


# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowerCAmelCase       		=    get_logger(__name__)




class 							lowerCamelCase   :
	_lowerCAmelCase     :		Dict										=						'''dummy_data'''
	_lowerCAmelCase     :		int										=						'''datasets'''
	_lowerCAmelCase     :		Union[str, Any]										=						False


	def __init__(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = False				,     lowercase__ = True				,     lowercase__ = None				,     ):
							__UpperCAmelCase			:							Optional[int]	    =	0
							__UpperCAmelCase			:							Tuple	    =	dataset_name
							__UpperCAmelCase			:							List[str]	    =	cache_dir
							__UpperCAmelCase			:							int	    =	use_local_dummy_data
							__UpperCAmelCase			:							Dict	    =	config
							# download_callbacks take a single url as input
							__UpperCAmelCase			:							List[Callable]	    =	download_callbacks or []
							# if False, it doesn't load existing files and it returns the paths of the dummy files relative
							# to the dummy_data zip file root
							__UpperCAmelCase			:							Union[str, Any]	    =	load_existing_dummy_data

							# TODO(PVP, QL) might need to make this more general
							__UpperCAmelCase			:							List[Any]	    =	str(lowercase__)
							# to be downloaded
							__UpperCAmelCase			:							str	    =	None
							__UpperCAmelCase			:							Optional[int]	    =	None


	@property
	def  A(    self):
							if self._dummy_file is None:
													__UpperCAmelCase			:							int	    =	self.download_dummy_data()
							return self._dummy_file


	@property
	def  A(    self):
							if self.config is not None:
													# structure is dummy / config_name / version_name
													return os.path.join('''dummy'''				,     self.config.name				,     self.version_name)
							# structure is dummy / version_name
							return os.path.join('''dummy'''				,     self.version_name)


	@property
	def  A(    self):
							return os.path.join(self.dummy_data_folder				,     '''dummy_data.zip''')


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	(
							    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
							)

							__UpperCAmelCase			:							int	    =	cached_path(
							    lowercase__				,     cache_dir=self.cache_dir				,     extract_compressed_file=lowercase__				,     force_extract=lowercase__)

							return os.path.join(lowercase__				,     self.dummy_file_name)


	@property
	def  A(    self):
							return os.path.join(self.datasets_scripts_dir				,     self.dataset_name				,     self.dummy_zip_file)


	@property
	def  A(    self):
							if self._bucket_url is None:
													__UpperCAmelCase			:							str	    =	hf_github_url(self.dataset_name				,     self.dummy_zip_file.replace(os.sep				,     '''/'''))
							return self._bucket_url


	@property
	def  A(    self):
							# return full path if its a dir
							if os.path.isdir(self.dummy_file):
													return self.dummy_file
							# else cut off path to file -> example `xsum`.
							return "/".join(self.dummy_file.replace(os.sep				,     '''/''').split('''/''')[:-1])


	def  A(    self				,     lowercase__				,     *lowercase__):
							if self.load_existing_dummy_data:
													# dummy data is downloaded and tested
													__UpperCAmelCase			:							str	    =	self.dummy_file
							else:
													# dummy data cannot be downloaded and only the path to dummy file is returned
													__UpperCAmelCase			:							Optional[int]	    =	self.dummy_file_name

							# special case when data_url is a dict
							if isinstance(lowercase__				,     lowercase__):
													return self.create_dummy_data_dict(lowercase__				,     lowercase__)
							elif isinstance(lowercase__				,     (list, tuple)):
													return self.create_dummy_data_list(lowercase__				,     lowercase__)
							else:
													return self.create_dummy_data_single(lowercase__				,     lowercase__)


	def  A(    self				,     lowercase__				,     *lowercase__):
							return self.download_and_extract(lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__):
							return self.download_and_extract(lowercase__)


	def  A(    self				,     lowercase__				,     *lowercase__				,     **lowercase__):
							return path


	def  A(    self):
							return {}


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Dict	    =	{}
							for key, single_urls in data_url.items():
													for download_callback in self.download_callbacks:
																			if isinstance(lowercase__				,     lowercase__):
																									for single_url in single_urls:
																															download_callback(lowercase__)
																			else:
																									__UpperCAmelCase			:							List[Any]	    =	single_urls
																									download_callback(lowercase__)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
													if isinstance(lowercase__				,     lowercase__):
																			__UpperCAmelCase			:							Optional[int]	    =	[os.path.join(lowercase__				,     urllib.parse.quote_plus(Path(lowercase__).name)) for x in single_urls]
													else:
																			__UpperCAmelCase			:							List[Any]	    =	single_urls
																			__UpperCAmelCase			:							List[Any]	    =	os.path.join(lowercase__				,     urllib.parse.quote_plus(Path(lowercase__).name))
													__UpperCAmelCase			:							List[Any]	    =	value

							# make sure that values are unique
							if all(isinstance(lowercase__				,     lowercase__) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
							    dummy_data_dict.values()):
													# append key to value to make its name unique
													__UpperCAmelCase			:							Tuple	    =	{key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	[]
							# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
							__UpperCAmelCase			:							Optional[int]	    =	all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''				,     lowercase__)) for url in data_url)
							__UpperCAmelCase			:							Optional[int]	    =	all(
							    url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''') for url in data_url)
							if data_url and (is_tf_records or is_pubmed_records):
													__UpperCAmelCase			:							Optional[int]	    =	[data_url[0]] * len(lowercase__)
							for single_url in data_url:
													for download_callback in self.download_callbacks:
																			download_callback(lowercase__)
													# we force the name of each key to be the last file / folder name of the url path
													# if the url has arguments, we need to encode them with urllib.parse.quote_plus
													__UpperCAmelCase			:							Union[str, Any]	    =	os.path.join(lowercase__				,     urllib.parse.quote_plus(single_url.split('''/''')[-1]))
													dummy_data_list.append(lowercase__)
							return dummy_data_list


	def  A(    self				,     lowercase__				,     lowercase__):
							for download_callback in self.download_callbacks:
													download_callback(lowercase__)
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.join(lowercase__				,     urllib.parse.quote_plus(data_url.split('''/''')[-1]))
							if os.path.exists(lowercase__) or not self.load_existing_dummy_data:
													return value
							else:
													# Backward compatibility, maybe deprecate at one point.
													# For many datasets with single url calls to dl_manager.download_and_extract,
													# the dummy_data.zip file is actually the zipped downloaded file
													# while now we expected the dummy_data.zip file to be a directory containing
													# the downloaded file.
													return path_to_dummy_data


	def  A(    self):
							pass


	def  A(    self):
							pass


	def  A(    self				,     lowercase__):
							def _iter_archive_members(lowercase__):
													# this preserves the order of the members inside the ZIP archive
													__UpperCAmelCase			:							Union[str, Any]	    =	Path(self.dummy_file).parent
													__UpperCAmelCase			:							Dict	    =	path.relative_to(lowercase__)
													with ZipFile(self.local_path_to_dummy_data) as zip_file:
																			__UpperCAmelCase			:							int	    =	zip_file.namelist()
													for member in members:
																			if member.startswith(relative_path.as_posix()):
																									yield dummy_parent_path.joinpath(lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	Path(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	_iter_archive_members(lowercase__) if self.use_local_dummy_data else path.rglob('''*''')
							for file_path in file_paths:
													if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')):
																			yield file_path.relative_to(lowercase__).as_posix(), file_path.open('''rb''')


	def  A(    self				,     lowercase__):
							if not isinstance(lowercase__				,     lowercase__):
													__UpperCAmelCase			:							Any	    =	[paths]
							for path in paths:
													if os.path.isfile(lowercase__):
																			if os.path.basename(lowercase__).startswith(('''.''', '''__''')):
																									return
																			yield path
													else:
																			for dirpath, dirnames, filenames in os.walk(lowercase__):
																									if os.path.basename(lowercase__).startswith(('''.''', '''__''')):
																															continue
																									dirnames.sort()
																									for filename in sorted(lowercase__):
																															if filename.startswith(('''.''', '''__''')):
																																					continue
																															yield os.path.join(lowercase__				,     lowercase__)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


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


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

