# 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 dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


_lowerCamelCase								=os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_lowerCamelCase								=os.path.join(hf_cache_home, "accelerate")
_lowerCamelCase								=os.path.join(cache_dir, "default_config.yaml")
_lowerCamelCase								=os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
 _lowerCamelCase								=default_yaml_config_file
else:
 _lowerCamelCase								=default_json_config_file







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     if config_file is not None:
          if not os.path.isfile(lowerCAmelCase_      ):
               raise FileNotFoundError(
                   F'The passed configuration file `{config_file}` does not exist. '
                   'Please pass an existing file to `accelerate launch`, or use the the default one '
                   'created through `accelerate config` and run `accelerate launch` '
                   'without the `--config_file` argument.'      )
     else:
          SCREAMING_SNAKE_CASE										=default_config_file
     with open(lowerCAmelCase_,   'r',   encoding='utf-8'      ) as f:
          if config_file.endswith('.json'      ):
               if (
                   json.load(lowerCAmelCase_      ).get('compute_environment',   ComputeEnvironment.LOCAL_MACHINE      )
                   == ComputeEnvironment.LOCAL_MACHINE
               ):
                    SCREAMING_SNAKE_CASE										=ClusterConfig
               else:
                    SCREAMING_SNAKE_CASE										=SageMakerConfig
               return config_class.from_json_file(json_file=lowerCAmelCase_      )
          else:
               if (
                   yaml.safe_load(lowerCAmelCase_      ).get('compute_environment',   ComputeEnvironment.LOCAL_MACHINE      )
                   == ComputeEnvironment.LOCAL_MACHINE
               ):
                    SCREAMING_SNAKE_CASE										=ClusterConfig
               else:
                    SCREAMING_SNAKE_CASE										=SageMakerConfig
               return config_class.from_yaml_file(yaml_file=lowerCAmelCase_      )



@dataclass
class 		a_	:



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.__dict__
           # For serialization, it's best to convert Enums to strings (or their underlying value type).
           for key, value in result.items():
                if isinstance(snake_case      ,snake_case    ):
                     SCREAMING_SNAKE_CASE										=value.value
                if isinstance(snake_case      ,snake_case    ) and not bool(snake_case    ):
                     SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										={k: v for k, v in result.items() if v is not None}
           return result
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							List[Any]      ,snake_case					:							Optional[int]=None    ):
           SCREAMING_SNAKE_CASE										=default_json_config_file if json_file is None else json_file
           with open(snake_case      ,'r'      ,encoding='utf-8'    ) as f:
                SCREAMING_SNAKE_CASE										=json.load(snake_case    )
           if "compute_environment" not in config_dict:
                SCREAMING_SNAKE_CASE										=ComputeEnvironment.LOCAL_MACHINE
           if "mixed_precision" not in config_dict:
                SCREAMING_SNAKE_CASE										='fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
           if "fp16" in config_dict:  # Convert the config to the new format.
                del config_dict["fp16"]
           if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                SCREAMING_SNAKE_CASE										=config_dict.pop('dynamo_backend'    )
                SCREAMING_SNAKE_CASE										={} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
           if "use_cpu" not in config_dict:
                SCREAMING_SNAKE_CASE										=False
           return cls(**snake_case    )
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							Dict    ):
           with open(snake_case      ,'w'      ,encoding='utf-8'    ) as f:
                SCREAMING_SNAKE_CASE										=json.dumps(self.to_dict()      ,indent=2      ,sort_keys=snake_case    ) + '\n'
                f.write(snake_case    )
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							int      ,snake_case					:							Optional[Any]=None    ):
           SCREAMING_SNAKE_CASE										=default_yaml_config_file if yaml_file is None else yaml_file
           with open(snake_case      ,'r'      ,encoding='utf-8'    ) as f:
                SCREAMING_SNAKE_CASE										=yaml.safe_load(snake_case    )
           if "compute_environment" not in config_dict:
                SCREAMING_SNAKE_CASE										=ComputeEnvironment.LOCAL_MACHINE

           if "mixed_precision" not in config_dict:
                SCREAMING_SNAKE_CASE										='fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
           if isinstance(config_dict['mixed_precision']      ,snake_case    ) and not config_dict["mixed_precision"]:
                SCREAMING_SNAKE_CASE										='no'
           if "fp16" in config_dict:  # Convert the config to the new format.
                del config_dict["fp16"]
           if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                SCREAMING_SNAKE_CASE										=config_dict.pop('dynamo_backend'    )
                SCREAMING_SNAKE_CASE										={} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
           if "use_cpu" not in config_dict:
                SCREAMING_SNAKE_CASE										=False
           return cls(**snake_case    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							int    ):
           with open(snake_case      ,'w'      ,encoding='utf-8'    ) as f:
                yaml.safe_dump(self.to_dict()      ,snake_case    )


      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           if isinstance(self.compute_environment      ,snake_case    ):
                SCREAMING_SNAKE_CASE										=ComputeEnvironment(self.compute_environment    )
           if isinstance(self.distributed_type      ,snake_case    ):
                if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                     SCREAMING_SNAKE_CASE										=SageMakerDistributedType(self.distributed_type    )
                else:
                     SCREAMING_SNAKE_CASE										=DistributedType(self.distributed_type    )
           if self.dynamo_config is None:
                SCREAMING_SNAKE_CASE										={}







@dataclass
class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				0
      __UpperCAmelCase      		=				1
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				'static'
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				'main'

      # args for deepspeed_plugin
      __UpperCAmelCase      		=				None
      # args for fsdp
      __UpperCAmelCase      		=				None
      # args for megatron_lm
      __UpperCAmelCase      		=				None
      # args for ipex
      __UpperCAmelCase      		=				None
      # args for TPU
      __UpperCAmelCase      		=				False

      # args for TPU pods
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None

      # args for dynamo
      __UpperCAmelCase      		=				None
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           if self.deepspeed_config is None:
                SCREAMING_SNAKE_CASE										={}
           if self.fsdp_config is None:
                SCREAMING_SNAKE_CASE										={}
           if self.megatron_lm_config is None:
                SCREAMING_SNAKE_CASE										={}
           if self.ipex_config is None:
                SCREAMING_SNAKE_CASE										={}
           return super().__post_init__()







@dataclass
class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				'us-east-1'
      __UpperCAmelCase      		=				1
      __UpperCAmelCase      		=				'all'
      __UpperCAmelCase      		=				F'''accelerate-sagemaker-{num_machines}'''
      __UpperCAmelCase      		=				SAGEMAKER_PYTORCH_VERSION
      __UpperCAmelCase      		=				SAGEMAKER_TRANSFORMERS_VERSION
      __UpperCAmelCase      		=				SAGEMAKER_PYTHON_VERSION
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           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')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

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

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

