






'''simple docstring'''



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase      :       Optional[int]      			=      logging.getLogger()



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Path,					SCREAMING_SNAKE_CASE__:	list    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    """\n""".join(_lowerCamelCase    )
   Path(_lowerCamelCase    ).open('w'    ).writelines(_lowerCamelCase    )


__UpperCamelCase      :       List[Any]      			=      """patrickvonplaten/t5-tiny-random"""
__UpperCamelCase      :       str      			=      """sshleifer/bart-tiny-random"""
__UpperCamelCase      :       str      			=      """sshleifer/tiny-mbart"""

__UpperCamelCase      :       int      			=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class 							__SCREAMING_SNAKE_CASE       (							__snake_case     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    Path(self.get_auto_remove_tmp_dir()      ) / """utest_input.source"""
         __a				     =    input_file_name.parent / """utest_output.txt"""
         assert not output_file_name.exists()
         __a				     =    [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
         _dump_articles(_lowercase ,   _lowercase      )

         __a				     =    str(Path(self.get_auto_remove_tmp_dir()      ) / 'scores.json'      )
         __a				     =    """translation_en_to_de""" if model == T5_TINY else """summarization"""
         __a				     =    F"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

         with patch.object(_lowercase ,   'argv' ,   _lowercase      ):
            run_generate()
            assert Path(_lowercase      ).exists()
            # os.remove(Path(output_file_name))




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



         '''simple docstring'''
         self.run_eval_tester(_lowercase      )




      @parameterized.expand([BART_TINY, MBART_TINY]      )
      @slow
      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->Dict:



         '''simple docstring'''
         self.run_eval_tester(_lowercase      )






      @parameterized.expand([T5_TINY, MBART_TINY]      )
      @slow
      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         __a				     =    Path(self.get_auto_remove_tmp_dir()      ) / """utest_input.source"""
         __a				     =    input_file_name.parent / """utest_output.txt"""
         assert not output_file_name.exists()

         __a				     =    {
             """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
             """de""": [
                 """Maschinelles Lernen ist großartig, oder?""",
                 """Ich esse gerne Bananen""",
                 """Morgen ist wieder ein toller Tag!""",
             ],
         }

         __a				     =    Path(self.get_auto_remove_tmp_dir()      )
         __a				     =    str(tmp_dir / 'scores.json'      )
         __a				     =    str(tmp_dir / 'val.target'      )
         _dump_articles(_lowercase ,   text['en']      )
         _dump_articles(_lowercase ,   text['de']      )
         __a				     =    """translation_en_to_de""" if model == T5_TINY else """summarization"""
         __a				     =    F"""
            run_eval_search.py
            {model}
            {str(_lowercase      )}
            {str(_lowercase      )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
         testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']      )

         with patch.object(_lowercase ,   'argv' ,   _lowercase      ):
            with CaptureStdout() as cs:
               run_search()
            __a				     =    [""" num_beams | length_penalty""", model, """Best score args"""]
            __a				     =    ["""Info"""]
            if "translation" in task:
               expected_strings.append('bleu'      )
            else:
               expected_strings.extend(_lowercase      )
            for w in expected_strings:
               assert w in cs.out
            for w in un_expected_strings:
               assert w not in cs.out
            assert Path(_lowercase      ).exists()
            os.remove(Path(_lowercase      )      )







'''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__,					)