






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[Any]									=IFInpaintingPipeline
       a    :		Any									=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
       a    :		str									=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       a    :		Optional[int]									=PipelineTesterMixin.required_optional_params - {'''latents'''}





       def       _a   ( self							):
         return self._get_dummy_components()





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=0							):
         if str(_lowerCamelCase							).startswith('mps'							):
           UpperCamelCase_: Dict   				=    torch.manual_seed(_lowerCamelCase							)
         else:
           UpperCamelCase_: Tuple   				=    torch.Generator(device=_lowerCamelCase							).manual_seed(_lowerCamelCase							)

         UpperCamelCase_: Any   				=    floats_tensor((1, 3, 3_2, 3_2)  ,      rng=random.Random(_lowerCamelCase							)							).to(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    floats_tensor((1, 3, 3_2, 3_2)  ,      rng=random.Random(_lowerCamelCase							)							).to(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    {
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'mask_image': mask_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs





       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available()  ,      reason='XFormers attention is only available with CUDA and `xformers` installed'  ,      )
       def       _a   ( self							):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)





       def       _a   ( self							):
         self._test_save_load_optional_components()





       @unittest.skipIf(torch_device != 'cuda'  ,      reason='float16 requires CUDA'							)
       def       _a   ( self							):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1							)





       def       _a   ( self							):
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)





       def       _a   ( self							):
         self._test_save_load_local()





       def       _a   ( self							):
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2  ,      )







# 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 argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


A_		:		Any		     =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def   snake_case ()	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    _ask_options(
      'In which compute environment are you running?'     ,   ['This machine', 'AWS (Amazon SageMaker)']     ,   _convert_compute_environment     ,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCamelCase_: List[str]   				=    get_sagemaker_input()
  else:
    UpperCamelCase_: List[str]   				=    get_cluster_input()
  return config

def   snake_case (UpperCAmelCase__=None  )	->    Union[str, Any]:
  if subparsers is not None:
    UpperCamelCase_: List[Any]   				=    subparsers.add_parser('config'     ,   description=UpperCAmelCase__  )
  else:
    UpperCamelCase_: List[Any]   				=    argparse.ArgumentParser('Accelerate config command'     ,   description=UpperCAmelCase__  )

  parser.add_argument(
      '--config_file'     ,   default=UpperCAmelCase__     ,   help=(
          'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )     ,   )

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase__  )
  return parser

def   snake_case (UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    get_user_input()
  if args.config_file is not None:
    UpperCamelCase_: Tuple   				=    args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase__  ):
      os.makedirs(UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    default_yaml_config_file

  if config_file.endswith('.json'  ):
    config.to_json_file(UpperCAmelCase__  )
  else:
    config.to_yaml_file(UpperCAmelCase__  )
  print(F'''accelerate configuration saved at {config_file}'''  )

def   snake_case ()	->    str:
  UpperCamelCase_: Tuple   				=    config_command_parser()
  UpperCamelCase_: int   				=    parser.parse_args()
  config_command(UpperCAmelCase__  )


if __name__ == "__main__":
   main()