






'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=CanineTokenizer
      __a  					=False




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         super().setUp()
         __a				     =    CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         return CanineTokenizer.from_pretrained('google/canine-s'      )




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



         '''simple docstring'''
         __a				     =    self.tokenizer_class.from_pretrained(self.tmpdirname ,   **lowerCamelCase      )
         __a				     =    1024
         return tokenizer




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
         # fmt: off
         __a				     =    [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
         # fmt: on
         __a				     =    tokenizer(lowerCamelCase ,   padding=lowerCamelCase ,   return_tensors='pt'      )
         self.assertIsInstance(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    list(batch.input_ids.numpy()[0]      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         self.assertEqual((2, 39) ,   batch.input_ids.shape      )
         self.assertEqual((2, 39) ,   batch.attention_mask.shape      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
         __a				     =    tokenizer(lowerCamelCase ,   padding=lowerCamelCase ,   return_tensors='pt'      )
         # check if input_ids, attention_mask and token_type_ids are returned
         self.assertIn('input_ids' ,   lowerCamelCase      )
         self.assertIn('attention_mask' ,   lowerCamelCase      )
         self.assertIn('token_type_ids' ,   lowerCamelCase      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    [
             'What\'s the weater?',
             'It\'s about 25 degrees.',
         ]
         __a				     =    tokenizer(
             text_target=lowerCamelCase ,   max_length=32 ,   padding='max_length' ,   truncation=lowerCamelCase ,   return_tensors='pt'      )
         self.assertEqual(32 ,   targets['input_ids'].shape[1]      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               self.assertNotEqual(tokenizer.model_max_length ,   42      )

        # Now let's start the test
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # Isolate this from the other tests because we save additional tokens/etc
               __a				     =    tempfile.mkdtemp()

               __a				     =    ' He is very happy, UNwant\u00E9d,running'
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               tokenizer.save_pretrained(lowerCamelCase      )

               __a				     =    tokenizer.__class__.from_pretrained(lowerCamelCase      )
               __a				     =    after_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

               shutil.rmtree(lowerCamelCase      )

         __a				     =    self.get_tokenizers(model_max_length=42      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # Isolate this from the other tests because we save additional tokens/etc
               __a				     =    tempfile.mkdtemp()

               __a				     =    ' He is very happy, UNwant\u00E9d,running'

               __a				     =    tokenizer.additional_special_tokens

               # We can add a new special token for Canine as follows:
               __a				     =    chr(0xe007      )
               additional_special_tokens.append(lowerCamelCase      )
               tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               tokenizer.save_pretrained(lowerCamelCase      )

               __a				     =    tokenizer.__class__.from_pretrained(lowerCamelCase      )
               __a				     =    after_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
               self.assertIn(lowerCamelCase ,   after_tokenizer.additional_special_tokens      )
               self.assertEqual(after_tokenizer.model_max_length ,   42      )

               __a				     =    tokenizer.__class__.from_pretrained(lowerCamelCase ,   model_max_length=43      )
               self.assertEqual(tokenizer.model_max_length ,   43      )

               shutil.rmtree(lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a     ,			__a				     =    self.get_clean_sequence(lowerCamelCase      )

               # a special token for Canine can be defined as follows:
               __a				     =    0xe005
               __a				     =    chr(lowerCamelCase      )

               tokenizer.add_special_tokens({'cls_token': special_token}      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertEqual(len(lowerCamelCase      ) ,   1      )

               __a				     =    tokenizer.decode(ids + encoded_special_token ,   clean_up_tokenization_spaces=lowerCamelCase      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )

               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertEqual(lowerCamelCase ,   input_encoded + special_token_id      )

               __a				     =    tokenizer.decode(lowerCamelCase ,   skip_special_tokens=lowerCamelCase      )
               self.assertTrue(special_token not in decoded      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    chr(0xe005      )
               __a				     =    chr(0xe006      )

               # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
               tokenizer.add_tokens([SPECIAL_TOKEN_1] ,   special_tokens=lowerCamelCase      )
               # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
               # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
               tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}      )

               __a				     =    tokenizer.tokenize(lowerCamelCase      )
               __a				     =    tokenizer.tokenize(lowerCamelCase      )

               self.assertEqual(len(lowerCamelCase      ) ,   1      )
               self.assertEqual(len(lowerCamelCase      ) ,   1      )
               self.assertEqual(token_a[0] ,   lowerCamelCase      )
               self.assertEqual(token_a[0] ,   lowerCamelCase      )




      @require_tokenizers
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # a special token for Canine can be defined as follows:
               __a				     =    0xe006
               __a				     =    chr(lowerCamelCase      )

               __a				     =    AddedToken(lowerCamelCase ,   lstrip=lowerCamelCase      )
               tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}      )

               with tempfile.TemporaryDirectory() as tmp_dir_name:
                  tokenizer.save_pretrained(lowerCamelCase      )
                  tokenizer.from_pretrained(lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    []
         if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

         if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer_utils.save_pretrained(lowerCamelCase      )

               with open(os.path.join(lowerCamelCase ,   'special_tokens_map.json'      ) ,   encoding='utf-8'      ) as json_file:
                  __a				     =    json.load(lowerCamelCase      )

               with open(os.path.join(lowerCamelCase ,   'tokenizer_config.json'      ) ,   encoding='utf-8'      ) as json_file:
                  __a				     =    json.load(lowerCamelCase      )

               # a special token for Canine can be defined as follows:
               __a				     =    0xe006
               __a				     =    chr(lowerCamelCase      )

               __a				     =    [new_token_a]
               __a				     =    [new_token_a]

               with open(os.path.join(lowerCamelCase ,   'special_tokens_map.json'      ) ,   'w' ,   encoding='utf-8'      ) as outfile:
                  json.dump(lowerCamelCase ,   lowerCamelCase      )
               with open(os.path.join(lowerCamelCase ,   'tokenizer_config.json'      ) ,   'w' ,   encoding='utf-8'      ) as outfile:
                  json.dump(lowerCamelCase ,   lowerCamelCase      )

               # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
               # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
               # "special_tokens_map.json" files
               __a				     =    tokenizer_class.from_pretrained(lowerCamelCase ,   extra_ids=0      )
               self.assertIn(lowerCamelCase ,   tokenizer_without_change_in_init.additional_special_tokens      )
               # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a] ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                       tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]      )      ) ,   )

               __a				     =    0xe007
               __a				     =    chr(lowerCamelCase      )
               # Now we test that we can change the value of additional_special_tokens in the from_pretrained
               __a				     =    [AddedToken(lowerCamelCase ,   lstrip=lowerCamelCase      )]
               __a				     =    tokenizer_class.from_pretrained(
                   lowerCamelCase ,   additional_special_tokens=lowerCamelCase ,   extra_ids=0      )

               self.assertIn(lowerCamelCase ,   tokenizer.additional_special_tokens      )
               # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a] ,   tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]      )      )      )




      @require_tokenizers
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    'hello world'
               if self.space_between_special_tokens:
                  __a				     =    '[CLS] hello world [SEP]'
               else:
                  __a				     =    input
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               __a				     =    tokenizer.decode(lowerCamelCase ,   spaces_between_special_tokens=self.space_between_special_tokens      )
               self.assertIn(lowerCamelCase ,   [output, output.lower()]      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    [
                   'bos_token',
                   'eos_token',
                   'unk_token',
                   'sep_token',
                   'pad_token',
                   'cls_token',
                   'mask_token',
               ]

         __a				     =    'a'
         __a				     =    ord(lowerCamelCase      )

         for attr in attributes_list:
            setattr(lowerCamelCase ,   attr + '_id' ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   lowerCamelCase      ) ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   attr + '_id'      ) ,   lowerCamelCase      )

            setattr(lowerCamelCase ,   attr + '_id' ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   lowerCamelCase      ) ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   attr + '_id'      ) ,   lowerCamelCase      )

         setattr(lowerCamelCase ,   'additional_special_tokens_ids' ,   []      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens'      ) ,   []      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens_ids'      ) ,   []      )

         __a				     =    0xe006
         __a				     =    chr(lowerCamelCase      )
         setattr(lowerCamelCase ,   'additional_special_tokens_ids' ,   [additional_special_token_id]      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens'      ) ,   [additional_special_token]      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens_ids'      ) ,   [additional_special_token_id]      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass






      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         pass







'''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__,					)