generate its BWT transform: """
							lowercase_						=       input(entry_msg).strip()
							lowercase_						=       bwt_transform(s)
							print(
							    F'Burrows Wheeler transform for string \'{s}\' results '
							    F'in \'{result["bwt_string"]}\''
							)
							lowercase_						=       reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
							print(
							    F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
							    F'we get original string \'{original_string}\''
							)




