





# 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     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=botoa.client("iam"  )

   UpperCAmelCase_											={
       "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  )  )
      UpperCAmelCase_											={
          "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     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=botoa.client("iam"  )
   return iam_client.get_role(RoleName=lowercase__  )["Role"]["Arn"]





def     a__ (					):






   '''simple docstring'''






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

      UpperCAmelCase_											=_ask_field("AWS Secret Access Key: "  )
      UpperCAmelCase_											=aws_secret_access_key

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

   UpperCAmelCase_											=_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:
      UpperCAmelCase_											=_ask_field("Enter your IAM role name: "  )
   else:
      UpperCAmelCase_											="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__  )

   UpperCAmelCase_											=_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."  ,    )
   UpperCAmelCase_											=None
   if is_custom_docker_image:
      UpperCAmelCase_											=_ask_field("Enter your Docker image: "  ,    lambda lowercase__  : str(lowercase__  ).lower()  )

   UpperCAmelCase_											=_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."  ,    )
   UpperCAmelCase_											=None
   if is_sagemaker_inputs_enabled:
      UpperCAmelCase_											=_ask_field(
          "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "  ,    lambda lowercase__  : str(lowercase__  ).lower()  ,    )

   UpperCAmelCase_											=_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."  ,    )
   UpperCAmelCase_											=None
   if is_sagemaker_metrics_enabled:
      UpperCAmelCase_											=_ask_field(
          "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "  ,    lambda lowercase__  : str(lowercase__  ).lower()  ,    )

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

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

   UpperCAmelCase_											=_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__  ,    )











import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


__lowercase  :   Any						      ="""hf-internal-testing/tiny-random-bert"""
__lowercase  :   List[str]						      =os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
__lowercase  :   Dict						      ="""9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""

class      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  _lowerCAmelCase       )
         # Should have downloaded the file in here
         self.assertTrue(os.path.isdir(_lowerCAmelCase       )       )
         # Cache should contain at least those three subfolders:
         for subfolder in ["blobs", "refs", "snapshots"]:
            self.assertTrue(os.path.isdir(os.path.join(_lowerCAmelCase		,  _lowerCAmelCase       )       )       )
         with open(os.path.join(_lowerCAmelCase		,  "refs"		,  "main"       )       ) as f:
            UpperCAmelCase_											=f.read()
         self.assertEqual(_lowerCAmelCase		,  os.path.join(_lowerCAmelCase		,  "snapshots"		,  _lowerCAmelCase		,  _lowerCAmelCase       )       )
         self.assertTrue(os.path.isfile(_lowerCAmelCase       )       )

         # File is cached at the same place the second time.
         UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  _lowerCAmelCase       )
         self.assertEqual(_lowerCAmelCase		,  _lowerCAmelCase       )

         # Using a specific revision to test the full commit hash.
         UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  _lowerCAmelCase		,  revision="9b8c223"       )
         self.assertEqual(_lowerCAmelCase		,  os.path.join(_lowerCAmelCase		,  "snapshots"		,  _lowerCAmelCase		,  _lowerCAmelCase       )       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         with self.assertRaisesRegex(_lowerCAmelCase		,  "is not a valid model identifier"       ):
            UpperCAmelCase_											=cached_file("tiny-random-bert"		,  _lowerCAmelCase       )

         with self.assertRaisesRegex(_lowerCAmelCase		,  "is not a valid git identifier"       ):
            UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  _lowerCAmelCase		,  revision="aaaa"       )

         with self.assertRaisesRegex(_lowerCAmelCase		,  "does not appear to have a file named"       ):
            UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  "conf"       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Optional[Any]:
         '''simple docstring'''

         with self.assertRaisesRegex(_lowerCAmelCase		,  "does not appear to have a file named"       ):
            UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  "conf"       )

         with open(os.path.join(_lowerCAmelCase		,  "refs"		,  "main"       )       ) as f:
            UpperCAmelCase_											=f.read()
         self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase		,  ".no_exist"		,  _lowerCAmelCase		,  "conf"       )       )       )

         UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  "conf"		,  _raise_exceptions_for_missing_entries=_lowerCAmelCase       )
         self.assertIsNone(_lowerCAmelCase       )

         UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  "conf"		,  local_files_only=_lowerCAmelCase		,  _raise_exceptions_for_missing_entries=_lowerCAmelCase       )
         self.assertIsNone(_lowerCAmelCase       )

         UpperCAmelCase_											=mock.Mock()
         UpperCAmelCase_											=500
         UpperCAmelCase_											={}
         UpperCAmelCase_											=HTTPError
         UpperCAmelCase_											={}

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch("requests.Session.request"		,  return_value=_lowerCAmelCase       ) as mock_head:
            UpperCAmelCase_											=cached_file(_lowerCAmelCase		,  "conf"		,  _raise_exceptions_for_connection_errors=_lowerCAmelCase       )
            self.assertIsNone(_lowerCAmelCase       )
            # This check we did call the fake head request
            mock_head.assert_called()



      def 	lowerCAmelCase__    (	self:						Any       )						->    Tuple:
         '''simple docstring'''

         self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only"		,  _lowerCAmelCase       )       )
         self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"		,  _lowerCAmelCase       )       )
         self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"		,  _lowerCAmelCase       )       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    Dict:
         '''simple docstring'''

         self.assertIsNone(get_file_from_repo("bert-base-cased"		,  "ahah.txt"       )       )

         # The function raises if the repository does not exist.
         with self.assertRaisesRegex(_lowerCAmelCase		,  "is not a valid model identifier"       ):
            get_file_from_repo("bert-base-case"		,  _lowerCAmelCase       )

         # The function raises if the revision does not exist.
         with self.assertRaisesRegex(_lowerCAmelCase		,  "is not a valid git identifier"       ):
            get_file_from_repo("bert-base-cased"		,  _lowerCAmelCase		,  revision="ahaha"       )

         UpperCAmelCase_											=get_file_from_repo("bert-base-cased"		,  _lowerCAmelCase       )
         # The name is the cached name which is not very easy to test, so instead we load the content.
         UpperCAmelCase_											=json.loads(open(_lowerCAmelCase		,  "r"       ).read()       )
         self.assertEqual(config["hidden_size"]		,  768       )






      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Dict:
         '''simple docstring'''

         with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_											=Path(_lowerCAmelCase       ) / "a.txt"
            filename.touch()
            self.assertEqual(get_file_from_repo(_lowerCAmelCase		,  "a.txt"       )		,  str(_lowerCAmelCase       )       )

            self.assertIsNone(get_file_from_repo(_lowerCAmelCase		,  "b.txt"       )       )




