
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


lowercase   =	get_logger(__name__)





class    __A:
					SCREAMING_SNAKE_CASE           =     '''dummy_data'''
					SCREAMING_SNAKE_CASE           =     '''datasets'''
					SCREAMING_SNAKE_CASE           =     False

					def __init__(						self       :  List[str] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Any = None ,       __UpperCamelCase       :  List[Any] = False ,       __UpperCamelCase       :  Any = True ,       __UpperCamelCase       :  Tuple = None ,       ):
												lowerCamelCase_					=       0
												lowerCamelCase_					=       dataset_name
												lowerCamelCase_					=       cache_dir
												lowerCamelCase_					=       use_local_dummy_data
												lowerCamelCase_					=       config
												# download_callbacks take a single url as input
												lowerCamelCase_					=       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
												lowerCamelCase_					=       load_existing_dummy_data

												# TODO(PVP, QL) might need to make this more general
												lowerCamelCase_					=       str(_a )
												# to be downloaded
												lowerCamelCase_					=       None
												lowerCamelCase_					=       None

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

					@property
					def  lowercase__   (						self       :  Dict ):
												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  lowercase__   (						self       :  int ):
												return os.path.join(self.dummy_data_folder ,       """dummy_data.zip""" )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       (
												    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
												)

												lowerCamelCase_					=       cached_path(
												    _a ,       cache_dir=self.cache_dir ,       extract_compressed_file=_a ,       force_extract=_a )

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

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

					@property
					def  lowercase__   (						self       :  Any ):
												if self._bucket_url is None:
																			lowerCamelCase_					=       hf_github_url(self.dataset_name ,       self.dummy_zip_file.replace(os.sep ,       """/""" ) )
												return self._bucket_url

					@property
					def  lowercase__   (						self       :  Optional[int] ):
												# 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  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  List[str] ,       *__UpperCamelCase       :  Union[str, Any] ):
												if self.load_existing_dummy_data:
																			# dummy data is downloaded and tested
																			lowerCamelCase_					=       self.dummy_file
												else:
																			# dummy data cannot be downloaded and only the path to dummy file is returned
																			lowerCamelCase_					=       self.dummy_file_name

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

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  List[Any] ,       *__UpperCamelCase       :  List[Any] ):
												return self.download_and_extract(_a )

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  str ):
												return self.download_and_extract(_a )

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  List[Any] ,       *__UpperCamelCase       :  List[str] ,       **__UpperCamelCase       :  List[str] ):
												return path

					def  lowercase__   (						self       :  Optional[Any] ):
												return {}

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  int ):
												lowerCamelCase_					=       {}
												for key, single_urls in data_url.items():
																			for download_callback in self.download_callbacks:
																										if isinstance(_a ,       _a ):
																																	for single_url in single_urls:
																																								download_callback(_a )
																										else:
																																	lowerCamelCase_					=       single_urls
																																	download_callback(_a )
            # 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(_a ,       _a ):
																										lowerCamelCase_					=       [os.path.join(_a ,       urllib.parse.quote_plus(Path(_a ).name ) ) for x in single_urls]
																			else:
																										lowerCamelCase_					=       single_urls
																										lowerCamelCase_					=       os.path.join(_a ,       urllib.parse.quote_plus(Path(_a ).name ) )
																			lowerCamelCase_					=       value

												# make sure that values are unique
												if all(isinstance(_a ,       _a ) 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
																			lowerCamelCase_					=       {key: value + key for key, value in dummy_data_dict.items()}

												return dummy_data_dict

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       []
												# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
												lowerCamelCase_					=       all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" ,       _a ) ) for url in data_url )
												lowerCamelCase_					=       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):
																			lowerCamelCase_					=       [data_url[0]] * len(_a )
												for single_url in data_url:
																			for download_callback in self.download_callbacks:
																										download_callback(_a )
																			# 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
																			lowerCamelCase_					=       os.path.join(_a ,       urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
																			dummy_data_list.append(_a )
												return dummy_data_list

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Tuple ):
												for download_callback in self.download_callbacks:
																			download_callback(_a )
												# 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
												lowerCamelCase_					=       os.path.join(_a ,       urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
												if os.path.exists(_a ) 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  lowercase__   (						self       :  Optional[Any] ):
												pass

					def  lowercase__   (						self       :  Any ):
												pass

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

												lowerCamelCase_					=       Path(_a )
												lowerCamelCase_					=       _iter_archive_members(_a ) 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(_a ).as_posix(), file_path.open("""rb""" )



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







"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						a__		( unittest.TestCase		):
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				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")      ,       )
          return model

   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.dummy_uncond_unet
          lowercase       :							Dict									=				KarrasVeScheduler()

          lowercase       :							Any									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							int									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"      ,       return_dict=_a							)[0]

          lowercase       :							Any									=				image[0, -3:, -3:, -1]
          lowercase       :							Dict									=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							Tuple									=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2


@slow
@require_torch
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"google/ncsnpp-celebahq-256"
          lowercase       :							str									=				UNetaDModel.from_pretrained(_a							)
          lowercase       :							Optional[Any]									=				KarrasVeScheduler()

          lowercase       :							Union[str, Any]									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Dict									=				pipe(num_inference_steps=20      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							Optional[Any]									=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          lowercase       :							Optional[Any]									=				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


