





import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard

def   a_   (			lowerCAmelCase_				:      List[Any]     ):
   assert "mock" in _fsspec_registry
   assert "bz2" in _fsspec_registry

def   a_   (			):
   assert "mock" not in _fsspec_registry
   assert "bz2" in _fsspec_registry

def   a_   (			):
   __lowerCAmelCase							     =     'mock-s3-bucket'
   __lowerCAmelCase							     =     F"""s3://{mock_bucket}"""
   __lowerCAmelCase							     =     extract_path_from_uri(lowerCAmelCase_     )
   assert dataset_path.startswith('s3://'     ) is False

   __lowerCAmelCase							     =     './local/path'
   __lowerCAmelCase							     =     extract_path_from_uri(lowerCAmelCase_     )
   assert dataset_path == new_dataset_path

def   a_   (			lowerCAmelCase_				:      Tuple     ):
   __lowerCAmelCase							     =     is_remote_filesystem(lowerCAmelCase_     )
   assert is_remote is True

   __lowerCAmelCase							     =     fsspec.filesystem('file'     )

   __lowerCAmelCase							     =     is_remote_filesystem(lowerCAmelCase_     )
   assert is_remote is False

@pytest.mark.parametrize('compression_fs_class',     lowerCAmelCase_     )
def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      Dict     ):
   __lowerCAmelCase							     =     {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
   __lowerCAmelCase							     =     input_paths[compression_fs_class.protocol]
   if input_path is None:
      __lowerCAmelCase							     =     F"""for '{compression_fs_class.protocol}' compression protocol, """
      if compression_fs_class.protocol == "lz4":
         reason += require_lza.kwargs["reason"]
      elif compression_fs_class.protocol == "zstd":
         reason += require_zstandard.kwargs["reason"]
      pytest.skip(lowerCAmelCase_     )
   __lowerCAmelCase							     =     fsspec.filesystem(compression_fs_class.protocol,     fo=lowerCAmelCase_     )
   assert isinstance(lowerCAmelCase_,     lowerCAmelCase_     )
   __lowerCAmelCase							     =     os.path.basename(lowerCAmelCase_     )
   __lowerCAmelCase							     =     expected_filename[: expected_filename.rindex('.'     )]
   assert fs.glob('*'     ) == [expected_filename]
   with fs.open(lowerCAmelCase_,     'r',     encoding='utf-8'     ) as f, open(lowerCAmelCase_,     encoding='utf-8'     ) as expected_file:
      assert f.read() == expected_file.read()

@pytest.mark.parametrize('protocol',     ['zip', 'gzip']     )
def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Dict     ):
   __lowerCAmelCase							     =     {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
   __lowerCAmelCase							     =     compressed_file_paths[protocol]
   __lowerCAmelCase							     =     'dataset.jsonl'
   __lowerCAmelCase							     =     F"""{protocol}://{member_file_path}::{compressed_file_path}"""
   __lowerCAmelCase     ,     *__lowerCAmelCase							     =     fsspec.get_fs_token_paths(lowerCAmelCase_     )
   assert fs.isfile(lowerCAmelCase_     )
   assert not fs.isfile('non_existing_' + member_file_path     )

@pytest.mark.integration
def   a_   (			lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      str,     lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     hf_api.dataset_info(lowerCAmelCase_,     token=lowerCAmelCase_     )
   __lowerCAmelCase							     =     HfFileSystem(repo_info=lowerCAmelCase_,     token=lowerCAmelCase_     )
   assert sorted(hffs.glob('*'     )     ) == [".gitattributes", "data"]
   assert hffs.isdir('data'     )
   assert hffs.isfile('.gitattributes'     ) and hffs.isfile('data/text_data.txt'     )
   with open(lowerCAmelCase_     ) as f:
      assert hffs.open('data/text_data.txt',     'r'     ).read() == f.read()




def   a_   (			):
   __lowerCAmelCase							     =     'bz2'

   # Import module
   import datasets.filesystems

   # Overwrite protocol and reload
   register_implementation(lowerCAmelCase_,     lowerCAmelCase_,     clobber=lowerCAmelCase_     )
   with pytest.warns(lowerCAmelCase_     ) as warning_info:
      importlib.reload(datasets.filesystems     )

   assert len(lowerCAmelCase_     ) == 1
   assert (
       str(warning_info[0].message     )
       == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
   )








import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_snake_case  :   Union[str, Any]						   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   warnings.warn(
       'The preprocess method is deprecated and will be removed in a future version. Please'
       ' use VaeImageProcessor.preprocess instead',     lowerCAmelCase_,     )
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return image
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [image]

   if isinstance(image[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     image[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

      __lowerCAmelCase							     =     [np.array(i.resize((w, h),     resample=PIL_INTERPOLATION['lanczos']     )     )[None, :] for i in image]
      __lowerCAmelCase							     =     np.concatenate(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     np.array(lowerCAmelCase_     ).astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     image.transpose(0,     3,     1,     2     )
      __lowerCAmelCase							     =     2.0 * image - 1.0
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(image[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return image

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

   if isinstance(mask[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     mask[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      __lowerCAmelCase							     =     [np.array(m.convert('L'     ).resize((w, h),     resample=PIL_INTERPOLATION['nearest']     )     )[None, :] for m in mask]
      __lowerCAmelCase							     =     np.concatenate(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     mask.astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     0
      __lowerCAmelCase							     =     1
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(mask[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return mask




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






  @torch.no_grad()
  def __call__(    self			:		Dict ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		int = 2_5_0 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:		Optional[str] = "pil" ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[ImagePipelineOutput, Tuple]:
     __lowerCAmelCase							     =     image

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __lowerCAmelCase							     =     mask_image.to(device=self.device ,  dtype=self.unet.dtype					)

     __lowerCAmelCase							     =     original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) and len(lowerCAmelCase_					) != batch_size:
        raise ValueError(
            f"""You have passed a list of generators of length {len(lowerCAmelCase_					)}, but requested an effective batch"""
            f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

     __lowerCAmelCase							     =     self.scheduler.timesteps[0] + 1
     __lowerCAmelCase							     =     generator[0] if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
        if t < t_last:
           # predict the noise residual
           __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_					).sample
           # compute previous image: x_t -> x_t-1
           __lowerCAmelCase							     =     self.scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					).prev_sample

        else:
           # compute the reverse: x_t-1 -> x_t
           __lowerCAmelCase							     =     self.scheduler.undo_step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     t

     __lowerCAmelCase							     =     (image / 2 + 0.5).clamp(0 ,  1					)
     __lowerCAmelCase							     =     image.cpu().permute(0 ,  2 ,  3 ,  1					).numpy()
     if output_type == "pil":
        __lowerCAmelCase							     =     self.numpy_to_pil(lowerCAmelCase_					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

