



import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        UpperCAmelCase__  (    A__							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				RePaintPipeline
 __SCREAMING_SNAKE_CASE					      =				IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
 __SCREAMING_SNAKE_CASE					      =				PipelineTesterMixin.required_optional_params - {
     '''latents''',
     '''num_images_per_prompt''',
     '''callback''',
     '''callback_steps''',
 }
 __SCREAMING_SNAKE_CASE					      =				IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE					      =				False



 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  torch.manual_seed(0			)
  torch.manual_seed(0			)
  __UpperCamelCase				      =  UNetaDModel(
      block_out_channels=(3_2, 6_4)		,	layers_per_block=2		,	sample_size=3_2		,	in_channels=3		,	out_channels=3		,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,	)
  __UpperCamelCase				      =  RePaintScheduler()
  __UpperCamelCase				      =  {"unet": unet, "scheduler": scheduler}
  return components



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=0			)  ->							Tuple:






  if str(lowercase			).startswith("""mps"""			):
   __UpperCamelCase				      =  torch.manual_seed(lowercase			)
  else:
   __UpperCamelCase				      =  torch.Generator(device=lowercase			).manual_seed(lowercase			)
  __UpperCamelCase				      =  np.random.RandomState(lowercase			).standard_normal((1, 3, 3_2, 3_2)			)
  __UpperCamelCase				      =  torch.from_numpy(lowercase			).to(device=lowercase		,	dtype=torch.floataa			)
  __UpperCamelCase				      =  (image > 0).to(device=lowercase		,	dtype=torch.floataa			)
  __UpperCamelCase				      =  {
      "image": image,
      "mask_image": mask,
      "generator": generator,
      "num_inference_steps": 5,
      "eta": 0.0,
      "jump_length": 2,
      "jump_n_sample": 2,
      "output_type": "numpy",
  }
  return inputs



 def    __lowerCamelCase				(  self			)  ->							int:
  __UpperCamelCase				      =  "cpu"  # ensure determinism for the device-dependent torch.Generator
  __UpperCamelCase				      =  self.get_dummy_components()
  __UpperCamelCase				      =  RePaintPipeline(**lowercase			)
  __UpperCamelCase				      =  sd_pipe.to(lowercase			)
  sd_pipe.set_progress_bar_config(disable=lowercase			)

  __UpperCamelCase				      =  self.get_dummy_inputs(lowercase			)
  __UpperCamelCase				      =  sd_pipe(**lowercase			).images
  __UpperCamelCase				      =  image[0, -3:, -3:, -1]

  assert image.shape == (1, 3_2, 3_2, 3)
  __UpperCamelCase				      =  np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]			)

  assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3



 @skip_mps
 def    __lowerCamelCase				(  self			)  ->							Tuple:
  return super().test_save_load_local()



 @unittest.skip("""non-deterministic pipeline"""			)
 def    __lowerCamelCase				(  self			)  ->							Tuple:
  return super().test_inference_batch_single_identical()



 @skip_mps
 def    __lowerCamelCase				(  self			)  ->							List[str]:
  return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    __lowerCamelCase				(  self			)  ->							Dict:
  return super().test_save_load_optional_components()




 @skip_mps
 def    __lowerCamelCase				(  self			)  ->							List[str]:
  return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class        UpperCAmelCase__  (    unittest.TestCase):



 def    __lowerCamelCase				(  self			)  ->							List[str]:
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()




 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
      """repaint/celeba_hq_256.png"""			)
  __UpperCamelCase				      =  load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""			)
  __UpperCamelCase				      =  load_numpy(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
      """repaint/celeba_hq_256_result.npy"""			)

  __UpperCamelCase				      =  "google/ddpm-ema-celebahq-256"
  __UpperCamelCase				      =  UNetaDModel.from_pretrained(lowercase			)
  __UpperCamelCase				      =  RePaintScheduler.from_pretrained(lowercase			)

  __UpperCamelCase				      =  RePaintPipeline(unet=lowercase		,	scheduler=lowercase			).to(lowercase			)
  repaint.set_progress_bar_config(disable=lowercase			)
  repaint.enable_attention_slicing()

  __UpperCamelCase				      =  torch.manual_seed(0			)
  __UpperCamelCase				      =  repaint(
      lowercase		,	lowercase		,	num_inference_steps=2_5_0		,	eta=0.0		,	jump_length=1_0		,	jump_n_sample=1_0		,	generator=lowercase		,	output_type="""np"""		,	)
  __UpperCamelCase				      =  output.images[0]

  assert image.shape == (2_5_6, 2_5_6, 3)
  assert np.abs(expected_image - image			).mean() < 1E-2










'''simple docstring'''



import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil







@pytest.fixture
def _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  tmp_path / """text.txt"""
 __UpperCamelCase				      =  textwrap.dedent(
     """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """       )
 with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
  f.write(__A       )
 return str(__A       )







@pytest.fixture
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
 with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
  f.write(__A       )
 return str(__A       )







@pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
 __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
 __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
 assert generated_content == expected_content







@require_pil
def _lowercase   (  __A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read().splitlines()[0]
 __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
 __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
 assert pa_table.schema.field("""image"""       ).type == Image()()
 __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
 assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read()
 if sample_by == "line":
  __UpperCamelCase				      =  expected_content.splitlines()
 elif sample_by == "paragraph":
  __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
 elif sample_by == "document":
  __UpperCamelCase				      =  [expected_content]
 __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
 __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
 assert generated_content == expected_content





