





'''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 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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = botoa.client("iam"							)

				lowercase         = {
				    "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=lowerCAmelCase_		,							AssumeRolePolicyDocument=json.dumps(lowerCAmelCase_		,							indent=2							)							)
								lowercase         = {
								    "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=lowerCAmelCase_		,							PolicyName=f'{role_name}_policy_permission'		,							PolicyDocument=json.dumps(lowerCAmelCase_		,							indent=2							)		,							)
				except iam_client.exceptions.EntityAlreadyExistsException:
								print(f'role {role_name} already exists. Using existing one'							)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = botoa.client("iam"							)
				return iam_client.get_role(RoleName=lowerCAmelCase_							)["Role"]["Arn"]







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = _ask_options(
				    "How do you want to authorize?"		,							["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]		,							lowerCAmelCase_		,							)
				lowercase         = None
				if credentials_configuration == 0:
								lowercase         = _ask_field("Enter your AWS Profile name: [default] "		,							default="default"							)
								lowercase         = 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`"							)
								lowercase         = _ask_field("AWS Access Key ID: "							)
								lowercase         = aws_access_key_id

								lowercase         = _ask_field("AWS Secret Access Key: "							)
								lowercase         = aws_secret_access_key

				lowercase         = _ask_field("Enter your AWS Region: [us-east-1]"		,							default="us-east-1"							)
				lowercase         = aws_region

				lowercase         = _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"]		,							lowerCAmelCase_		,							)
				if role_management == 0:
								lowercase         = _ask_field("Enter your IAM role name: "							)
				else:
								lowercase         = "accelerate_sagemaker_execution_role"
								print(f'Accelerate will create an iam role "{iam_role_name}" using the provided credentials'							)
								_create_iam_role_for_sagemaker(lowerCAmelCase_							)

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

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

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

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

								if use_custom_options:
												lowercase         = _ask_options(
												    "Which mode do you want to use?"		,							lowerCAmelCase_		,							lambda lowerCAmelCase_							: TORCH_DYNAMO_MODES[int(lowerCAmelCase_							)]		,							default="default"		,							)
												lowercase         = _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=lowerCAmelCase_		,							error_message="Please enter yes or no."		,							)
												lowercase         = _ask_field(
												    "Do you want to enable dynamic shape tracing? [yes/NO]: "		,							_convert_yes_no_to_bool		,							default=lowerCAmelCase_		,							error_message="Please enter yes or no."		,							)
				lowercase         = "Which EC2 instance type you want to use for your training?"
				if distributed_type != SageMakerDistributedType.NO:
								lowercase         = _ask_options(
								    lowerCAmelCase_		,							lowerCAmelCase_		,							lambda lowerCAmelCase_							: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(lowerCAmelCase_							)]							)
				else:
								eca_instance_query += "? [ml.p3.2xlarge]:"
								lowercase         = _ask_field(lowerCAmelCase_		,							lambda lowerCAmelCase_							: str(lowerCAmelCase_							).lower()		,							default="ml.p3.2xlarge"							)

				lowercase         = 1
				if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
								lowercase         = _ask_field(
								    "How many machines do you want use? [1]: "		,							lowerCAmelCase_		,							default=1		,							)

				lowercase         = _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=lowerCAmelCase_		,							compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER		,							distributed_type=lowerCAmelCase_		,							use_cpu=lowerCAmelCase_		,							dynamo_config=lowerCAmelCase_		,							eca_instance_type=lowerCAmelCase_		,							profile=lowerCAmelCase_		,							region=lowerCAmelCase_		,							iam_role_name=lowerCAmelCase_		,							mixed_precision=lowerCAmelCase_		,							num_machines=lowerCAmelCase_		,							sagemaker_inputs_file=lowerCAmelCase_		,							sagemaker_metrics_file=lowerCAmelCase_		,							)











'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




