






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__A      =				transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def      __A					(     _lowercase ):

					'''simple docstring'''







					if isinstance(_lowercase					,			torch.Tensor ):
										return image
					elif isinstance(_lowercase					,			PIL.Image.Image ):
										_A								= [image]

					_A								= [trans(img.convert('''RGB''' ) ) for img in image]
					_A								= torch.stack(_lowercase )
					return image





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   Dict		,     __A:   Optional[int]		,     __A:   int   )    ->				Optional[Any]:
												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_A								= DDIMScheduler.from_config(scheduler.config   )

												self.register_modules(unet=__A		,     scheduler=__A   )

							def       __A (  self:   List[Any]		,     __A:   Dict   )    ->				str:
												if strength < 0 or strength > 1:
																	raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""   )

							def       __A (  self:   Tuple		,     __A:   str		,     __A:   Tuple		,     __A:   Tuple   )    ->				str:
												# get the original timestep using init_timestep
												_A								= min(int(num_inference_steps * strength   )		,     __A   )

												_A								= max(num_inference_steps - init_timestep		,     0   )
												_A								= self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start

							def       __A (  self:   Union[str, Any]		,     __A:   Optional[int]		,     __A:   List[str]		,     __A:   List[str]		,     __A:   Dict		,     __A:   str		,     __A:   Tuple=None   )    ->				List[Any]:
												if not isinstance(__A		,     (torch.Tensor, PIL.Image.Image, list)   ):
																	raise ValueError(
																	    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__A   )}"""   )

												_A								= image.to(device=__A		,     dtype=__A   )

												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."""   )

												_A								= init_latents.shape
												_A								= randn_tensor(__A		,     generator=__A		,     device=__A		,     dtype=__A   )

												# get latents
												print('''add noise to latents at timestep'''		,     __A   )
												_A								= self.scheduler.add_noise(__A		,     __A		,     __A   )
												_A								= init_latents

												return latents



							@torch.no_grad()
							def __call__(  self:   Dict		,     __A:   Union[torch.FloatTensor, PIL.Image.Image] = None		,     __A:   float = 0.8		,     __A:   int = 1		,     __A:   Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     __A:   float = 0.0		,     __A:   int = 50		,     __A:   Optional[bool] = None		,     __A:   Optional[str] = "pil"		,     __A:   bool = True		,     )    ->				Union[ImagePipelineOutput, Tuple]:
												self.check_inputs(__A   )

												# 2. Preprocess image
												_A								= preprocess(__A   )

												# 3. set timesteps
												self.scheduler.set_timesteps(__A		,     device=self.device   )
												_A							,_A								= self.get_timesteps(__A		,     __A		,     self.device   )
												_A								= timesteps[:1].repeat(__A   )

												# 4. Prepare latent variables
												_A								= self.prepare_latents(__A		,     __A		,     __A		,     self.unet.dtype		,     self.device		,     __A   )
												_A								= latents

												# 5. Denoising loop
												for t in self.progress_bar(__A   ):
																	# 1. predict noise model_output
																	_A								= self.unet(__A		,     __A   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	_A								= self.scheduler.step(
																	    __A		,     __A		,     __A		,     eta=__A		,     use_clipped_model_output=__A		,     generator=__A		,     ).prev_sample

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

												if not return_dict:
																	return (image, latent_timestep.item())

												return ImagePipelineOutput(images=__A   )











import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__A      =				'\\n    Text data.\n    Second line of data.'

__A      =				'file'




@pytest.fixture(scope='''session''' )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
					_A								= bytes(_lowercase					,			'''utf-8''' )
					with zstd.open(_lowercase					,			'''wb''' ) as f:
										f.write(_lowercase )
					return path




@pytest.fixture
def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(os.path.join(tmpfs.local_root_dir					,			_lowercase )					,			'''w''' ) as f:
										f.write(_lowercase )
					return FILE_PATH




@pytest.mark.parametrize('''compression_format'''					,			['''gzip''', '''xz''', '''zstd'''] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
					_A								= input_paths[compression_format]
					_A								= tmp_path / '''cache'''
					_A								= DownloadConfig(cache_dir=_lowercase					,			extract_compressed_file=_lowercase )
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					with open(_lowercase ) as f:
										_A								= f.read()
					with open(_lowercase ) as f:
										_A								= f.read()
					assert extracted_file_content == expected_file_content




@pytest.mark.parametrize('''default_extracted'''					,			[True, False] )
@pytest.mark.parametrize('''default_cache_dir'''					,			[True, False] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= '''custom_cache'''
					_A								= '''custom_extracted_dir'''
					_A								= tmp_path / '''custom_extracted_path'''
					if default_extracted:
										_A								= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
					else:
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''					,			_lowercase )
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,			str(_lowercase ) )
										_A								= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					_A								= xz_file
					_A								= (
					    DownloadConfig(extract_compressed_file=_lowercase )
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,			extract_compressed_file=_lowercase )
					)
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					assert Path(_lowercase ).parent.parts[-2:] == expected




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(Path(_lowercase ).resolve() )
					assert cached_path(_lowercase ) == text_file
					# relative path
					_A								= str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
					assert cached_path(_lowercase ) == text_file




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(tmp_path.resolve() / '''__missing_file__.txt''' )
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )
					# relative path
					_A								= '''./__missing_file__.txt'''
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= get_from_cache(f"""tmp://{tmpfs_file}""" )
					with open(_lowercase ) as f:
										_A								= f.read()
					assert output_file_content == FILE_CONTENT




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     ):

					'''simple docstring'''







					with pytest.raises(_lowercase ):
										cached_path('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										http_get('''https://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										http_head('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										ftp_get('''ftp://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										ftp_head('''ftp://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										fsspec_get('''s3://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										fsspec_head('''s3://huggingface.co''' )



