
'''simple docstring'''





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 							snake_case				(		UpperCAmelCase						)->      Tuple:







      """simple docstring"""
      __A													=      tmp_path / 'file.jsonl'
      __A													=      textwrap.dedent(
          '\\n        {"col_1": -1}\n        {"col_1": 1, "col_2": 2}\n        {"col_1": 10, "col_2": 20}\n        '						)
      with open(UpperCAmelCase   ,	'w'						) as f:
            f.write(UpperCAmelCase						)
      return str(UpperCAmelCase						)





@pytest.fixture
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      __A													=      tmp_path / 'file_utf16_encoded.jsonl'
      __A													=      textwrap.dedent(
          '\\n        {"col_1": -1}\n        {"col_1": 1, "col_2": 2}\n        {"col_1": 10, "col_2": 20}\n        '						)
      with open(UpperCAmelCase   ,	'w'   ,	encoding='utf-16'						) as f:
            f.write(UpperCAmelCase						)
      return str(UpperCAmelCase						)





@pytest.fixture
def 							snake_case				(		UpperCAmelCase						)->      Optional[int]:







      """simple docstring"""
      __A													=      tmp_path / 'file_with_list_of_dicts.json'
      __A													=      textwrap.dedent(
          '\\n        [\n            {"col_1": -1},\n            {"col_1": 1, "col_2": 2},\n            {"col_1": 10, "col_2": 20}\n        ]\n        '						)
      with open(UpperCAmelCase   ,	'w'						) as f:
            f.write(UpperCAmelCase						)
      return str(UpperCAmelCase						)





@pytest.fixture
def 							snake_case				(		UpperCAmelCase						)->      Optional[Any]:







      """simple docstring"""
      __A													=      tmp_path / 'file_with_list_of_dicts_field.json'
      __A													=      textwrap.dedent(
          '\\n        {\n            "field1": 1,\n            "field2": "aabb",\n            "field3": [\n                {"col_1": -1},\n                {"col_1": 1, "col_2": 2},\n                {"col_1": 10, "col_2": 20}\n            ]\n        }\n        '						)
      with open(UpperCAmelCase   ,	'w'						) as f:
            f.write(UpperCAmelCase						)
      return str(UpperCAmelCase						)





@pytest.mark.parametrize(
    'file_fixture, config_kwargs'   ,	[
        ('jsonl_file', {}),
        ('jsonl_file_utf16_encoded', {'encoding': 'utf-16'}),
        ('json_file_with_list_of_dicts', {}),
        ('json_file_with_list_of_dicts_field', {'field': 'field3'}),
    ]   ,	)
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      List[str]:







      """simple docstring"""
      __A													=      Json(**UpperCAmelCase						)
      __A													=      json._generate_tables([[request.getfixturevalue(UpperCAmelCase						)]]						)
      __A													=      pa.concat_tables([table for _, table in generator]						)
      assert pa_table.to_pydict() == {"col_1": [-1, 1, 1_0], "col_2": [None, 2, 2_0]}





@pytest.mark.parametrize(
    'file_fixture, config_kwargs'   ,	[
        (
            'jsonl_file',
            {'features': Features({'col_1': Value('int64'						), 'col_2': Value('int64'						), 'missing_col': Value('string'						)}						)},
        ),
        (
            'json_file_with_list_of_dicts',
            {'features': Features({'col_1': Value('int64'						), 'col_2': Value('int64'						), 'missing_col': Value('string'						)}						)},
        ),
        (
            'json_file_with_list_of_dicts_field',
            {
                'field': 'field3',
                'features': Features(
                    {'col_1': Value('int64'						), 'col_2': Value('int64'						), 'missing_col': Value('string'						)}						),
            },
        ),
    ]   ,	)
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      __A													=      Json(**UpperCAmelCase						)
      __A													=      json._generate_tables([[request.getfixturevalue(UpperCAmelCase						)]]						)
      __A													=      pa.concat_tables([table for _, table in generator]						)
      assert pa_table.to_pydict() == {"col_1": [-1, 1, 1_0], "col_2": [None, 2, 2_0], "missing_col": [None, None, None]}








'''simple docstring'''





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__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      UNetaDModel(
           block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           '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 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

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

       assert image.shape == (1, 32, 32, 3)
       __A													=      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 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


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






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image      ).mean() < 1E-2






