




'''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     A					(	unittest.TestCase					):







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Dict:


														"""simple docstring"""


														self.assertEqual(len(lowerCAmelCase_      )							,    len(lowerCAmelCase_      )      )
														for a, b in zip(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					self.assertAlmostEqual(lowerCAmelCase_							,    lowerCAmelCase_							,    delta=lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       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      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       None
														ops.enable_eager_execution_internal()
														_a			  =       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			  =       tf.config.list_logical_devices(device_type='''CPU'''      )
														_a			  =       tf.distribute.MirroredStrategy(devices=devices[:2]      )

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

														def accumulate_on_replica(lowerCAmelCase_ :						Any      ):
																					accumulator([gradient]      )

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

														@tf.function
														def accumulate(lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						int      ):
																					with strategy.scope():
																												_a			  =       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_ :						str							,    lowerCAmelCase_ :						str      ):
																					_a			  =       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]      )







'''simple docstring'''


from manim import *

class     A					(	_a					):







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Dict:


														"""simple docstring"""


														_a			  =       Rectangle(height=0.5							,    width=0.5      )
														_a			  =       Rectangle(height=0.4_6							,    width=0.4_6      ).set_stroke(width=0      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       VGroup(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       Text('''CPU'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														cpu.move_to([-2.5, -0.5, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(4      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       Text('''GPU'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														gpu.move_to([-1, -1, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )

														_a			  =       Text('''Model'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														model.move_to([3, -1.0, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       []
														for i, rect in enumerate(lowerCAmelCase_      ):
																					rect.set_stroke(lowerCAmelCase_      )
																					# target = fill.copy().set_fill(YELLOW, opacity=0.7)
																					# target.move_to(rect)
																					# self.add(target)

																					_a			  =       Rectangle(height=0.4_6 / 4							,    width=0.4_6 / 3      ).set_stroke(width=0.0      ).set_fill(lowerCAmelCase_							,    opacity=0.7      )

																					if i == 0:
																												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )							,    buff=0.0_2							,    direction=lowerCAmelCase_      )
																												cpu_target.set_x(cpu_target.get_x() + 0.1      )
																					elif i == 3:
																												cpu_target.next_to(cpu_targs[0]							,    direction=lowerCAmelCase_							,    buff=0.0      )
																					else:
																												cpu_target.next_to(cpu_targs[i - 1]							,    direction=lowerCAmelCase_							,    buff=0.0      )
																					self.add(lowerCAmelCase_      )
																					cpu_targs.append(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )

														_a			  =       Text('''Loaded Checkpoint'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    aligned_edge=lowerCAmelCase_							,    buff=0.4      )
														checkpoint.move_to([3, 0.5, 0]      )

														_a			  =       Square(side_length=2.2      )
														key.move_to([-5, 2, 0]      )

														_a			  =       MarkupText(
														    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'							,    font_size=18							,    )

														key_text.move_to([-5, 2.4, 0]      )

														self.add(lowerCAmelCase_							,    lowerCAmelCase_      )

														_a			  =       MarkupText(
														    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'							,    font_size=18							,    )

														blue_text.next_to(lowerCAmelCase_							,    DOWN * 2.4							,    aligned_edge=key_text.get_left()      )

														_a			  =       MarkupText(
														    F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'							,    font_size=24							,    )
														step_a.move_to([2, 2, 0]      )
														self.play(Write(lowerCAmelCase_      )							,    Write(lowerCAmelCase_      )      )

														self.play(Write(lowerCAmelCase_							,    run_time=1      )							,    Create(lowerCAmelCase_							,    run_time=1      )      )

														_a			  =       []
														_a			  =       []
														for i, rect in enumerate(lowerCAmelCase_      ):
																					_a			  =       fill.copy().set_fill(lowerCAmelCase_							,    opacity=0.7      )
																					target.move_to(lowerCAmelCase_      )
																					first_animations.append(GrowFromCenter(lowerCAmelCase_							,    run_time=1      )      )

																					_a			  =       target.copy()
																					cpu_target.generate_target()
																					if i < 5:
																												cpu_target.target.move_to(cpu_left_col_base[i + 1]      )
																					else:
																												cpu_target.target.move_to(cpu_right_col_base[i - 5]      )
																					second_animations.append(MoveToTarget(lowerCAmelCase_							,    run_time=1.5      )      )

														self.play(*lowerCAmelCase_      )
														self.play(*lowerCAmelCase_      )
														self.wait()

