





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''gpt_neox_japanese'''


    def __init__(		self ,		A=3_2000 ,		A=2560 ,		A=32 ,		A=32 ,		A=4 ,		A="gelu" ,		A=1.00 ,		A=1_0000 ,		A=2048 ,		A=0.02 ,		A=1e-5 ,		A=True ,		A=3_1996 ,		A=3_1999 ,		A=0.1 ,		A=0.0 ,		**A ,		)		->   List[str]:
        super().__init__(bos_token_id=UpperCamelCase__ ,		eos_token_id=UpperCamelCase__ ,		**UpperCamelCase__       )
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_multiple_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			rotary_pct
        _SCREAMING_SNAKE_CASE									=			rotary_emb_base
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			attention_dropout
        _SCREAMING_SNAKE_CASE									=			hidden_dropout







"""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   __lowerCAmelCase						(						lowercase  :  List[str] )	->						str:


    """simple docstring"""





    snake_case    :      Optional[int]      					=    botoa.client("iam" )

    snake_case    :      Any      					=    {
        "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=lowercase						,							AssumeRolePolicyDocument=json.dumps(lowercase						,							indent=2 ) )
        snake_case    :      Union[str, Any]      					=    {
            "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=lowercase						,							PolicyName=F'{role_name}_policy_permission'						,							PolicyDocument=json.dumps(lowercase						,							indent=2 )						,							)
    except iam_client.exceptions.EntityAlreadyExistsException:
        print(F'role {role_name} already exists. Using existing one' )







def   __lowerCAmelCase						(						lowercase  :  Dict )	->						Optional[int]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





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

        snake_case    :      Optional[int]      					=    _ask_field("AWS Secret Access Key: " )
        snake_case    :      Union[str, Any]      					=    aws_secret_access_key

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

    snake_case    :      List[str]      					=    _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"]						,							lowercase						,							)
    if role_management == 0:
        snake_case    :      Tuple      					=    _ask_field("Enter your IAM role name: " )
    else:
        snake_case    :      Union[str, 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(lowercase )

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

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

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

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

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

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

    snake_case    :      Union[str, Any]      					=    _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=lowercase						,							compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER						,							distributed_type=lowercase						,							use_cpu=lowercase						,							dynamo_config=lowercase						,							eca_instance_type=lowercase						,							profile=lowercase						,							region=lowercase						,							iam_role_name=lowercase						,							mixed_precision=lowercase						,							num_machines=lowercase						,							sagemaker_inputs_file=lowercase						,							sagemaker_metrics_file=lowercase						,							)





