


"""simple docstring"""



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__snake_case   			=    logging.get_logger(__name__)







@dataclass
class _lowerCAmelCase				:
   __UpperCAmelCase :       str							  =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()	)}	)
   __UpperCAmelCase :       str							  =  field(
       metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}	)
   __UpperCAmelCase :       int							  =  field(
       default=128    ,							metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }    ,							)
   __UpperCAmelCase :       bool							  =  field(
       default=snake_case_    ,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.task_name.lower()







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  '''train'''
   __UpperCAmelCase :       str							  =  '''dev'''
   __UpperCAmelCase :       Dict							  =  '''test'''







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       GlueDataTrainingArguments
   __UpperCAmelCase :       str
   __UpperCAmelCase :       List[InputFeatures]







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = Split.train						,	UpperCamelCase__ = None						,	)	->		Any:

       '''simple docstring'''




       warnings.warn(
           "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
           "library. You can have a look at this example script for pointers: "
           "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"						,	UpperCamelCase__						,	)
       snake_case    :      Optional[int]      					=    args
       snake_case    :      List[str]      					=    glue_processors[args.task_name]()
       snake_case    :      Dict      					=    glue_output_modes[args.task_name]
       if isinstance(UpperCamelCase__						,	UpperCamelCase__						):
           try:
               snake_case    :      List[Any]      					=    Split[mode]
           except KeyError:
               raise KeyError("mode is not a valid split name"						)
        # Load data features from cache or dataset file
       snake_case    :      Dict      					=    os.path.join(
           cache_dir if cache_dir is not None else args.data_dir						,	F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'						,	)
       snake_case    :      Dict      					=    self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           snake_case					,snake_case    :      str      					=    label_list[2], label_list[1]
       snake_case    :      Any      					=    label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       snake_case    :      Optional[Any]      					=    cached_features_file + ".lock"
       with FileLock(UpperCamelCase__						):
           if os.path.exists(UpperCamelCase__						) and not args.overwrite_cache:
               snake_case    :      List[str]      					=    time.time()
               snake_case    :      Optional[int]      					=    torch.load(UpperCamelCase__						)
               logger.info(
                   F'Loading features from cached file {cached_features_file} [took %.3f s]'						,	time.time() - start						)
           else:
               logger.info(F'Creating features from dataset file at {args.data_dir}'						)

               if mode == Split.dev:
                   snake_case    :      List[str]      					=    self.processor.get_dev_examples(args.data_dir						)
               elif mode == Split.test:
                   snake_case    :      Dict      					=    self.processor.get_test_examples(args.data_dir						)
               else:
                   snake_case    :      Tuple      					=    self.processor.get_train_examples(args.data_dir						)
               if limit_length is not None:
                   snake_case    :      List[Any]      					=    examples[:limit_length]
               snake_case    :      Optional[Any]      					=    glue_convert_examples_to_features(
                   UpperCamelCase__						,	UpperCamelCase__						,	max_length=args.max_seq_length						,	label_list=UpperCamelCase__						,	output_mode=self.output_mode						,	)
               snake_case    :      List[Any]      					=    time.time()
               torch.save(self.features						,	UpperCamelCase__						)
               # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
               logger.info(
                   F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'						)







   def __len__(	self						)	->		Optional[int]:

       '''simple docstring'''




       return len(self.features						)







   def __getitem__(	self						,	UpperCamelCase__						)	->		InputFeatures:

       '''simple docstring'''




       return self.features[i]







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       return self.label_list









"""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						,							)





