

import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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  __UpperCAmelCase					(	_UpperCAmelCase      ,  _UpperCAmelCase      ,  unittest.TestCase						):
     UpperCamelCase       					=					IFInpaintingSuperResolutionPipeline
     UpperCamelCase       					=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
     UpperCamelCase       					=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}						)
     UpperCamelCase       					=					PipelineTesterMixin.required_optional_params - {"""latents"""}



     def 	__magic_name__   (							self							:					Tuple						):
       return self._get_superresolution_dummy_components()



     def 	__magic_name__   (							self							:					List[Any],		__A							:					str,		__A							:					List[Any]=0						):
       if str(lowercase_						).startswith('''mps'''						):
         UpperCAmelCase	:							List[str]						=			torch.manual_seed(lowercase_						)
       else:
         UpperCAmelCase	:							int						=			torch.Generator(device=lowercase_						).manual_seed(lowercase_						)

       UpperCAmelCase	:							List[Any]						=			floats_tensor((1, 3, 1_6, 1_6),		rng=random.Random(lowercase_						)						).to(lowercase_						)
       UpperCAmelCase	:							Dict						=			floats_tensor((1, 3, 3_2, 3_2),		rng=random.Random(lowercase_						)						).to(lowercase_						)
       UpperCAmelCase	:							Optional[int]						=			floats_tensor((1, 3, 3_2, 3_2),		rng=random.Random(lowercase_						)						).to(lowercase_						)

       UpperCAmelCase	:							int						=			{
           """prompt""": """A painting of a squirrel eating a burger""",
           """image""": image,
           """original_image""": original_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 	__magic_name__   (							self							:					Any						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



     def 	__magic_name__   (							self							:					Tuple						):
       self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != '''cuda''',		reason='''float16 requires CUDA'''						)
     def 	__magic_name__   (							self							:					Union[str, Any]						):
       # 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 	__magic_name__   (							self							:					Union[str, Any]						):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



     def 	__magic_name__   (							self							:					str						):
       self._test_save_load_local()





     def 	__magic_name__   (							self							:					Tuple						):
       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


_lowerCamelCase    :       Optional[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       a__					(		)  ->						List[Any]:
  UpperCAmelCase	:							Dict						=			_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[Any]						=			get_sagemaker_input()
  else:
    UpperCAmelCase	:							Optional[Any]						=			get_cluster_input()
  return config

def       a__					(		UpperCAmelCase							:      Union[str, Any]=None      )  ->						List[Any]:
  if subparsers is not None:
    UpperCAmelCase	:							Optional[Any]						=			subparsers.add_parser('''config'''    ,			description=UpperCAmelCase      )
  else:
    UpperCAmelCase	:							List[str]						=			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       a__					(		UpperCAmelCase							:      List[Any]      )  ->						Optional[int]:
  UpperCAmelCase	:							str						=			get_user_input()
  if args.config_file is not None:
    UpperCAmelCase	:							Any						=			args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase      ):
      os.makedirs(UpperCAmelCase      )
    UpperCAmelCase	:							List[str]						=			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       a__					(		)  ->						Dict:
  UpperCAmelCase	:							str						=			config_command_parser()
  UpperCAmelCase	:							str						=			parser.parse_args()
  config_command(UpperCAmelCase      )


if __name__ == "__main__":
     main()





