


# 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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  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=SCREAMING_SNAKE_CASE , AssumeRolePolicyDocument=json.dumps(SCREAMING_SNAKE_CASE , 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=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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

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






def 	__lowerCAmelCase   ()->  Any:




      """simple docstring"""

      snake_case_	=				_ask_options(
          '''How do you want to authorize?''' , ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '''] , SCREAMING_SNAKE_CASE , )
      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'''] , SCREAMING_SNAKE_CASE , )
      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(SCREAMING_SNAKE_CASE )

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

      snake_case_	=				_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.''' , )
      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 SCREAMING_SNAKE_CASE : str(SCREAMING_SNAKE_CASE ).lower() , )

      snake_case_	=				_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.''' , )
      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 SCREAMING_SNAKE_CASE : str(SCREAMING_SNAKE_CASE ).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=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )

            if use_custom_options:
                  snake_case_	=				_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''' , )
                  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=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE , 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(
                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]:"
            snake_case_	=				_ask_field(SCREAMING_SNAKE_CASE , lambda SCREAMING_SNAKE_CASE : str(SCREAMING_SNAKE_CASE ).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]: ''' , SCREAMING_SNAKE_CASE , 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=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 , )



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				hidden_act='''gelu'''		,				projection_dim=5_12		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       snake_case_	=				{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				self.get_dummy_inputs(_UpperCAmelCase )
       snake_case_	=				sd_pipe(**_UpperCAmelCase ).images
       snake_case_	=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

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





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







@slow
@require_torch_gpu
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase		,				safety_checker=_UpperCAmelCase )
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				torch_dtype=torch.floataa		,				safety_checker=_UpperCAmelCase		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 5E-1





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np'''		,				)

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9