






'''simple docstring'''



import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
 import sqlitea

 import sqlalchemy
class 							__SCREAMING_SNAKE_CASE       (							__a     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = False ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         super().__init__(features=lowerCAmelCase_ ,   cache_dir=lowerCAmelCase_ ,   keep_in_memory=lowerCAmelCase_ ,   **lowerCAmelCase_      )
         __a				     =    Sql(
             cache_dir=lowerCAmelCase_ ,   features=lowerCAmelCase_ ,   sql=lowerCAmelCase_ ,   con=lowerCAmelCase_ ,   **lowerCAmelCase_ ,   )






      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    None
         __a				     =    None
         __a				     =    None
         __a				     =    None

         self.builder.download_and_prepare(
             download_config=lowerCAmelCase_ ,   download_mode=lowerCAmelCase_ ,   verification_mode=lowerCAmelCase_ ,   base_path=lowerCAmelCase_ ,   )

         # Build dataset for splits
         __a				     =    self.builder.as_dataset(
             split='train' ,   verification_mode=lowerCAmelCase_ ,   in_memory=self.keep_in_memory      )
         return dataset







class 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->List[Any]:



         '''simple docstring'''







         if num_proc is not None and num_proc <= 0:
            raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""      )

         __a				     =    dataset
         __a				     =    name
         __a				     =    con
         __a				     =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         __a				     =    num_proc
         __a				     =    to_sql_kwargs




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.to_sql_kwargs.pop('sql' ,   lowerCAmelCase_      )
         __a				     =    self.to_sql_kwargs.pop('con' ,   lowerCAmelCase_      )
         __a				     =    self.to_sql_kwargs.pop('index' ,   lowerCAmelCase_      )

         __a				     =    self._write(index=lowerCAmelCase_ ,   **self.to_sql_kwargs      )
         return written




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         __a     ,			__a     ,			__a				     =    args
         __a				     =    {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
         __a				     =    query_table(
             table=self.dataset.data ,   key=slice(lowerCAmelCase_ ,   offset + self.batch_size      ) ,   indices=self.dataset._indices ,   )
         __a				     =    batch.to_pandas()
         __a				     =    df.to_sql(self.name ,   self.con ,   index=lowerCAmelCase_ ,   **lowerCAmelCase_      )
         return num_rows or len(lowerCAmelCase_      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   **lowerCamelCase      )							->int:



         '''simple docstring'''
         __a				     =    0

         if self.num_proc is None or self.num_proc == 1:
            for offset in logging.tqdm(
                range(0 ,   len(self.dataset      ) ,   self.batch_size      ) ,   unit='ba' ,   disable=not logging.is_progress_bar_enabled() ,   desc='Creating SQL from Arrow format' ,   ):
               written += self._batch_sql((offset, index, to_sql_kwargs)      )
         else:
            __a     ,			__a				     =    len(self.dataset      ), self.batch_size
            with multiprocessing.Pool(self.num_proc      ) as pool:
               for num_rows in logging.tqdm(
                   pool.imap(
                       self._batch_sql ,   [(offset, index, to_sql_kwargs) for offset in range(0 ,   lowerCAmelCase_ ,   lowerCAmelCase_      )] ,   ) ,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,   unit='ba' ,   disable=not logging.is_progress_bar_enabled() ,   desc='Creating SQL from Arrow format' ,   ):
                  written += num_rows

         return written







'''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	(				SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			List[str]:

   """simple docstring"""
   __a				     =    botoa.client('iam'    )

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str]    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    botoa.client('iam'    )
   return iam_client.get_role(RoleName=SCREAMING_SNAKE_CASE__    )["Role"]["Arn"]



def    __UpperCAmelCase	(				)  ->			Any:

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

      __a				     =    _ask_field('AWS Secret Access Key: '    )
      __a				     =    aws_secret_access_key

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

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

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

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

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

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

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

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

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