

# 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 json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
						import botoa  # noqa: F401







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	botoa.client("iam"   )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	{
						    "Version": "2012-10-17",
						    "Statement": [
						        {"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
						    ],
						}
						try:
												# create the role, associated with the chosen trust policy
												iam_client.create_role(
												    RoleName=_UpperCAmelCase    ,     AssumeRolePolicyDocument=json.dumps(_UpperCAmelCase    ,     indent=2   )   )
												SCREAMING_SNAKE_CASE_:			int  		=	{
												    "Version": "2012-10-17",
												    "Statement": [
												        {
												            "Effect": "Allow",
												            "Action": [
												                "sagemaker:*",
												                "ecr:GetDownloadUrlForLayer",
												                "ecr:BatchGetImage",
												                "ecr:BatchCheckLayerAvailability",
												                "ecr:GetAuthorizationToken",
												                "cloudwatch:PutMetricData",
												                "cloudwatch:GetMetricData",
												                "cloudwatch:GetMetricStatistics",
												                "cloudwatch:ListMetrics",
												                "logs:CreateLogGroup",
												                "logs:CreateLogStream",
												                "logs:DescribeLogStreams",
												                "logs:PutLogEvents",
												                "logs:GetLogEvents",
												                "s3:CreateBucket",
												                "s3:ListBucket",
												                "s3:GetBucketLocation",
												                "s3:GetObject",
												                "s3:PutObject",
												            ],
												            "Resource": "*",
												        }
												    ],
												}
												# attach policy to role
												iam_client.put_role_policy(
												    RoleName=_UpperCAmelCase    ,     PolicyName=f"{role_name}_policy_permission"    ,     PolicyDocument=json.dumps(_UpperCAmelCase    ,     indent=2   )    ,     )
						except iam_client.exceptions.EntityAlreadyExistsException:
												print(f"role {role_name} already exists. Using existing one"   )







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	botoa.client("iam"   )
						return iam_client.get_role(RoleName=_UpperCAmelCase   )["Role"]["Arn"]







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			str  		=	_ask_options(
						    "How do you want to authorize?"    ,     ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]    ,     _UpperCAmelCase    ,     )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	None
						if credentials_configuration == 0:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	_ask_field("Enter your AWS Profile name: [default] "    ,     default="default"   )
												SCREAMING_SNAKE_CASE_:			Dict  		=	aws_profile
						else:
												print(
												    "Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
												    "`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`"   )
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	_ask_field("AWS Access Key ID: "   )
												SCREAMING_SNAKE_CASE_:			Dict  		=	aws_access_key_id

												SCREAMING_SNAKE_CASE_:			int  		=	_ask_field("AWS Secret Access Key: "   )
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	aws_secret_access_key

						SCREAMING_SNAKE_CASE_:			Any  		=	_ask_field("Enter your AWS Region: [us-east-1]"    ,     default="us-east-1"   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	aws_region

						SCREAMING_SNAKE_CASE_:			Tuple  		=	_ask_options(
						    "Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"    ,     ["Provide IAM Role name", "Create new IAM role using credentials"]    ,     _UpperCAmelCase    ,     )
						if role_management == 0:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	_ask_field("Enter your IAM role name: "   )
						else:
												SCREAMING_SNAKE_CASE_:			Any  		=	"accelerate_sagemaker_execution_role"
												print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"   )
												_create_iam_role_for_sagemaker(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	_ask_field(
						    "Do you want to use custom Docker image? [yes/NO]: "    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	None
						if is_custom_docker_image:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	_ask_field("Enter your Docker image: "    ,     lambda _UpperCAmelCase   : str(_UpperCAmelCase   ).lower()   )

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	_ask_field(
						    "Do you want to provide SageMaker input channels with data locations? [yes/NO]: "    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	None
						if is_sagemaker_inputs_enabled:
												SCREAMING_SNAKE_CASE_:			Dict  		=	_ask_field(
												    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "    ,     lambda _UpperCAmelCase   : str(_UpperCAmelCase   ).lower()    ,     )

						SCREAMING_SNAKE_CASE_:			int  		=	_ask_field(
						    "Do you want to enable SageMaker metrics? [yes/NO]: "    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )
						SCREAMING_SNAKE_CASE_:			Dict  		=	None
						if is_sagemaker_metrics_enabled:
												SCREAMING_SNAKE_CASE_:			Dict  		=	_ask_field(
												    "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "    ,     lambda _UpperCAmelCase   : str(_UpperCAmelCase   ).lower()    ,     )

						SCREAMING_SNAKE_CASE_:			List[str]  		=	_ask_options(
						    "What is the distributed mode?"    ,     ["No distributed training", "Data parallelism"]    ,     _convert_sagemaker_distributed_mode    ,     )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	{}
						SCREAMING_SNAKE_CASE_:			List[str]  		=	_ask_field(
						    "Do you wish to optimize your script with torch dynamo?[yes/NO]:"    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )
						if use_dynamo:
												SCREAMING_SNAKE_CASE_:			int  		=	"dynamo_"
												SCREAMING_SNAKE_CASE_:			str  		=	_ask_options(
												    "Which dynamo backend would you like to use?"    ,     [x.lower() for x in DYNAMO_BACKENDS]    ,     _convert_dynamo_backend    ,     default=2    ,     )
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	_ask_field(
												    "Do you want to customize the defaults sent to torch.compile? [yes/NO]: "    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )

												if use_custom_options:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	_ask_options(
																		    "Which mode do you want to use?"    ,     _UpperCAmelCase    ,     lambda _UpperCAmelCase   : TORCH_DYNAMO_MODES[int(_UpperCAmelCase   )]    ,     default="default"    ,     )
																		SCREAMING_SNAKE_CASE_:			Any  		=	_ask_field(
																		    "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: "    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )
																		SCREAMING_SNAKE_CASE_:			int  		=	_ask_field(
																		    "Do you want to enable dynamic shape tracing? [yes/NO]: "    ,     _convert_yes_no_to_bool    ,     default=_UpperCAmelCase    ,     error_message="Please enter yes or no."    ,     )
						SCREAMING_SNAKE_CASE_:			str  		=	"Which EC2 instance type you want to use for your training?"
						if distributed_type != SageMakerDistributedType.NO:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	_ask_options(
												    _UpperCAmelCase    ,     _UpperCAmelCase    ,     lambda _UpperCAmelCase   : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_UpperCAmelCase   )]   )
						else:
												eca_instance_query += "? [ml.p3.2xlarge]:"
												SCREAMING_SNAKE_CASE_:			List[str]  		=	_ask_field(_UpperCAmelCase    ,     lambda _UpperCAmelCase   : str(_UpperCAmelCase   ).lower()    ,     default="ml.p3.2xlarge"   )

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1
						if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	_ask_field(
												    "How many machines do you want use? [1]: "    ,     _UpperCAmelCase    ,     default=1    ,     )

						SCREAMING_SNAKE_CASE_:			str  		=	_ask_options(
						    "Do you wish to use FP16 or BF16 (mixed precision)?"    ,     ["no", "fp16", "bf16", "fp8"]    ,     _convert_mixed_precision    ,     )

						if use_dynamo and mixed_precision == "no":
												print(
												    "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts."   )

						return SageMakerConfig(
						    image_uri=_UpperCAmelCase    ,     compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER    ,     distributed_type=_UpperCAmelCase    ,     use_cpu=_UpperCAmelCase    ,     dynamo_config=_UpperCAmelCase    ,     eca_instance_type=_UpperCAmelCase    ,     profile=_UpperCAmelCase    ,     region=_UpperCAmelCase    ,     iam_role_name=_UpperCAmelCase    ,     mixed_precision=_UpperCAmelCase    ,     num_machines=_UpperCAmelCase    ,     sagemaker_inputs_file=_UpperCAmelCase    ,     sagemaker_metrics_file=_UpperCAmelCase    ,     )









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




			"""simple docstring"""







			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					Callable       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					float = 1.0       ,						lowerCAmelCase__  :					str = None       ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_:			str  		=	initial_learning_rate
									SCREAMING_SNAKE_CASE_:			Dict  		=	warmup_steps
									SCREAMING_SNAKE_CASE_:			Any  		=	power
									SCREAMING_SNAKE_CASE_:			int  		=	decay_schedule_fn
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	name



			def __call__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Any):
									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_:			Any  		=	tf.cast(lowerCAmelCase__       ,						tf.floataa)
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tf.cast(self.warmup_steps       ,						tf.floataa)
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	global_step_float / warmup_steps_float
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.initial_learning_rate * tf.math.pow(lowerCAmelCase__       ,						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=lowerCAmelCase__       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = 0.0    ,     _UpperCAmelCase = 0.9    ,     _UpperCAmelCase = 0.9_9_9    ,     _UpperCAmelCase = 1e-8    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = 0.0    ,     _UpperCAmelCase = 1.0    ,     _UpperCAmelCase = None    ,     ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	WarmUp(
												    initial_learning_rate=_UpperCAmelCase    ,     decay_schedule_fn=_UpperCAmelCase    ,     warmup_steps=_UpperCAmelCase    ,     )
						if weight_decay_rate > 0.0:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	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:
												SCREAMING_SNAKE_CASE_:			int  		=	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   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001       ,						lowerCAmelCase__  :					float = 0.9       ,						lowerCAmelCase__  :					float = 0.999       ,						lowerCAmelCase__  :					float = 1E-7       ,						lowerCAmelCase__  :					bool = False       ,						lowerCAmelCase__  :					float = 0.0       ,						lowerCAmelCase__  :					Optional[List[str]] = None       ,						lowerCAmelCase__  :					Optional[List[str]] = None       ,						lowerCAmelCase__  :					str = "AdamWeightDecay"       ,						**lowerCAmelCase__  :					int       ,						):
									super().__init__(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	weight_decay_rate
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	include_in_weight_decay
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	exclude_from_weight_decay



			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					Dict       ,						lowerCAmelCase__  :					Dict):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{"WarmUp": WarmUp}
									return super(lowerCAmelCase__       ,						cls).from_config(lowerCAmelCase__       ,						custom_objects=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Optional[int]):
									super(lowerCAmelCase__       ,						self)._prepare_local(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tf.constant(
									    self.weight_decay_rate       ,						name="adam_weight_decay_rate")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			str  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Optional[Any]=None       ,						**lowerCAmelCase__  :					List[str]):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	list(zip(*lowerCAmelCase__))
									return super(lowerCAmelCase__       ,						self).apply_gradients(zip(lowerCAmelCase__       ,						lowerCAmelCase__)       ,						name=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Tuple):
									if apply_state is None:
															return self._decayed_lr_t[var_dtype], {}

									SCREAMING_SNAKE_CASE_:			Dict  		=	apply_state or {}
									SCREAMING_SNAKE_CASE_:			List[str]  		=	apply_state.get((var_device, var_dtype))
									if coefficients is None:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._fallback_apply_state(lowerCAmelCase__       ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:			Any  		=	coefficients

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Tuple=None):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._decay_weights_op(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									with tf.control_dependencies([decay]):
															return super(lowerCAmelCase__       ,						self)._resource_apply_dense(lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Dict=None):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	self._decay_weights_op(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									with tf.control_dependencies([decay]):
															return super(lowerCAmelCase__       ,						self)._resource_apply_sparse(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	super().get_config()
									config.update({"weight_decay_rate": self.weight_decay_rate})
									return config




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Tuple):
									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(lowerCAmelCase__       ,						lowerCAmelCase__) is not None:
																											return True

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







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	[]
									SCREAMING_SNAKE_CASE_:			Any  		=	None



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

									return self._accum_steps.value()



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									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  :					str       ,						lowerCAmelCase__  :					Tuple):
									if not self._gradients:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.step  # Create the step variable.
															self._gradients.extend(
															    [
															        tf.Variable(
															            tf.zeros_like(lowerCAmelCase__)       ,						trainable=lowerCAmelCase__       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)
															        if gradient is not None
															        else gradient
															        for gradient in gradients
															    ])
									if len(lowerCAmelCase__) != len(self._gradients):
															raise ValueError(F"Expected {len(self._gradients)} gradients, but got {len(lowerCAmelCase__)}")

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

									self._accum_steps.assign_add(1)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									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(lowerCAmelCase__))






