




'''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			(_lowerCamelCase				:  Tuple						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	botoa.client('''iam'''						)

     __snake_case            =	{
         '''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						)						)
          __snake_case            =	{
              '''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       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Any:




     '''simple docstring'''





     __snake_case            =	botoa.client('''iam'''						)
     return iam_client.get_role(RoleName=_lowerCamelCase						)["Role"]["Arn"]







def       _UpperCamelCase			()-> Dict:




     '''simple docstring'''





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

          __snake_case            =	_ask_field('''AWS Secret Access Key: '''						)
          __snake_case            =	aws_secret_access_key

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

     __snake_case            =	_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:
          __snake_case            =	_ask_field('''Enter your IAM role name: '''						)
     else:
          __snake_case            =	'''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						)

     __snake_case            =	_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.''' ,						)
     __snake_case            =	None
     if is_custom_docker_image:
          __snake_case            =	_ask_field('''Enter your Docker image: ''' ,						lambda _lowerCamelCase						: str(_lowerCamelCase						).lower()						)

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

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

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

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

     __snake_case            =	_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 warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_							:	Tuple	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     warnings.warn(
         '''The preprocess method is deprecated and will be removed in a future version. Please'''
         ''' use VaeImageProcessor.preprocess instead''' ,						_lowerCamelCase ,						)
     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return image
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[image]

     if isinstance(image[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	image[0].size
          __snake_case       ,     __snake_case            =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return mask
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[mask]

     if isinstance(mask[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	mask[0].size
          __snake_case       ,     __snake_case            =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          __snake_case            =	[np.array(m.convert('''L'''						).resize((w, h) ,						resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	mask.astype(np.floataa						) / 255.0
          __snake_case            =	0
          __snake_case            =	1
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(mask[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return mask







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE	)}, but requested an effective batch'''
                     F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

            __snake_case            =	self.scheduler.timesteps[0] + 1
            __snake_case            =	generator[0] if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 if t < t_last:
                      # predict the noise residual
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                      # compute previous image: x_t -> x_t-1
                      __snake_case            =	self.scheduler.step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).prev_sample

                 else:
                      # compute the reverse: x_t-1 -> x_t
                      __snake_case            =	self.scheduler.undo_step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	t

            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






