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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							botoa.client('iam'       )
				return iam_client.get_role(RoleName=lowerCamelCase       )["Role"]["Arn"]







def   a_						(					):
				UpperCAmelCase__             =							_ask_options(
				    'How do you want to authorize?'					,			['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']					,			lowerCamelCase					,			)
				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']					,			lowerCamelCase					,			)
				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(lowerCamelCase       )

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

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

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

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






'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @property
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=3					,							out_channels=3					,							down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,							up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,							)
      return model


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""					,							return_dict=__SCREAMING_SNAKE_CASE			)[0]

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE     =      image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

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







@slow
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """google/ddpm-cifar10-32"""

      __SCREAMING_SNAKE_CASE     =      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]			)

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






