






# 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       lowercase__	(					A_:						Union[str, Any]		)	->				List[Any]:







       """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=A_  ,      AssumeRolePolicyDocument=json.dumps(A_  ,      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=A_  ,      PolicyName=F'''{role_name}_policy_permission'''  ,      PolicyDocument=json.dumps(A_  ,      indent=2		)  ,      )
       except iam_client.exceptions.EntityAlreadyExistsException:
              print(F'''role {role_name} already exists. Using existing one'''		)







def       lowercase__	(					A_:						Dict		)	->				Any:







       """simple docstring"""






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







def       lowercase__	(					)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=_ask_options(
           """How do you want to authorize?"""  ,      ["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]  ,      A_  ,      )
       __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"""]  ,      A_  ,      )
       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(A_		)

       __UpperCAmelCase     				=_ask_field(
           """Do you want to use custom Docker image? [yes/NO]: """  ,      _convert_yes_no_to_bool  ,      default=A_  ,      error_message="""Please enter yes or no."""  ,      )
       __UpperCAmelCase     				=None
       if is_custom_docker_image:
              __UpperCAmelCase     				=_ask_field("""Enter your Docker image: """  ,      lambda A_		: str(A_		).lower()		)

       __UpperCAmelCase     				=_ask_field(
           """Do you want to provide SageMaker input channels with data locations? [yes/NO]: """  ,      _convert_yes_no_to_bool  ,      default=A_  ,      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 A_		: str(A_		).lower()  ,      )

       __UpperCAmelCase     				=_ask_field(
           """Do you want to enable SageMaker metrics? [yes/NO]: """  ,      _convert_yes_no_to_bool  ,      default=A_  ,      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 A_		: str(A_		).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=A_  ,      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=A_  ,      error_message="""Please enter yes or no."""  ,      )

              if use_custom_options:
                     __UpperCAmelCase     				=_ask_options(
                         """Which mode do you want to use?"""  ,      A_  ,      lambda A_		: TORCH_DYNAMO_MODES[int(A_		)]  ,      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=A_  ,      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=A_  ,      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(
                  A_  ,      A_  ,      lambda A_		: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(A_		)]		)
       else:
              eca_instance_query += "? [ml.p3.2xlarge]:"
              __UpperCAmelCase     				=_ask_field(A_  ,      lambda A_		: str(A_		).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]: """  ,      A_  ,      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=A_  ,      compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER  ,      distributed_type=A_  ,      use_cpu=A_  ,      dynamo_config=A_  ,      eca_instance_type=A_  ,      profile=A_  ,      region=A_  ,      iam_role_name=A_  ,      mixed_precision=A_  ,      num_machines=A_  ,      sagemaker_inputs_file=A_  ,      sagemaker_metrics_file=A_  ,      )







import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images

				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-1






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_dpmpp_2m"						)

				SCREAMING_SNAKE_CASE :   str					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   output_type="np"   ,   use_karras_sigmas=a   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							output.images

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2