


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        UpperCamelCase__   (	tf.keras.optimizers.schedules.LearningRateSchedule   ):

					def __init__(     self       :   int							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Callable							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   float = 1.0							,					UpperCamelCase__       :   str = None							,					):






										'''simple docstring'''






										super().__init__()
										lowercase_  							=			initial_learning_rate
										lowercase_  							=			warmup_steps
										lowercase_  							=			power
										lowercase_  							=			decay_schedule_fn
										lowercase_  							=			name

					def __call__(     self       :   Dict							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										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`.
															lowercase_  							=			tf.cast(UpperCamelCase__							,					tf.floataa       )
															lowercase_  							=			tf.cast(self.warmup_steps							,					tf.floataa       )
															lowercase_  							=			global_step_float / warmup_steps_float
															lowercase_  							=			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   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__ = 0.0						,							UpperCAmelCase__ = 0.9						,							UpperCAmelCase__ = 0.999						,							UpperCAmelCase__ = 1e-8						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = 0.0						,							UpperCAmelCase__ = 1.0						,							UpperCAmelCase__ = None						,							):
					lowercase_  							=			tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=UpperCAmelCase__						,							decay_steps=num_train_steps - num_warmup_steps						,							end_learning_rate=init_lr * min_lr_ratio						,							power=UpperCAmelCase__						,							)
					if num_warmup_steps:
										lowercase_  							=			WarmUp(
										    initial_learning_rate=UpperCAmelCase__						,							decay_schedule_fn=UpperCAmelCase__						,							warmup_steps=UpperCAmelCase__						,							)
					if weight_decay_rate > 0.0:
										lowercase_  							=			AdamWeightDecay(
										    learning_rate=UpperCAmelCase__						,							weight_decay_rate=UpperCAmelCase__						,							beta_a=UpperCAmelCase__						,							beta_a=UpperCAmelCase__						,							epsilon=UpperCAmelCase__						,							clipnorm=UpperCAmelCase__						,							global_clipnorm=UpperCAmelCase__						,							exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]						,							include_in_weight_decay=UpperCAmelCase__						,							)
					else:
										lowercase_  							=			tf.keras.optimizers.Adam(
										    learning_rate=UpperCAmelCase__						,							beta_a=UpperCAmelCase__						,							beta_a=UpperCAmelCase__						,							epsilon=UpperCAmelCase__						,							clipnorm=UpperCAmelCase__						,							global_clipnorm=UpperCAmelCase__						,							)
					# 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        UpperCamelCase__   (	__magic_name__   ):

					def __init__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001							,					UpperCamelCase__       :   float = 0.9							,					UpperCamelCase__       :   float = 0.999							,					UpperCamelCase__       :   float = 1e-7							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   float = 0.0							,					UpperCamelCase__       :   Optional[List[str]] = None							,					UpperCamelCase__       :   Optional[List[str]] = None							,					UpperCamelCase__       :   str = "AdamWeightDecay"							,					**UpperCamelCase__       :   List[str]							,					):






										'''simple docstring'''






										super().__init__(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__       )
										lowercase_  							=			weight_decay_rate
										lowercase_  							=			include_in_weight_decay
										lowercase_  							=			exclude_from_weight_decay

					@classmethod
					def   UpperCAmelCase__       (     cls       :   Tuple							,					UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			{"""WarmUp""": WarmUp}
										return super(UpperCamelCase__							,					cls       ).from_config(UpperCamelCase__							,					custom_objects=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										super(UpperCamelCase__							,					self       )._prepare_local(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
										lowercase_  							=			tf.constant(
										    self.weight_decay_rate							,					name="""adam_weight_decay_rate"""       )

					def   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			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   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   int=None							,					**UpperCamelCase__       :   Any       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			list(zip(*UpperCamelCase__       )       )
										return super(UpperCamelCase__							,					self       ).apply_gradients(zip(UpperCamelCase__							,					UpperCamelCase__       )							,					name=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										if apply_state is None:
															return self._decayed_lr_t[var_dtype], {}

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

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

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   str=None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self._get_lr(var.device							,					var.dtype.base_dtype							,					UpperCamelCase__       )
										lowercase_  							=			self._decay_weights_op(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
										with tf.control_dependencies([decay]       ):
															return super(UpperCamelCase__							,					self       )._resource_apply_dense(UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   int=None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self._get_lr(var.device							,					var.dtype.base_dtype							,					UpperCamelCase__       )
										lowercase_  							=			self._decay_weights_op(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
										with tf.control_dependencies([decay]       ):
															return super(UpperCamelCase__							,					self       )._resource_apply_sparse(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			super().get_config()
										config.update({"""weight_decay_rate""": self.weight_decay_rate}       )
										return config







					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   List[str]       ):






										'''simple docstring'''






										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        UpperCamelCase__   (	__magic_name__   ):

					def __init__(     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			[]
										lowercase_  							=			None

					@property
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										if self._accum_steps is None:
															lowercase_  							=			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   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										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       :   Optional[int]							,					UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										if not self._gradients:
															lowercase_  							=			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   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										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__       )       )






# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available


def 			UpperCAmelCase_				(      UpperCAmelCase__=None  ):
					if subparsers is not None:
										lowercase_  							=			subparsers.add_parser("""env"""  )
					else:
										lowercase_  							=			argparse.ArgumentParser("""Accelerate env command"""  )

					parser.add_argument(
					    """--config_file"""						,							default=UpperCAmelCase__						,							help="""The config file to use for the default values in the launching script."""  )

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


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			torch.__version__
					lowercase_  							=			torch.cuda.is_available()
					lowercase_  							=			is_xpu_available()
					lowercase_  							=			is_npu_available()

					lowercase_  							=			"""Not found"""
					# Get the default from the config file.
					if args.config_file is not None or os.path.isfile(UpperCAmelCase__  ):
										lowercase_  							=			load_config_from_file(args.config_file  ).to_dict()

					lowercase_  							=			{
					    """`Accelerate` version""": version,
					    """Platform""": platform.platform(),
					    """Python version""": platform.python_version(),
					    """Numpy version""": np.__version__,
					    """PyTorch version (GPU?)""": F'''{pt_version} ({pt_cuda_available})''',
					    """PyTorch XPU available""": str(UpperCAmelCase__  ),
					    """PyTorch NPU available""": str(UpperCAmelCase__  ),
					    """System RAM""": F'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
					}
					if pt_cuda_available:
										lowercase_  							=			torch.cuda.get_device_name()

					print("""\nCopy-and-paste the text below in your GitHub issue\n"""  )
					print("""\n""".join([F'''- {prop}: {val}''' for prop, val in info.items()]  )  )

					print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""  )
					lowercase_  							=			(
					    """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
					    if isinstance(UpperCAmelCase__						,							UpperCAmelCase__  )
					    else F'''\t{accelerate_config}'''
					)
					print(UpperCAmelCase__  )

					lowercase_  							=			accelerate_config

					return info


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			env_command_parser()
					lowercase_  							=			parser.parse_args()
					env_command(UpperCAmelCase__  )
					return 0


if __name__ == "__main__":
							raise SystemExit(main())


