




'''simple docstring'''


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  def __init__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							NestedDataStructureLike[PathLike]					,  SCREAMING_SNAKE_CASE_				:							Optional[NamedSplit] = None					,  SCREAMING_SNAKE_CASE_				:							Optional[Features] = None					,  SCREAMING_SNAKE_CASE_				:							str = None					,  SCREAMING_SNAKE_CASE_				:							bool = False					,  SCREAMING_SNAKE_CASE_				:							bool = False					,  SCREAMING_SNAKE_CASE_				:							Optional[int] = None					,  **SCREAMING_SNAKE_CASE_				:							Tuple					,  )			->		Dict:







       '''simple docstring'''



       super().__init__(
           SCREAMING_SNAKE_CASE_					,  split=SCREAMING_SNAKE_CASE_					,  features=SCREAMING_SNAKE_CASE_					,  cache_dir=SCREAMING_SNAKE_CASE_					,  keep_in_memory=SCREAMING_SNAKE_CASE_					,  streaming=SCREAMING_SNAKE_CASE_					,  num_proc=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_					,  )
       A:		Union[str, Any]          =		path_or_paths if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	) else {self.split: path_or_paths}
       A:		List[Any]          =		Csv(
           cache_dir=SCREAMING_SNAKE_CASE_					,  data_files=SCREAMING_SNAKE_CASE_					,  features=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_					,  )




  def 					_snake_case     ( self				:							List[str]	)			->		Optional[Any]:







       '''simple docstring'''



       if self.streaming:
            A:		Any          =		self.builder.as_streaming_dataset(split=self.split	)
       # Build regular (map-style) dataset
       else:
            A:		List[Any]          =		None
            A:		Any          =		None
            A:		Optional[int]          =		None
            A:		Optional[Any]          =		None

            self.builder.download_and_prepare(
                download_config=SCREAMING_SNAKE_CASE_					,  download_mode=SCREAMING_SNAKE_CASE_					,  verification_mode=SCREAMING_SNAKE_CASE_					,  base_path=SCREAMING_SNAKE_CASE_					,  num_proc=self.num_proc					,  )
            A:		Optional[int]          =		self.builder.as_dataset(
                split=self.split					,  verification_mode=SCREAMING_SNAKE_CASE_					,  in_memory=self.keep_in_memory	)
       return dataset






class 						lowerCAmelCase_	:







  '''simple docstring'''

  def __init__( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							Dataset					,  SCREAMING_SNAKE_CASE_				:							Union[PathLike, BinaryIO]					,  SCREAMING_SNAKE_CASE_				:							Optional[int] = None					,  SCREAMING_SNAKE_CASE_				:							Optional[int] = None					,  **SCREAMING_SNAKE_CASE_				:							Dict					,  )			->		Optional[Any]:







       '''simple docstring'''



       if num_proc is not None and num_proc <= 0:
            raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""	)

       A:		Union[str, Any]          =		dataset
       A:		Dict          =		path_or_buf
       A:		int          =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       A:		List[str]          =		num_proc
       A:		int          =		'''utf-8'''
       A:		Optional[int]          =		to_csv_kwargs



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







       '''simple docstring'''



       A:		List[str]          =		self.to_csv_kwargs.pop('''path_or_buf'''					,  SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		self.to_csv_kwargs.pop('''header'''					,  SCREAMING_SNAKE_CASE_	)
       A:		Optional[int]          =		self.to_csv_kwargs.pop('''index'''					,  SCREAMING_SNAKE_CASE_	)

       if isinstance(self.path_or_buf					,  (str, bytes, os.PathLike)	):
            with open(self.path_or_buf					,  '''wb+'''	) as buffer:
                 A:		List[Any]          =		self._write(file_obj=SCREAMING_SNAKE_CASE_					,  header=SCREAMING_SNAKE_CASE_					,  index=SCREAMING_SNAKE_CASE_					,  **self.to_csv_kwargs	)
       else:
            A:		List[Any]          =		self._write(file_obj=self.path_or_buf					,  header=SCREAMING_SNAKE_CASE_					,  index=SCREAMING_SNAKE_CASE_					,  **self.to_csv_kwargs	)
       return written



  def 					_snake_case     ( self				:							str					,  SCREAMING_SNAKE_CASE_				:							Any	)			->		Union[str, Any]:







       '''simple docstring'''



       A     ,    A     ,    A     ,    A:		Optional[int]          =		args

       A:		Dict          =		query_table(
           table=self.dataset.data					,  key=slice(SCREAMING_SNAKE_CASE_					,  offset + self.batch_size	)					,  indices=self.dataset._indices					,  )
       A:		List[str]          =		batch.to_pandas().to_csv(
           path_or_buf=SCREAMING_SNAKE_CASE_					,  header=header if (offset == 0) else False					,  index=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)
       return csv_str.encode(self.encoding	)




  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							BinaryIO					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  **SCREAMING_SNAKE_CASE_				:							str	)			->		int:







       '''simple docstring'''



       A:		Dict          =		0

       if self.num_proc is None or self.num_proc == 1:
            for offset in logging.tqdm(
                range(0					,  len(self.dataset	)					,  self.batch_size	)					,  unit='''ba'''					,  disable=not logging.is_progress_bar_enabled()					,  desc='''Creating CSV from Arrow format'''					,  ):
                 A:		List[str]          =		self._batch_csv((offset, header, index, to_csv_kwargs)	)
                 written += file_obj.write(SCREAMING_SNAKE_CASE_	)

       else:
            A     ,    A:		Optional[int]          =		len(self.dataset	), self.batch_size
            with multiprocessing.Pool(self.num_proc	) as pool:
                 for csv_str in logging.tqdm(
                     pool.imap(
                         self._batch_csv					,  [(offset, header, index, to_csv_kwargs) for offset in range(0					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)]					,  )					,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,  unit='''ba'''					,  disable=not logging.is_progress_bar_enabled()					,  desc='''Creating CSV from Arrow format'''					,  ):
                      written += file_obj.write(SCREAMING_SNAKE_CASE_	)

       return written












'''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 						lowerCAmelCase_	(       UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      Any										=				RePaintPipeline
  UpperCamelCase_			:      int										=				IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  UpperCamelCase_			:      List[str]										=				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  UpperCamelCase_			:      List[str]										=				IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase_			:      str										=				False



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







       '''simple docstring'''



       torch.manual_seed(0	)
       torch.manual_seed(0	)
       A:		Optional[Any]          =		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:		Optional[int]          =		RePaintScheduler()
       A:		Optional[int]          =		{'''unet''': unet, '''scheduler''': scheduler}
       return components



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Tuple=0	)			->		Optional[int]:







       '''simple docstring'''



       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            A:		str          =		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            A:		Any          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		str          =		np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 3, 32, 32)	)
       A:		Tuple          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		List[Any]          =		(image > 0).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		Tuple          =		{
           '''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 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		int          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		List[str]          =		self.get_dummy_components()
       A:		int          =		RePaintPipeline(**SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
       A:		Optional[Any]          =		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       A:		List[Any]          =		np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]	)

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



  @skip_mps
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_save_load_local()



  @unittest.skip('''non-deterministic pipeline'''	)
  def 					_snake_case     ( self				:							Tuple	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       return super().test_save_load_optional_components()




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







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()






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







  '''simple docstring'''

  def 					_snake_case     ( self				:							Optional[Any]	)			->		Optional[Any]:







       '''simple docstring'''



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




  def 					_snake_case     ( self				:							Optional[int]	)			->		List[Any]:







       '''simple docstring'''



       A:		str          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''	)
       A:		Union[str, Any]          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''	)
       A:		Optional[Any]          =		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''	)

       A:		Dict          =		'''google/ddpm-ema-celebahq-256'''
       A:		Tuple          =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

       A:		Optional[Any]          =		RePaintPipeline(unet=SCREAMING_SNAKE_CASE_					,  scheduler=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
       repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
       repaint.enable_attention_slicing()

       A:		str          =		torch.manual_seed(0	)
       A:		str          =		repaint(
           SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  num_inference_steps=2_50					,  eta=0.0					,  jump_length=10					,  jump_n_sample=10					,  generator=SCREAMING_SNAKE_CASE_					,  output_type='''np'''					,  )
       A:		Optional[Any]          =		output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image	).mean() < 1E-2






