





"""simple docstring"""




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        _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->int:






	'''simple docstring'''






	a   :	List[str]			=       botoa.client("iam"      )

	a   :	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=lowercase_					, AssumeRolePolicyDocument=json.dumps(lowercase_					, indent=2      )      )
		a   :	List[str]			=       {
		    "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        _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->List[str]:






	'''simple docstring'''






	a   :	Dict			=       botoa.client("iam"      )
	return iam_client.get_role(RoleName=lowercase_      )["Role"]["Arn"]







def        _SCREAMING_SNAKE_CASE	(			)				->int:






	'''simple docstring'''






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

		a   :	Any			=       _ask_field("AWS Secret Access Key: "      )
		a   :	Tuple			=       aws_secret_access_key

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

	a   :	Optional[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"]					, lowercase_					, )
	if role_management == 0:
		a   :	List[Any]			=       _ask_field("Enter your IAM role name: "      )
	else:
		a   :	str			=       "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_      )

	a   :	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."					, )
	a   :	Optional[int]			=       None
	if is_custom_docker_image:
		a   :	Optional[int]			=       _ask_field("Enter your Docker image: "					, lambda _lowercase      : str(lowercase_      ).lower()      )

	a   :	List[str]			=       _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."					, )
	a   :	Optional[int]			=       None
	if is_sagemaker_inputs_enabled:
		a   :	List[str]			=       _ask_field(
		    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "					, lambda _lowercase      : str(lowercase_      ).lower()					, )

	a   :	List[Any]			=       _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."					, )
	a   :	Union[str, Any]			=       None
	if is_sagemaker_metrics_enabled:
		a   :	Tuple			=       _ask_field(
		    "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "					, lambda _lowercase      : str(lowercase_      ).lower()					, )

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

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

	a   :	List[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_					, )









"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


a		:				Optional[Any]  			=    datasets.utils.logging.get_logger(__name__)

a		:				Union[str, Any]  			=    ['''names''', '''prefix''']
a		:				Any  			=    ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
a		:				Any  			=    ['''encoding_errors''', '''on_bad_lines''']
a		:				List[str]  			=    ['''date_format''']


@dataclass
class  __UpperCamelCase       (		datasets.BuilderConfig		):
							lowerCamelCase     :      str         =","
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[Union[int, List[int], str]]         ="infer"
							lowerCamelCase     :      Optional[List[str]]         =None
							lowerCamelCase     :      Optional[List[str]]         =None
							lowerCamelCase     :      Optional[Union[int, str, List[int], List[str]]]         =None
							lowerCamelCase     :      Optional[Union[List[int], List[str]]]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      Optional[Literal["c", "python", "pyarrow"]]         =None
							lowerCamelCase     :      Dict[Union[int, str], Callable[[Any], Any]]         =None
							lowerCamelCase     :      Optional[list]         =None
							lowerCamelCase     :      Optional[list]         =None
							lowerCamelCase     :      bool         =False
							lowerCamelCase     :      Optional[Union[int, List[int]]]         =None
							lowerCamelCase     :      Optional[int]         =None
							lowerCamelCase     :      Optional[Union[str, List[str]]]         =None
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =False
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      str         ="."
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      str         ='"'
							lowerCamelCase     :      int         =0
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      int         =0
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =False
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      int         =1_0000
							lowerCamelCase     :      Optional[datasets.Features]         =None
							lowerCamelCase     :      Optional[str]         ="strict"
							lowerCamelCase     :      Literal["error", "warn", "skip"]         ="error"
							lowerCamelCase     :      Optional[str]         =None






							def 					__a     (     self   )			->				Dict:
								if self.delimiter is not None:
									a   :	int			=       self.delimiter
								if self.column_names is not None:
									a   :	Any			=       self.column_names






							@property
							def 					__a     (     self   )			->				List[str]:
								a   :	Dict			=       {
								    "sep": self.sep,
								    "header": self.header,
								    "names": self.names,
								    "index_col": self.index_col,
								    "usecols": self.usecols,
								    "prefix": self.prefix,
								    "mangle_dupe_cols": self.mangle_dupe_cols,
								    "engine": self.engine,
								    "converters": self.converters,
								    "true_values": self.true_values,
								    "false_values": self.false_values,
								    "skipinitialspace": self.skipinitialspace,
								    "skiprows": self.skiprows,
								    "nrows": self.nrows,
								    "na_values": self.na_values,
								    "keep_default_na": self.keep_default_na,
								    "na_filter": self.na_filter,
								    "verbose": self.verbose,
								    "skip_blank_lines": self.skip_blank_lines,
								    "thousands": self.thousands,
								    "decimal": self.decimal,
								    "lineterminator": self.lineterminator,
								    "quotechar": self.quotechar,
								    "quoting": self.quoting,
								    "escapechar": self.escapechar,
								    "comment": self.comment,
								    "encoding": self.encoding,
								    "dialect": self.dialect,
								    "error_bad_lines": self.error_bad_lines,
								    "warn_bad_lines": self.warn_bad_lines,
								    "skipfooter": self.skipfooter,
								    "doublequote": self.doublequote,
								    "memory_map": self.memory_map,
								    "float_precision": self.float_precision,
								    "chunksize": self.chunksize,
								    "encoding_errors": self.encoding_errors,
								    "on_bad_lines": self.on_bad_lines,
								    "date_format": self.date_format,
								}

								# some kwargs must not be passed if they don't have a default value
								# some others are deprecated and we can also not pass them if they are the default value
								for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
									if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()   ,      lowerCAmelCase__   ):
										del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
								if not (datasets.config.PANDAS_VERSION.major >= 2):
									for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
										del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
								if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
									for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
										del pd_read_csv_kwargs[pd_read_csv_parameter]

								return pd_read_csv_kwargs


class  __UpperCamelCase       (		datasets.ArrowBasedBuilder		):
							lowerCamelCase     :      Union[str, Any]         =CsvConfig






							def 					__a     (     self   )			->				Optional[Any]:
								return datasets.DatasetInfo(features=self.config.features   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								if not self.config.data_files:
									raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
								a   :	Optional[Any]			=       dl_manager.download_and_extract(self.config.data_files   )
								if isinstance(lowerCAmelCase__   ,      (str, list, tuple)   ):
									a   :	Tuple			=       data_files
									if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
										a   :	Tuple			=       [files]
									a   :	int			=       [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,      gen_kwargs={"files": files}   )]
								a   :	int			=       []
								for split_name, files in data_files.items():
									if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
										a   :	Any			=       [files]
									a   :	List[str]			=       [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
									splits.append(datasets.SplitGenerator(name=lowerCAmelCase__   ,      gen_kwargs={"files": files}   )   )
								return splits






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				pa.Table:
								if self.config.features is not None:
									a   :	Optional[Any]			=       self.config.features.arrow_schema
									if all(not require_storage_cast(lowerCAmelCase__   ) for feature in self.config.features.values()   ):
										# cheaper cast
										a   :	Dict			=       pa.Table.from_arrays([pa_table[field.name] for field in schema]   ,      schema=lowerCAmelCase__   )
									else:
										# more expensive cast; allows str <-> int/float or str to Audio for example
										a   :	Union[str, Any]			=       table_cast(lowerCAmelCase__   ,      lowerCAmelCase__   )
								return pa_table






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								a   :	Tuple			=       self.config.features.arrow_schema if self.config.features else None
								# dtype allows reading an int column as str
								a   :	Any			=       (
								    {
								        name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__   ) else object
								        for name, dtype, feature in zip(schema.names   ,      schema.types   ,      self.config.features.values()   )
								    }
								    if schema is not None
								    else None
								)
								for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__   )   ):
									a   :	Tuple			=       pd.read_csv(lowerCAmelCase__   ,      iterator=lowerCAmelCase__   ,      dtype=lowerCAmelCase__   ,      **self.config.pd_read_csv_kwargs   )
									try:
										for batch_idx, df in enumerate(lowerCAmelCase__   ):
											a   :	Any			=       pa.Table.from_pandas(lowerCAmelCase__   )
											# Uncomment for debugging (will print the Arrow table size and elements)
											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
											yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__   )
									except ValueError as e:
										logger.error(f"""Failed to read file '{file}' with error {type(lowerCAmelCase__   )}: {e}"""   )
										raise


