


"""simple docstring"""






import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
	from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
	from tensorflow.keras.optimizers import Adam


class 			lowerCamelCase_		(			tf.keras.optimizers.schedules.LearningRateSchedule   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 1.0       ,		UpperCAmelCase__ = None       ,		):
								super().__init__()
								SCREAMING_SNAKE_CASE__     							=				initial_learning_rate
								SCREAMING_SNAKE_CASE__     							=				warmup_steps
								SCREAMING_SNAKE_CASE__     							=				power
								SCREAMING_SNAKE_CASE__     							=				decay_schedule_fn
								SCREAMING_SNAKE_CASE__     							=				name



				def __call__(						self       ,		UpperCAmelCase__     ):
								with tf.name_scope(self.name or "WarmUp"     ) as name:
												# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
												# learning rate will be `global_step/num_warmup_steps * init_lr`.
												SCREAMING_SNAKE_CASE__     							=				tf.cast(UpperCAmelCase__       ,		tf.floataa     )
												SCREAMING_SNAKE_CASE__     							=				tf.cast(self.warmup_steps       ,		tf.floataa     )
												SCREAMING_SNAKE_CASE__     							=				global_step_float / warmup_steps_float
												SCREAMING_SNAKE_CASE__     							=				self.initial_learning_rate * tf.math.pow(UpperCAmelCase__       ,		self.power     )
												return tf.cond(
												    global_step_float < warmup_steps_float       ,		lambda: warmup_learning_rate       ,		lambda: self.decay_schedule_fn(step - self.warmup_steps     )       ,		name=UpperCAmelCase__       ,		)






				def   lowerCAmelCase__		(						self     ):
								return {
								    "initial_learning_rate": self.initial_learning_rate,
								    "decay_schedule_fn": self.decay_schedule_fn,
								    "warmup_steps": self.warmup_steps,
								    "power": self.power,
								    "name": self.name,
								}


def __lowercase     (      lowerCamelCase_  :      float						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      float = 0.0						,					lowerCamelCase_  :      float = 0.9						,					lowerCamelCase_  :      float = 0.999						,					lowerCamelCase_  :      float = 1e-8						,					lowerCamelCase_  :      Optional[float] = None						,					lowerCamelCase_  :      Optional[float] = None						,					lowerCamelCase_  :      float = 0.0						,					lowerCamelCase_  :      float = 1.0						,					lowerCamelCase_  :      Optional[List[str]] = None						,					):
				SCREAMING_SNAKE_CASE__     							=				tf.keras.optimizers.schedules.PolynomialDecay(
				    initial_learning_rate=lowerCamelCase_						,					decay_steps=num_train_steps - num_warmup_steps						,					end_learning_rate=init_lr * min_lr_ratio						,					power=lowerCamelCase_						,					)
				if num_warmup_steps:
								SCREAMING_SNAKE_CASE__     							=				WarmUp(
								    initial_learning_rate=lowerCamelCase_						,					decay_schedule_fn=lowerCamelCase_						,					warmup_steps=lowerCamelCase_						,					)
				if weight_decay_rate > 0.0:
								SCREAMING_SNAKE_CASE__     							=				AdamWeightDecay(
								    learning_rate=lowerCamelCase_						,					weight_decay_rate=lowerCamelCase_						,					beta_a=lowerCamelCase_						,					beta_a=lowerCamelCase_						,					epsilon=lowerCamelCase_						,					clipnorm=lowerCamelCase_						,					global_clipnorm=lowerCamelCase_						,					exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]						,					include_in_weight_decay=lowerCamelCase_						,					)
				else:
								SCREAMING_SNAKE_CASE__     							=				tf.keras.optimizers.Adam(
								    learning_rate=lowerCamelCase_						,					beta_a=lowerCamelCase_						,					beta_a=lowerCamelCase_						,					epsilon=lowerCamelCase_						,					clipnorm=lowerCamelCase_						,					global_clipnorm=lowerCamelCase_						,					)
				# We return the optimizer and the LR scheduler in order to better track the
				# evolution of the LR independently of the optimizer.
				return optimizer, lr_schedule


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__ = 0.001       ,		UpperCAmelCase__ = 0.9       ,		UpperCAmelCase__ = 0.999       ,		UpperCAmelCase__ = 1e-7       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = 0.0       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = "AdamWeightDecay"       ,		**UpperCAmelCase__       ,		):
								super().__init__(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				weight_decay_rate
								SCREAMING_SNAKE_CASE__     							=				include_in_weight_decay
								SCREAMING_SNAKE_CASE__     							=				exclude_from_weight_decay



				@classmethod
				def   lowerCAmelCase__		(						cls       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				{"WarmUp": WarmUp}
								return super(UpperCAmelCase__       ,		cls     ).from_config(UpperCAmelCase__       ,		custom_objects=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								super(UpperCAmelCase__       ,		self     )._prepare_local(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tf.constant(
								    self.weight_decay_rate       ,		name="adam_weight_decay_rate"     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self._do_use_weight_decay(var.name     )
								if do_decay:
												return var.assign_sub(
												    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]       ,		use_locking=self._use_locking       ,		)
								return tf.no_op()



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None       ,		**UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				list(zip(*UpperCAmelCase__     )     )
								return super(UpperCAmelCase__       ,		self     ).apply_gradients(zip(UpperCAmelCase__       ,		UpperCAmelCase__     )       ,		name=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								if apply_state is None:
												return self._decayed_lr_t[var_dtype], {}

								SCREAMING_SNAKE_CASE__     							=				apply_state or {}
								SCREAMING_SNAKE_CASE__     							=				apply_state.get((var_device, var_dtype)     )
								if coefficients is None:
												SCREAMING_SNAKE_CASE__     							=				self._fallback_apply_state(UpperCAmelCase__       ,		UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				coefficients

								return coefficients["lr_t"], {"apply_state": apply_state}



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self._get_lr(var.device       ,		var.dtype.base_dtype       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self._decay_weights_op(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     )
								with tf.control_dependencies([decay]     ):
												return super(UpperCAmelCase__       ,		self     )._resource_apply_dense(UpperCAmelCase__       ,		UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self._get_lr(var.device       ,		var.dtype.base_dtype       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self._decay_weights_op(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     )
								with tf.control_dependencies([decay]     ):
												return super(UpperCAmelCase__       ,		self     )._resource_apply_sparse(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				super().get_config()
								config.update({"weight_decay_rate": self.weight_decay_rate}     )
								return config






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								if self.weight_decay_rate == 0:
												return False

								if self._include_in_weight_decay:
												for r in self._include_in_weight_decay:
																if re.search(UpperCAmelCase__       ,		UpperCAmelCase__     ) is not None:
																				return True

								if self._exclude_from_weight_decay:
												for r in self._exclude_from_weight_decay:
																if re.search(UpperCAmelCase__       ,		UpperCAmelCase__     ) is not None:
																				return False
								return True


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				[]
								SCREAMING_SNAKE_CASE__     							=				None



				@property
				def   lowerCAmelCase__		(						self     ):
								if self._accum_steps is None:
												SCREAMING_SNAKE_CASE__     							=				tf.Variable(
												    tf.constant(0       ,		dtype=tf.intaa     )       ,		trainable=UpperCAmelCase__       ,		synchronization=tf.VariableSynchronization.ON_READ       ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,		)

								return self._accum_steps.value()



				@property
				def   lowerCAmelCase__		(						self     ):
								if not self._gradients:
												raise ValueError("The accumulator should be called first to initialize the gradients"     )
								return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]



				def __call__(						self       ,		UpperCAmelCase__     ):
								if not self._gradients:
												SCREAMING_SNAKE_CASE__     							=				self.step  # Create the step variable.
												self._gradients.extend(
												    [
												        tf.Variable(
												            tf.zeros_like(UpperCAmelCase__     )       ,		trainable=UpperCAmelCase__       ,		synchronization=tf.VariableSynchronization.ON_READ       ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,		)
												        if gradient is not None
												        else gradient
												        for gradient in gradients
												    ]     )
								if len(UpperCAmelCase__     ) != len(self._gradients     ):
												raise ValueError(f'''Expected {len(self._gradients     )} gradients, but got {len(UpperCAmelCase__     )}'''     )

								for accum_gradient, gradient in zip(self._gradients       ,		UpperCAmelCase__     ):
												if accum_gradient is not None and gradient is not None:
																accum_gradient.assign_add(UpperCAmelCase__     )

								self._accum_steps.assign_add(1     )






				def   lowerCAmelCase__		(						self     ):
								if not self._gradients:
												return
								self._accum_steps.assign(0     )
								for gradient in self._gradients:
												if gradient is not None:
																gradient.assign(tf.zeros_like(UpperCAmelCase__     )     )




"""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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				ArgumentParser("Accelerate CLI tool"						,					usage="accelerate <command> [<args>]"						,					allow_abbrev=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				parser.add_subparsers(help="accelerate command helpers"  )

				# Register commands
				get_config_parser(subparsers=lowerCamelCase_  )
				env_command_parser(subparsers=lowerCamelCase_  )
				launch_command_parser(subparsers=lowerCamelCase_  )
				tpu_command_parser(subparsers=lowerCamelCase_  )
				test_command_parser(subparsers=lowerCamelCase_  )

				# Let's go
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()

				if not hasattr(lowerCamelCase_						,					"func"  ):
								parser.print_help()
								exit(1  )

				# Run
				args.func(lowerCamelCase_  )


if __name__ == "__main__":
	main()
