'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						KandinskyImgaImgPipeline
      __UpperCamelCase:  Optional[int]						=						["prompt", "image_embeds", "negative_image_embeds", "image"]
      __UpperCamelCase:  Optional[Any]						=						[
          "prompt",
          "negative_prompt",
          "image_embeds",
          "negative_image_embeds",
          "image",
      ]
      __UpperCamelCase:  int						=						[
          "generator",
          "height",
          "width",
          "strength",
          "guidance_scale",
          "negative_prompt",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      __UpperCamelCase:  Tuple						=						False




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return 32




      @property
      def      _A	(       self						:				Dict       ):
             return 32




      @property
      def      _A	(       self						:				List[str]       ):
             return self.time_input_dim




      @property
      def      _A	(       self						:				str       ):
             return self.time_input_dim * 4




      @property
      def      _A	(       self						:				Dict       ):
             return 100




      @property
      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"       )
             return tokenizer




      @property
      def      _A	(       self						:				List[Any]       ):
             torch.manual_seed(0       )
             _UpperCAmelCase     :  Any							      =    MCLIPConfig(
                 numDims=self.cross_attention_dim						,					transformerDimensions=self.text_embedder_hidden_size						,					hidden_size=self.text_embedder_hidden_size						,					intermediate_size=37						,					num_attention_heads=4						,					num_hidden_layers=5						,					vocab_size=1005						,					)

             _UpperCAmelCase     :  Optional[int]							      =    MultilingualCLIP(A       )
             _UpperCAmelCase     :  Dict							      =    text_encoder.eval()

             return text_encoder




      @property
      def      _A	(       self						:				Dict       ):
             torch.manual_seed(0       )

             _UpperCAmelCase     :  str							      =    {
                 "in_channels": 4,
                 # Out channels is double in channels because predicts mean and variance
                 "out_channels": 8,
                 "addition_embed_type": "text_image",
                 "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
                 "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
                 "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
                 "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                 "layers_per_block": 1,
                 "encoder_hid_dim": self.text_embedder_hidden_size,
                 "encoder_hid_dim_type": "text_image_proj",
                 "cross_attention_dim": self.cross_attention_dim,
                 "attention_head_dim": 4,
                 "resnet_time_scale_shift": "scale_shift",
                 "class_embed_type": None,
             }

             _UpperCAmelCase     :  str							      =    UNetaDConditionModel(**A       )
             return model




      @property
      def      _A	(       self						:				str       ):
             return {
                 "block_out_channels": [32, 64],
                 "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                 "in_channels": 3,
                 "latent_channels": 4,
                 "layers_per_block": 1,
                 "norm_num_groups": 8,
                 "norm_type": "spatial",
                 "num_vq_embeddings": 12,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }




      @property
      def      _A	(       self						:				int       ):
             torch.manual_seed(0       )
             _UpperCAmelCase     :  str							      =    VQModel(**self.dummy_movq_kwargs       )
             return model




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.dummy_text_encoder
             _UpperCAmelCase     :  str							      =    self.dummy_tokenizer
             _UpperCAmelCase     :  int							      =    self.dummy_unet
             _UpperCAmelCase     :  List[str]							      =    self.dummy_movq

             _UpperCAmelCase     :  int							      =    {
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.00_085,
                 "beta_end": 0.012,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             _UpperCAmelCase     :  List[Any]							      =    DDIMScheduler(**A       )

             _UpperCAmelCase     :  List[Any]							      =    {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components




      def      _A	(       self						:				Dict						,					A						:				Any						,					A						:				Dict=0       ):
             _UpperCAmelCase     :  Dict							      =    floats_tensor((1, self.cross_attention_dim)						,					rng=random.Random(A       )       ).to(A       )
             _UpperCAmelCase     :  int							      =    floats_tensor((1, self.cross_attention_dim)						,					rng=random.Random(seed + 1       )       ).to(A       )
             # create init_image
             _UpperCAmelCase     :  int							      =    floats_tensor((1, 3, 64, 64)						,					rng=random.Random(A       )       ).to(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    image.cpu().permute(0						,					2						,					3						,					1       )[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    Image.fromarray(np.uinta(A       )       ).convert("RGB"       ).resize((256, 256)       )

             if str(A       ).startswith("mps"       ):
                    _UpperCAmelCase     :  str							      =    torch.manual_seed(A       )
             else:
                    _UpperCAmelCase     :  Dict							      =    torch.Generator(device=A       ).manual_seed(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    {
                 "prompt": "horse",
                 "image": init_image,
                 "image_embeds": image_embeds,
                 "negative_image_embeds": negative_image_embeds,
                 "generator": generator,
                 "height": 64,
                 "width": 64,
                 "num_inference_steps": 10,
                 "guidance_scale": 7.0,
                 "strength": 0.2,
                 "output_type": "np",
             }
             return inputs





      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  str							      =    "cpu"

             _UpperCAmelCase     :  Tuple							      =    self.get_dummy_components()

             _UpperCAmelCase     :  Optional[Any]							      =    self.pipeline_class(**A       )
             _UpperCAmelCase     :  Dict							      =    pipe.to(A       )

             pipe.set_progress_bar_config(disable=A       )

             _UpperCAmelCase     :  Optional[Any]							      =    pipe(**self.get_dummy_inputs(A       )       )
             _UpperCAmelCase     :  Union[str, Any]							      =    output.images

             _UpperCAmelCase     :  str							      =    pipe(
                 **self.get_dummy_inputs(A       )						,					return_dict=A						,					)[0]

             _UpperCAmelCase     :  Optional[Any]							      =    image[0, -3:, -3:, -1]
             _UpperCAmelCase     :  Optional[Any]							      =    image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             _UpperCAmelCase     :  int							      =    np.array(
                 [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]       )
             assert (
                 np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
             ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
             assert (
                 np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2
             ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""

@slow
@require_torch_gpu
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				Union[str, Any]       ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()





      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Any							      =    load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinsky/kandinsky_img2img_frog.npy"       )

             _UpperCAmelCase     :  int							      =    load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
             _UpperCAmelCase     :  Optional[Any]							      =    "A red cartoon frog, 4k"

             _UpperCAmelCase     :  Any							      =    KandinskyPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1-prior"						,					torch_dtype=torch.floataa       )
             pipe_prior.to(A       )

             _UpperCAmelCase     :  List[str]							      =    KandinskyImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1"						,					torch_dtype=torch.floataa       )
             _UpperCAmelCase     :  List[Any]							      =    pipeline.to(A       )

             pipeline.set_progress_bar_config(disable=A       )

             _UpperCAmelCase     :  int							      =    torch.Generator(device="cpu"       ).manual_seed(0       )
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    pipe_prior(
                 A						,					generator=A						,					num_inference_steps=5						,					negative_prompt=""						,					).to_tuple()

             _UpperCAmelCase     :  int							      =    pipeline(
                 A						,					image=A						,					image_embeds=A						,					negative_image_embeds=A						,					generator=A						,					num_inference_steps=100						,					height=768						,					width=768						,					strength=0.2						,					output_type="np"						,					)

             _UpperCAmelCase     :  List[Any]							      =    output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(A						,					A       )


'''simple docstring'''




import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


__SCREAMING_SNAKE_CASE       :     Dict     =  get_logger(__name__)

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[str]						,					A						:				Optional[str] = None       ):
             _UpperCAmelCase     :  Dict							      =    (
                 os.path.join(A						,					config.EXTRACTED_DATASETS_DIR       ) if cache_dir else config.EXTRACTED_DATASETS_PATH
             )
             _UpperCAmelCase     :  Union[str, Any]							      =    Extractor




      def      _A	(       self						:				Tuple						,					A						:				str       ):
             from .file_utils import hash_url_to_filename

             # Path where we extract compressed archives
             # We extract in the cache dir, and get the extracted path name by hashing the original path"
             _UpperCAmelCase     :  Dict							      =    os.path.abspath(A       )
             return os.path.join(self.extract_dir						,					hash_url_to_filename(A       )       )




      def      _A	(       self						:				int						,					A						:				str						,					A						:				bool       ):
             return force_extract or (
                 not os.path.isfile(A       ) and not (os.path.isdir(A       ) and os.listdir(A       ))
             )





      def      _A	(       self						:				Optional[int]						,					A						:				str						,					A						:				bool = False       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.extractor.infer_extractor_format(A       )
             if not extractor_format:
                    return input_path
             _UpperCAmelCase     :  Optional[Any]							      =    self._get_output_path(A       )
             if self._do_extract(A						,					A       ):
                    self.extractor.extract(A						,					A						,					A       )
             return output_path

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @classmethod
      @abstractmethod
      def      _A	(       cls						:				str						,					A						:				Union[Path, str]						,					**A						:				Dict       ):
             ...





      @staticmethod
      @abstractmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             ...

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[bytes]						=						[]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				int       ):
             with open(A						,					"rb"       ) as f:
                    return f.read(A       )





      @classmethod
      def      _A	(       cls						:				Any						,					A						:				Union[Path, str]						,					A						:				bytes = b""       ):
             if not magic_number:
                    _UpperCAmelCase     :  Any							      =    max(len(A       ) for cls_magic_number in cls.magic_numbers       )
                    try:
                           _UpperCAmelCase     :  int							      =    cls.read_magic_number(A						,					A       )
                    except OSError:
                           return False
             return any(magic_number.startswith(A       ) for cls_magic_number in cls.magic_numbers       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @classmethod
      def      _A	(       cls						:				str						,					A						:				Union[Path, str]						,					**A						:				List[Any]       ):
             return tarfile.is_tarfile(A       )




      @staticmethod
      def      _A	(       A						:				Union[str, Any]						,					A						:				str       ):

             def resolved(A						:				str       ) -> str:
                    return os.path.realpath(os.path.abspath(A       )       )

             def badpath(A						:				str						,					A						:				str       ) -> bool:
                    # joinpath will ignore base if path is absolute
                    return not resolved(os.path.join(A						,					A       )       ).startswith(A       )

             def badlink(A						:				str						,					A						:				str       ) -> bool:
                    # Links are interpreted relative to the directory containing the link
                    _UpperCAmelCase     :  List[str]							      =    resolved(os.path.join(A						,					os.path.dirname(info.name       )       )       )
                    return badpath(info.linkname						,					base=A       )

             _UpperCAmelCase     :  Optional[int]							      =    resolved(A       )

             for finfo in members:
                    if badpath(finfo.name						,					A       ):
                           logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)"""       )
                    elif finfo.issym() and badlink(A						,					A       ):
                           logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"""       )
                    elif finfo.islnk() and badlink(A						,					A       ):
                           logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"""       )
                    else:
                           yield finfo





      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             os.makedirs(A						,					exist_ok=A       )
             _UpperCAmelCase     :  int							      =    tarfile.open(A       )
             tar_file.extractall(A						,					members=TarExtractor.safemembers(A						,					A       )       )
             tar_file.close()

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						[b"\x1F\x8B"]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             with gzip.open(A						,					"rb"       ) as gzip_file:
                    with open(A						,					"wb"       ) as extracted_file:
                           shutil.copyfileobj(A						,					A       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						[
          b"PK\x03\x04",
          b"PK\x05\x06",  # empty archive
          b"PK\x07\x08",  # spanned archive
      ]




      @classmethod
      def      _A	(       cls						:				Dict						,					A						:				Union[Path, str]						,					A						:				bytes = b""       ):
             if super().is_extractable(A						,					magic_number=A       ):
                    return True
             try:
                    # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
                    # From: https://github.com/python/cpython/pull/5053
                    from zipfile import (
                        _CD_SIGNATURE,
                        _ECD_DISK_NUMBER,
                        _ECD_DISK_START,
                        _ECD_ENTRIES_TOTAL,
                        _ECD_OFFSET,
                        _ECD_SIZE,
                        _EndRecData,
                        sizeCentralDir,
                        stringCentralDir,
                        structCentralDir,
                    )

                    with open(A						,					"rb"       ) as fp:
                           _UpperCAmelCase     :  Tuple							      =    _EndRecData(A       )
                           if endrec:
                                  if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
                                         return True  # Empty zipfiles are still zipfiles
                                  elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
                                         fp.seek(endrec[_ECD_OFFSET]       )  # Central directory is on the same disk
                                         if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
                                                _UpperCAmelCase     :  Dict							      =    fp.read(A       )  # CD is where we expect it to be
                                                if len(A       ) == sizeCentralDir:
                                                       _UpperCAmelCase     :  Any							      =    struct.unpack(A						,					A       )  # CD is the right size
                                                       if centdir[_CD_SIGNATURE] == stringCentralDir:
                                                              return True  # First central directory entry  has correct magic number
                    return False
             except Exception:  # catch all errors in case future python versions change the zipfile internals
                    return False





      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             os.makedirs(A						,					exist_ok=A       )
             with zipfile.ZipFile(A						,					"r"       ) as zip_file:
                    zip_file.extractall(A       )
                    zip_file.close()

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						[b"\xFD\x37\x7A\x58\x5A\x00"]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             with lzma.open(A       ) as compressed_file:
                    with open(A						,					"wb"       ) as extracted_file:
                           shutil.copyfileobj(A						,					A       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						[b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"]  # RAR_ID  # RAR5_ID




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             if not config.RARFILE_AVAILABLE:
                    raise ImportError("Please pip install rarfile"       )
             import rarfile

             os.makedirs(A						,					exist_ok=A       )
             _UpperCAmelCase     :  List[str]							      =    rarfile.RarFile(A       )
             rf.extractall(A       )
             rf.close()

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						[b"\x28\xb5\x2F\xFD"]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             if not config.ZSTANDARD_AVAILABLE:
                    raise ImportError("Please pip install zstandard"       )
             import zstandard as zstd

             _UpperCAmelCase     :  Optional[Any]							      =    zstd.ZstdDecompressor()
             with open(A						,					"rb"       ) as ifh, open(A						,					"wb"       ) as ofh:
                    dctx.copy_stream(A						,					A       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						[b"\x42\x5A\x68"]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             with bza.open(A						,					"rb"       ) as compressed_file:
                    with open(A						,					"wb"       ) as extracted_file:
                           shutil.copyfileobj(A						,					A       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						[b"\x37\x7A\xBC\xAF\x27\x1C"]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             if not config.PY7ZR_AVAILABLE:
                    raise ImportError("Please pip install py7zr"       )
             import pyazr

             os.makedirs(A						,					exist_ok=A       )
             with pyazr.SevenZipFile(A						,					"r"       ) as archive:
                    archive.extractall(A       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						[b"\x04\x22\x4D\x18"]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             if not config.LZ4_AVAILABLE:
                    raise ImportError("Please pip install lz4"       )
             import lza.frame

             with lza.frame.open(A						,					"rb"       ) as compressed_file:
                    with open(A						,					"wb"       ) as extracted_file:
                           shutil.copyfileobj(A						,					A       )

class 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  Dict[str, Type[BaseExtractor]]						=						{
          "tar": TarExtractor,
          "gzip": GzipExtractor,
          "zip": ZipExtractor,
          "xz": XzExtractor,
          "rar": RarExtractor,
          "zstd": ZstdExtractor,
          "bz2": BzipaExtractor,
          "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
          "lz4": LzaExtractor,  # <Added version="2.4.0"/>
      }




      @classmethod
      def      _A	(       cls						:				List[Any]       ):
             return max(
                 len(A       )
                 for extractor in cls.extractors.values()
                 if issubclass(A						,					A       )
                 for extractor_magic_number in extractor.magic_numbers       )




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				int       ):
             try:
                    return MagicNumberBaseExtractor.read_magic_number(A						,					magic_number_length=A       )
             except OSError:
                    return b""




      @classmethod
      def      _A	(       cls						:				Optional[Any]						,					A						:				Union[Path, str]						,					A						:				bool = False       ):
             warnings.warn(
                 "Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
                 "Use 'infer_extractor_format' instead."						,					category=A						,					)
             _UpperCAmelCase     :  Union[str, Any]							      =    cls.infer_extractor_format(A       )
             if extractor_format:
                    return True if not return_extractor else (True, cls.extractors[extractor_format])
             return False if not return_extractor else (False, None)




      @classmethod
      def      _A	(       cls						:				Dict						,					A						:				Union[Path, str]       ):  # <Added version="2.4.0"/>
             _UpperCAmelCase     :  Optional[int]							      =    cls._get_magic_number_max_length()
             _UpperCAmelCase     :  str							      =    cls._read_magic_number(A						,					A       )
             for extractor_format, extractor in cls.extractors.items():
                    if extractor.is_extractable(A						,					magic_number=A       ):
                           return extractor_format





      @classmethod
      def      _A	(       cls						:				List[str]						,					A						:				Union[Path, str]						,					A						:				Union[Path, str]						,					A						:				Optional[str] = None						,					A						:				Optional[BaseExtractor] = "deprecated"						,					):
             os.makedirs(os.path.dirname(A       )						,					exist_ok=A       )
             # Prevent parallel extractions
             _UpperCAmelCase     :  Tuple							      =    str(Path(A       ).with_suffix(".lock"       )       )
             with FileLock(A       ):
                    shutil.rmtree(A						,					ignore_errors=A       )
                    if extractor_format or extractor != "deprecated":
                           if extractor != "deprecated" or not isinstance(A						,					A       ):  # passed as positional arg
                                  warnings.warn(
                                      "Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
                                      "Use 'extractor_format' instead."						,					category=A						,					)
                                  _UpperCAmelCase     :  Tuple							      =    extractor if extractor != "deprecated" else extractor_format
                           else:
                                  _UpperCAmelCase     :  Tuple							      =    cls.extractors[extractor_format]
                           return extractor.extract(A						,					A       )
                    else:
                           warnings.warn(
                               "Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
                               "exception in 3.0.0."						,					category=A						,					)
                           for extractor in cls.extractors.values():
                                  if extractor.is_extractable(A       ):
                                         return extractor.extract(A						,					A       )

