



import itertools
import string
from collections.abc import Generator, Iterable




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						iter(_lowerCamelCase    )
			while True:
						A							:					Union[str, Any]              =						tuple(itertools.islice(_lowerCamelCase ,    _lowerCamelCase    )    )
						if not chunk:
									return
						yield chunk




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					str              =						"".join([c.upper() for c in dirty if c in string.ascii_letters]    )
			A							:					int              =						""

			if len(_lowerCamelCase    ) < 2:
						return dirty

			for i in range(len(_lowerCamelCase    ) - 1    ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(_lowerCamelCase    ) & 1:
						clean += "X"

			return clean




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			# I and J are used interchangeably to allow
			# us to use a 5x5 table (25 letters)
			A							:					Optional[Any]              =						"ABCDEFGHIKLMNOPQRSTUVWXYZ"
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			A							:					List[Any]              =						[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(_lowerCamelCase    )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(_lowerCamelCase    )

			return table




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						generate_table(_lowerCamelCase    )
			A							:					str              =						prepare_input(_lowerCamelCase    )
			A							:					List[Any]              =						""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(_lowerCamelCase ,    2    ):
						A       , A							:					List[Any]              =						divmod(table.index(_lowerCamelCase    ) ,    5    )
						A       , A							:					List[str]              =						divmod(table.index(_lowerCamelCase    ) ,    5    )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[str]              =						generate_table(_lowerCamelCase    )
			A							:					Tuple              =						""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(_lowerCamelCase ,    2    ):
						A       , A							:					Tuple              =						divmod(table.index(_lowerCamelCase    ) ,    5    )
						A       , A							:					Optional[Any]              =						divmod(table.index(_lowerCamelCase    ) ,    5    )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext




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						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Optional[int]:
									self.assertEqual(len(__lowerCamelCase )	,	len(__lowerCamelCase ) )
									for a, b in zip(__lowerCamelCase	,	__lowerCamelCase ):
												self.assertAlmostEqual(__lowerCamelCase	,	__lowerCamelCase	,	delta=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									A							:					List[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(__lowerCamelCase ):
												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 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Union[str, Any]              =						None
									ops.enable_eager_execution_internal()
									A							:					Tuple              =						tf.config.list_physical_devices("CPU" )
									if len(__lowerCamelCase ) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]	,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
									A							:					Dict              =						tf.config.list_logical_devices(device_type="CPU" )
									A							:					List[str]              =						tf.distribute.MirroredStrategy(devices=devices[:2] )

									with strategy.scope():
												A							:					Optional[int]              =						GradientAccumulator()
												A							:					Tuple              =						tf.Variable([4.0, 3.0] )
												A       , A							:					List[Any]              =						create_optimizer(5e-5	,	10	,	5 )
												A							:					List[str]              =						tf.Variable([0.0, 0.0]	,	trainable=__lowerCamelCase )

									def accumulate_on_replica(__lowerCamelCase      :			Tuple ):
												accumulator([gradient] )

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

									@tf.function
									def accumulate(__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[int] ):
												with strategy.scope():
															A							:					int              =						strategy.experimental_local_results(__lowerCamelCase )
															local_variables[0].assign(__lowerCamelCase )
															local_variables[1].assign(__lowerCamelCase )
															strategy.run(__lowerCamelCase	,	args=(gradient_placeholder,) )

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

									def _check_local_values(__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any] ):
												A							:					Optional[int]              =						strategy.experimental_local_results(accumulator._gradients[0] )
												self.assertListAlmostEqual(values[0].value()	,	__lowerCamelCase	,	tol=1e-2 )
												self.assertListAlmostEqual(values[1].value()	,	__lowerCamelCase	,	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] )