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    ):
    __a  =      IFInpaintingPipeline
    __a  =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
    __a  =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __a  =      PipelineTesterMixin.required_optional_params - {"latents"}
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     return self._get_dummy_components()
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0   )       ->					Dict:
     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   Tuple=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   int=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      {
         '''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 						UpperCamelCase_				(  self   )       ->					List[str]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA'''   )
    def 						UpperCamelCase_				(  self   )       ->					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   )       ->					Tuple:
     self._test_save_load_local()



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     self._test_inference_batch_single_identical(
         expected_max_diff=1e-2 , )


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowercase_     :   str 				=				re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      None
    __a  =      None
    __a  =      None
    __a  =      None
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      _str_to_version_tuple(self.version_str   )
    def __repr__(  self   )       ->					Dict:
     return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     return self.major, self.minor, self.patch
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return Version(lowerCAmelCase   )
     elif isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return other
     raise TypeError(f'{other} (type {type(lowerCAmelCase   )}) cannot be compared to version.'   )
    def __eq__(  self , lowerCAmelCase   )       ->					Optional[int]:
     try:
      SCREAMING_SNAKE_CASE__:   List[str]=      self._validate_operand(lowerCAmelCase   )
     except (TypeError, ValueError):
      return False
     else:
      return self.tuple == other.tuple
    def __lt__(  self , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self._validate_operand(lowerCAmelCase   )
     return self.tuple < other.tuple
    def __hash__(  self   )       ->					List[Any]:
     return hash(_version_tuple_to_str(self.tuple   )   )
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Dict=      {f.name for f in dataclasses.fields(cls   )}
     return cls(**{k: v for k, v in dic.items() if k in field_names}   )



    def 						UpperCamelCase_				(  self   )       ->					str:
     return self.version_str







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   List[Any]=      _VERSION_REG.match(snake_case_      )
 if not res:
  raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
 return tuple(int(snake_case_      ) for v in [res.group('''major'''      ), res.group('''minor'''      ), res.group('''patch'''      )]      )







def 			A__      (	snake_case_ :		Union[str, Any]      ):
 return ".".join(str(snake_case_      ) for v in version_tuple      )

