
"""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 			snake_case  (				unittest.TestCase ):

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			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(SCREAMING_SNAKE_CASE_     ):
											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 _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			None
									ops.enable_eager_execution_internal()
									SCREAMING_SNAKE_CASE_    			=			tf.config.list_physical_devices('''CPU'''     )
									if len(SCREAMING_SNAKE_CASE_     ) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0]							,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]     )
									SCREAMING_SNAKE_CASE_    			=			tf.config.list_logical_devices(device_type='''CPU'''     )
									SCREAMING_SNAKE_CASE_    			=			tf.distribute.MirroredStrategy(devices=devices[:2]     )

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

									def accumulate_on_replica(SCREAMING_SNAKE_CASE_     ):
											accumulator([gradient]     )

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

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

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

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




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async



def 							_lowerCamelCase			(			__a=None     ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file''',				default=__a,				help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    ),				)

		if subparsers is not None:
				parser.set_defaults(func=__a     )
		return parser



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE_    			=			script_name
		else:
				SCREAMING_SNAKE_CASE_    			=			F'--config_file={args.config_file} {script_name}'

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			execute_subprocess_async(__a,				env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()