


'''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      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  IFInpaintingSuperResolutionPipeline
  _lowerCamelCase		    =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
  _lowerCamelCase		    =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}			)
  _lowerCamelCase		    =  PipelineTesterMixin.required_optional_params - {"latents"}






  def   snake_case		(    self     ):







   """simple docstring"""
   return self._get_superresolution_dummy_components()






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(UpperCamelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(UpperCamelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )

   lowerCamelCase_      =  floats_tensor((1, 3, 16, 16)			,  rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
   lowerCamelCase_      =  floats_tensor((1, 3, 32, 32)			,  rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
   lowerCamelCase_      =  floats_tensor((1, 3, 32, 32)			,  rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )

   lowerCamelCase_      =  {
       "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   snake_case		(    self     ):







   """simple docstring"""
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self._test_save_load_optional_components()






  @unittest.skipIf(torch_device != "cuda"			,  reason="float16 requires CUDA"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   # 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   snake_case		(    self     ):







   """simple docstring"""
   self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self._test_save_load_local()







  def   snake_case		(    self     ):







   """simple docstring"""
   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.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser



def 	a			( lowerCamelCase__=None    ):
			'''simple docstring'''





			A_	:						int	=     argparse.ArgumentParser(add_help=lowerCamelCase__						,      allow_abbrev=lowerCamelCase__    )
			# The main config parser
			A_	:						int	=     config_command_parser(lowerCamelCase__    )
			# The subparser to add commands to
			A_	:						int	=     config_parser.add_subparsers(title="""subcommands"""						,      dest="""subcommand"""    )

			# Then add other parsers with the parent parser
			default_command_parser(lowerCamelCase__						,      parents=[parent_parser]    )
			update_command_parser(lowerCamelCase__						,      parents=[parent_parser]    )

			return config_parser



def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     get_config_parser()
			A_	:						List[str]	=     config_parser.parse_args()

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

			# Run
			args.func(lowerCamelCase__    )


if __name__ == "__main__":
						main()