






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      UpperCAmelCase_		(   lowercase,      lowercase,      unittest.TestCase					):



      """simple docstring"""
      UpperCamelCase_							:   Optional[int] 	=IFInpaintingPipeline
      UpperCamelCase_							:   str 	=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
      UpperCamelCase_							:   Optional[Any] 	=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase_							:   Optional[Any] 	=PipelineTesterMixin.required_optional_params - {'latents'}
      def        UpperCAmelCase		(							self					)							-> Optional[Any]:
          return self._get_dummy_components()
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=0					)							-> int:







          if str(__lowercase					).startswith('''mps'''					):
              UpperCamelCase			:Optional[int]      				=  torch.manual_seed(__lowercase					)
          else:
              UpperCamelCase			:int      				=  torch.Generator(device=__lowercase					).manual_seed(__lowercase					)

          UpperCamelCase			:Any      				=  floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(__lowercase					)					).to(__lowercase					)
          UpperCamelCase			:List[str]      				=  floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(__lowercase					)					).to(__lowercase					)

          UpperCamelCase			:str      				=  {
              '''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:
          super().test_save_load_floataa(expected_max_diff=1e-1					)
      def        UpperCAmelCase		(							self					)							-> Optional[int]:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)
      def        UpperCAmelCase		(							self					)							-> Union[str, Any]:
          self._test_save_load_local()




      def        UpperCAmelCase		(							self					)							-> int:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2       ,							)








'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=A )
class 	lowerCAmelCase  (   A ):
       # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
       lowerCAmelCase_												=			field(default="summarization" ,      metadata={"include_in_asdict_even_if_is_default": True} )
       lowerCAmelCase_												=			Features({"text": Value("string" )} )
       lowerCAmelCase_												=			Features({"summary": Value("string" )} )
       lowerCAmelCase_												=			"text"
       lowerCAmelCase_												=			"summary"







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            return {self.text_column: "text", self.summary_column: "summary"}


