



# 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__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					botoa.client('''iam''')

				lowerCAmelCase__					:    List[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=lowerCamelCase_	,AssumeRolePolicyDocument=json.dumps(lowerCamelCase_	,indent=2))
								lowerCAmelCase__					:    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=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      lowerCAmelCase__		(   lowerCamelCase_		:		Dict):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					botoa.client('''iam''')
				return iam_client.get_role(RoleName=lowerCamelCase_)["Role"]["Arn"]






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

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

								lowerCAmelCase__					:    Optional[Any]												=					_ask_field('''AWS Secret Access Key: ''')
								lowerCAmelCase__					:    Optional[Any]												=					aws_secret_access_key

				lowerCAmelCase__					:    Tuple												=					_ask_field('''Enter your AWS Region: [us-east-1]'''	,default='''us-east-1''')
				lowerCAmelCase__					:    Any												=					aws_region

				lowerCAmelCase__					:    List[Any]												=					_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:
								lowerCAmelCase__					:    int												=					_ask_field('''Enter your IAM role name: ''')
				else:
								lowerCAmelCase__					:    Optional[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(lowerCamelCase_)

				lowerCAmelCase__					:    str												=					_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.'''	,)
				lowerCAmelCase__					:    int												=					None
				if is_custom_docker_image:
								lowerCAmelCase__					:    Optional[int]												=					_ask_field('''Enter your Docker image: '''	,lambda lowerCamelCase_: str(lowerCamelCase_).lower())

				lowerCAmelCase__					:    Union[str, Any]												=					_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.'''	,)
				lowerCAmelCase__					:    Tuple												=					None
				if is_sagemaker_inputs_enabled:
								lowerCAmelCase__					:    Optional[Any]												=					_ask_field(
								    '''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '''	,lambda lowerCamelCase_: str(lowerCamelCase_).lower()	,)

				lowerCAmelCase__					:    Any												=					_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.'''	,)
				lowerCAmelCase__					:    List[str]												=					None
				if is_sagemaker_metrics_enabled:
								lowerCAmelCase__					:    List[str]												=					_ask_field(
								    '''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '''	,lambda lowerCamelCase_: str(lowerCamelCase_).lower()	,)

				lowerCAmelCase__					:    Any												=					_ask_options(
				    '''What is the distributed mode?'''	,['''No distributed training''', '''Data parallelism''']	,_convert_sagemaker_distributed_mode	,)
				lowerCAmelCase__					:    int												=					{}
				lowerCAmelCase__					:    Optional[Any]												=					_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:
								lowerCAmelCase__					:    str												=					'''dynamo_'''
								lowerCAmelCase__					:    Optional[Any]												=					_ask_options(
								    '''Which dynamo backend would you like to use?'''	,[x.lower() for x in DYNAMO_BACKENDS]	,_convert_dynamo_backend	,default=2	,)
								lowerCAmelCase__					:    List[str]												=					_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:
												lowerCAmelCase__					:    Any												=					_ask_options(
												    '''Which mode do you want to use?'''	,lowerCamelCase_	,lambda lowerCamelCase_: TORCH_DYNAMO_MODES[int(lowerCamelCase_)]	,default='''default'''	,)
												lowerCAmelCase__					:    Dict												=					_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.'''	,)
												lowerCAmelCase__					:    Dict												=					_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.'''	,)
				lowerCAmelCase__					:    List[str]												=					'''Which EC2 instance type you want to use for your training?'''
				if distributed_type != SageMakerDistributedType.NO:
								lowerCAmelCase__					:    str												=					_ask_options(
								    lowerCamelCase_	,lowerCamelCase_	,lambda lowerCamelCase_: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(lowerCamelCase_)])
				else:
								eca_instance_query += "? [ml.p3.2xlarge]:"
								lowerCAmelCase__					:    Tuple												=					_ask_field(lowerCamelCase_	,lambda lowerCamelCase_: str(lowerCamelCase_).lower()	,default='''ml.p3.2xlarge''')

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

				lowerCAmelCase__					:    List[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=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_	,)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str = "The quick brown fox jumps over the lazy dog"	,):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					set()

				# Replace all the whitespace in our sentence
				lowerCAmelCase__					:    List[Any]												=					input_str.replace(''' '''	,'''''')
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower())
				return len(lowerCamelCase_) == 26






def      lowerCAmelCase__		(   lowerCamelCase_		:		str = "The quick brown fox jumps over the lazy dog"	,):





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					[False] * 26
				for char in input_str:
								if char.islower():
												lowerCAmelCase__					:    Union[str, Any]												=					True
								elif char.isupper():
												lowerCAmelCase__					:    str												=					True
				return all(lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str = "The quick brown fox jumps over the lazy dog"	,):





				'''simple docstring'''

				return len({char for char in input_str.lower() if char.isalpha()}) == 26






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				from timeit import timeit

				lowerCAmelCase__					:    Optional[Any]												=					'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
				print(timeit('''is_pangram()'''	,setup=lowerCamelCase_))
				print(timeit('''is_pangram_faster()'''	,setup=lowerCamelCase_))
				print(timeit('''is_pangram_fastest()'''	,setup=lowerCamelCase_))
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

