






"""simple docstring"""



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        lowercase__      (							snake_case__,       snake_case__,       unittest.TestCase       ):
   _UpperCAmelCase		:Tuple             =			IFInpaintingSuperResolutionPipeline
   _UpperCAmelCase		:Dict             =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
   _UpperCAmelCase		:Optional[Any]             =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
   _UpperCAmelCase		:List[str]             =			PipelineTesterMixin.required_optional_params - {"latents"}


   def      UpperCAmelCase__						(				self					:  int ):
          return self._get_superresolution_dummy_components()


   def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, Any]=0 ):
          if str(snake_case__ ).startswith("mps" ):
                 lowerCamelCase_	:							Any									=torch.manual_seed(snake_case__ )
          else:
                 lowerCamelCase_	:							str									=torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )

          lowerCamelCase_	:							int									=floats_tensor((1, 3, 16, 16)       ,	rng=random.Random(snake_case__ ) ).to(snake_case__ )
          lowerCamelCase_	:							Any									=floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(snake_case__ ) ).to(snake_case__ )
          lowerCamelCase_	:							Optional[Any]									=floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(snake_case__ ) ).to(snake_case__ )

          lowerCamelCase_	:							Any									={
              "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      UpperCAmelCase__						(				self					:  List[str] ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


   def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
          self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != "cuda"       ,	reason="float16 requires CUDA" )
   def      UpperCAmelCase__						(				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      UpperCAmelCase__						(				self					:  Tuple ):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


   def      UpperCAmelCase__						(				self					:  int ):
          self._test_save_load_local()


   def      UpperCAmelCase__						(				self					:  str ):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2       ,	)











"""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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Optional[int]									=ArgumentParser("Accelerate CLI tool"    ,						usage="accelerate <command> [<args>]"    ,						allow_abbrev=lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=parser.add_subparsers(help="accelerate command helpers"  )

							# Register commands
							get_config_parser(subparsers=lowerCamelCase__  )
							env_command_parser(subparsers=lowerCamelCase__  )
							launch_command_parser(subparsers=lowerCamelCase__  )
							tpu_command_parser(subparsers=lowerCamelCase__  )
							test_command_parser(subparsers=lowerCamelCase__  )

							# Let's go
							lowerCamelCase_	:							int									=parser.parse_args()

							if not hasattr(lowerCamelCase__    ,						"func"  ):
														parser.print_help()
														exit(1  )

							# Run
							args.func(lowerCamelCase__  )


if __name__ == "__main__":
						main()



