
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 :		Optional[Any]							      =  logging.get_logger(__name__)  # pylint: disable=invalid-name






def   lowerCAmelCase_						(					_snake_case      :  Union[List, PIL.Image.Image, torch.Tensor]   )				->		Optional[int]:







	'''simple docstring'''


	warnings.warn(
	    "The preprocess method is deprecated and will be removed in a future version. Please"
	    " use VaeImageProcessor.preprocess instead"      ,   _snake_case      ,   )
	if isinstance(_snake_case      ,   torch.Tensor   ):
		return image
	elif isinstance(_snake_case      ,   PIL.Image.Image   ):
		__magic_name__		:		Dict	   =     [image]

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

		__magic_name__		:		Optional[int]	   =     [np.array(i.resize((w, h)      ,   resample=PIL_INTERPOLATION["lanczos"]   )   )[None, :] for i in image]
		__magic_name__		:		Union[str, Any]	   =     np.concatenate(_snake_case      ,   axis=0   )
		__magic_name__		:		Any	   =     np.array(_snake_case   ).astype(np.floataa   ) / 255.0
		__magic_name__		:		List[Any]	   =     image.transpose(0      ,   3      ,   1      ,   2   )
		__magic_name__		:		int	   =     2.0 * image - 1.0
		__magic_name__		:		int	   =     torch.from_numpy(_snake_case   )
	elif isinstance(image[0]      ,   torch.Tensor   ):
		__magic_name__		:		Tuple	   =     torch.cat(_snake_case      ,   dim=0   )
	return image






def   lowerCAmelCase_						(					_snake_case      :  Union[List, PIL.Image.Image, torch.Tensor]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   torch.Tensor   ):
		return mask
	elif isinstance(_snake_case      ,   PIL.Image.Image   ):
		__magic_name__		:		Any	   =     [mask]

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





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    42







			def __init__(				self						,   _a						,   _a		):
				super().__init__()
				self.register_modules(unet=_a						,   scheduler=_a		)







			@torch.no_grad()
			def __call__(				self						,   _a						,   _a						,   _a = 250						,   _a = 0.0						,   _a = 10						,   _a = 10						,   _a = None						,   _a = "pil"						,   _a = True						,   ):
				__magic_name__		:		int	   =     image

				__magic_name__		:		Tuple	   =     _preprocess_image(_a		)
				__magic_name__		:		Dict	   =     original_image.to(device=self.device						,   dtype=self.unet.dtype		)
				__magic_name__		:		Union[str, Any]	   =     _preprocess_mask(_a		)
				__magic_name__		:		str	   =     mask_image.to(device=self.device						,   dtype=self.unet.dtype		)

				__magic_name__		:		Any	   =     original_image.shape[0]

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

				__magic_name__		:		int	   =     original_image.shape
				__magic_name__		:		List[Any]	   =     randn_tensor(_a						,   generator=_a						,   device=self.device						,   dtype=self.unet.dtype		)

				# set step values
				self.scheduler.set_timesteps(_a						,   _a						,   _a						,   self.device		)
				__magic_name__		:		Optional[int]	   =     eta

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

					else:
						# compute the reverse: x_t-1 -> x_t
						__magic_name__		:		List[Any]	   =     self.scheduler.undo_step(_a						,   _a						,   _a		)
					__magic_name__		:		Any	   =     t

				__magic_name__		:		Dict	   =     (image / 2 + 0.5).clamp(0						,   1		)
				__magic_name__		:		List[str]	   =     image.cpu().permute(0						,   2						,   3						,   1		).numpy()
				if output_type == "pil":
					__magic_name__		:		str	   =     self.numpy_to_pil(_a		)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_a		)


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   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






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







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     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(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

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

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