

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


lowercase_             =     logging.get_logger(__name__)  # pylint: disable=invalid-name





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     )						->				Tuple:
        '''simple docstring'''






        super().__init__()

        if safety_checker is None:
              logger.warning(
                  F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                  ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
                  ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
                  ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
                  ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
                  ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'      )

        self.register_modules(
            speech_model=lowerCAmelCase							,     speech_processor=lowerCAmelCase							,     vae=lowerCAmelCase							,     text_encoder=lowerCAmelCase							,     tokenizer=lowerCAmelCase							,     unet=lowerCAmelCase							,     scheduler=lowerCAmelCase							,     feature_extractor=lowerCAmelCase							,     )




  def 						A__  (       self							,     lowerCAmelCase = "auto"      )						->				Dict:
        '''simple docstring'''






        if slice_size == "auto":
              _lowercase			 =self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(lowerCAmelCase      )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        self.enable_attention_slicing(lowerCAmelCase      )




  @torch.no_grad()
  def __call__(       self							,     lowerCAmelCase							,     lowerCAmelCase=16_000							,     lowerCAmelCase = 512							,     lowerCAmelCase = 512							,     lowerCAmelCase = 50							,     lowerCAmelCase = 7.5							,     lowerCAmelCase = None							,     lowerCAmelCase = 1							,     lowerCAmelCase = 0.0							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = "pil"							,     lowerCAmelCase = True							,     lowerCAmelCase = None							,     lowerCAmelCase = 1							,     **lowerCAmelCase							,     )						->				Any:
        '''simple docstring'''






        _lowercase			 =self.speech_processor.feature_extractor(
            lowerCAmelCase							,     return_tensors='pt'							,     sampling_rate=lowerCAmelCase      ).input_features.to(self.device      )
        _lowercase			 =self.speech_model.generate(lowerCAmelCase							,     max_length=480_000      )

        _lowercase			 =self.speech_processor.tokenizer.batch_decode(lowerCAmelCase							,     skip_special_tokens=lowerCAmelCase							,     normalize=lowerCAmelCase      )[
            0
        ]

        if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =1
        elif isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =len(lowerCAmelCase      )
        else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase      )}'''      )

        if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''      )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(lowerCAmelCase							,     lowerCAmelCase      ) or callback_steps <= 0)
        ):
              raise ValueError(
                  F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  F''' {type(lowerCAmelCase      )}.'''      )

        # get prompt text embeddings
        _lowercase			 =self.tokenizer(
            lowerCAmelCase							,     padding='max_length'							,     max_length=self.tokenizer.model_max_length							,     return_tensors='pt'							,     )
        _lowercase			 =text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              _lowercase			 =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
              logger.warning(
                  'The following part of your input was truncated because CLIP can only handle sequences up to'
                  F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''      )
              _lowercase			 =text_input_ids[:, : self.tokenizer.model_max_length]
        _lowercase			 =self.text_encoder(text_input_ids.to(self.device      )      )[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        _lowercase , _lowercase , _lowercase			 =text_embeddings.shape
        _lowercase			 =text_embeddings.repeat(1							,     lowerCAmelCase							,     1      )
        _lowercase			 =text_embeddings.view(bs_embed * num_images_per_prompt							,     lowerCAmelCase							,     -1      )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        _lowercase			 =guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
              _lowercase			 =42
              if negative_prompt is None:
                    _lowercase			 =[''] * batch_size
              elif type(lowerCAmelCase      ) is not type(lowerCAmelCase      ):
                    raise TypeError(
                        F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase      )} !='''
                        F''' {type(lowerCAmelCase      )}.'''      )
              elif isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
                    _lowercase			 =[negative_prompt]
              elif batch_size != len(lowerCAmelCase      ):
                    raise ValueError(
                        F'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase      )}, but `prompt`:'''
                        F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                        ' the batch size of `prompt`.'      )
              else:
                    _lowercase			 =negative_prompt

              _lowercase			 =text_input_ids.shape[-1]
              _lowercase			 =self.tokenizer(
                  lowerCAmelCase							,     padding='max_length'							,     max_length=lowerCAmelCase							,     truncation=lowerCAmelCase							,     return_tensors='pt'							,     )
              _lowercase			 =self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              _lowercase			 =uncond_embeddings.shape[1]
              _lowercase			 =uncond_embeddings.repeat(1							,     lowerCAmelCase							,     1      )
              _lowercase			 =uncond_embeddings.view(batch_size * num_images_per_prompt							,     lowerCAmelCase							,     -1      )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              _lowercase			 =torch.cat([uncond_embeddings, text_embeddings]      )

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        _lowercase			 =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        _lowercase			 =text_embeddings.dtype
        if latents is None:
              if self.device.type == "mps":
                    # randn does not exist on mps
                    _lowercase			 =torch.randn(lowerCAmelCase							,     generator=lowerCAmelCase							,     device='cpu'							,     dtype=lowerCAmelCase      ).to(
                        self.device      )
              else:
                    _lowercase			 =torch.randn(lowerCAmelCase							,     generator=lowerCAmelCase							,     device=self.device							,     dtype=lowerCAmelCase      )
        else:
              if latents.shape != latents_shape:
                    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''      )
              _lowercase			 =latents.to(self.device      )

        # set timesteps
        self.scheduler.set_timesteps(lowerCAmelCase      )

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        _lowercase			 =self.scheduler.timesteps.to(self.device      )

        # scale the initial noise by the standard deviation required by the scheduler
        _lowercase			 =latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        _lowercase			 ='eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
        _lowercase			 ={}
        if accepts_eta:
              _lowercase			 =eta

        for i, t in enumerate(self.progress_bar(lowerCAmelCase      )      ):
              # expand the latents if we are doing classifier free guidance
              _lowercase			 =torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
              _lowercase			 =self.scheduler.scale_model_input(lowerCAmelCase							,     lowerCAmelCase      )

              # predict the noise residual
              _lowercase			 =self.unet(lowerCAmelCase							,     lowerCAmelCase							,     encoder_hidden_states=lowerCAmelCase      ).sample

              # perform guidance
              if do_classifier_free_guidance:
                    _lowercase , _lowercase			 =noise_pred.chunk(2      )
                    _lowercase			 =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              _lowercase			 =self.scheduler.step(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     **lowerCAmelCase      ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                    callback(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )

        _lowercase			 =1 / 0.18215 * latents
        _lowercase			 =self.vae.decode(lowerCAmelCase      ).sample

        _lowercase			 =(image / 2 + 0.5).clamp(0							,     1      )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        _lowercase			 =image.cpu().permute(0							,     2							,     3							,     1      ).float().numpy()

        if output_type == "pil":
              _lowercase			 =self.numpy_to_pil(lowerCAmelCase      )

        if not return_dict:
              return image

        return StableDiffusionPipelineOutput(images=lowerCAmelCase							,     nsfw_content_detected=lowerCAmelCase      )





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


lowercase_             =     get_logger(__name__)





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase = None      )						->				str:
        '''simple docstring'''






        _lowercase			 =(
            os.path.join(lowerCAmelCase							,     config.EXTRACTED_DATASETS_DIR      ) if cache_dir else config.EXTRACTED_DATASETS_PATH
        )
        _lowercase			 =Extractor




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        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"
        _lowercase			 =os.path.abspath(lowerCAmelCase      )
        return os.path.join(self.extract_dir							,     hash_url_to_filename(lowerCAmelCase      )      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				bool:
        '''simple docstring'''






        return force_extract or (
            not os.path.isfile(lowerCAmelCase      ) and not (os.path.isdir(lowerCAmelCase      ) and os.listdir(lowerCAmelCase      ))
        )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				str:
        '''simple docstring'''






        _lowercase			 =self.extractor.infer_extractor_format(lowerCAmelCase      )
        if not extractor_format:
              return input_path
        _lowercase			 =self._get_output_path(lowerCAmelCase      )
        if self._do_extract(lowerCAmelCase							,     lowerCAmelCase      ):
              self.extractor.extract(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )
        return output_path





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  @classmethod
  @abstractmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				bool:
        '''simple docstring'''






        ...




  @staticmethod
  @abstractmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        ...





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE							):
  _a		    =						[]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






        with open(lowerCAmelCase							,     'rb'      ) as f:
              return f.read(lowerCAmelCase      )




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     lowerCAmelCase = b""      )						->				bool:
        '''simple docstring'''






        if not magic_number:
              _lowercase			 =max(len(lowerCAmelCase      ) for cls_magic_number in cls.magic_numbers      )
              try:
                    _lowercase			 =cls.read_magic_number(lowerCAmelCase							,     lowerCAmelCase      )
              except OSError:
                    return False
        return any(magic_number.startswith(lowerCAmelCase      ) for cls_magic_number in cls.magic_numbers      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				bool:
        '''simple docstring'''






        return tarfile.is_tarfile(lowerCAmelCase      )




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






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

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

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

        _lowercase			 =resolved(lowerCAmelCase      )

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




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        os.makedirs(lowerCAmelCase							,     exist_ok=lowerCAmelCase      )
        _lowercase			 =tarfile.open(lowerCAmelCase      )
        tar_file.extractall(lowerCAmelCase							,     members=TarExtractor.safemembers(lowerCAmelCase							,     lowerCAmelCase      )      )
        tar_file.close()





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""\x1F\x8B"""]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        with gzip.open(lowerCAmelCase							,     'rb'      ) as gzip_file:
              with open(lowerCAmelCase							,     'wb'      ) as extracted_file:
                    shutil.copyfileobj(lowerCAmelCase							,     lowerCAmelCase      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[
      b"""PK\x03\x04""",
      b"""PK\x05\x06""",  # empty archive
      b"""PK\x07\x08""",  # spanned archive
  ]




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     lowerCAmelCase = b""      )						->				bool:
        '''simple docstring'''






        if super().is_extractable(lowerCAmelCase							,     magic_number=lowerCAmelCase      ):
              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(lowerCAmelCase							,     'rb'      ) as fp:
                    _lowercase			 =_EndRecData(lowerCAmelCase      )
                    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:
                                      _lowercase			 =fp.read(lowerCAmelCase      )  # CD is where we expect it to be
                                      if len(lowerCAmelCase      ) == sizeCentralDir:
                                            _lowercase			 =struct.unpack(lowerCAmelCase							,     lowerCAmelCase      )  # 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__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        os.makedirs(lowerCAmelCase							,     exist_ok=lowerCAmelCase      )
        with zipfile.ZipFile(lowerCAmelCase							,     'r'      ) as zip_file:
              zip_file.extractall(lowerCAmelCase      )
              zip_file.close()





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""\xFD\x37\x7A\x58\x5A\x00"""]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        with lzma.open(lowerCAmelCase      ) as compressed_file:
              with open(lowerCAmelCase							,     'wb'      ) as extracted_file:
                    shutil.copyfileobj(lowerCAmelCase							,     lowerCAmelCase      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""Rar!\x1a\x07\x00""", b"""Rar!\x1a\x07\x01\x00"""]  # RAR_ID  # RAR5_ID




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if not config.RARFILE_AVAILABLE:
              raise ImportError('Please pip install rarfile'      )
        import rarfile

        os.makedirs(lowerCAmelCase							,     exist_ok=lowerCAmelCase      )
        _lowercase			 =rarfile.RarFile(lowerCAmelCase      )
        rf.extractall(lowerCAmelCase      )
        rf.close()





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""\x28\xb5\x2F\xFD"""]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if not config.ZSTANDARD_AVAILABLE:
              raise ImportError('Please pip install zstandard'      )
        import zstandard as zstd

        _lowercase			 =zstd.ZstdDecompressor()
        with open(lowerCAmelCase							,     'rb'      ) as ifh, open(lowerCAmelCase							,     'wb'      ) as ofh:
              dctx.copy_stream(lowerCAmelCase							,     lowerCAmelCase      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""\x42\x5A\x68"""]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        with bza.open(lowerCAmelCase							,     'rb'      ) as compressed_file:
              with open(lowerCAmelCase							,     'wb'      ) as extracted_file:
                    shutil.copyfileobj(lowerCAmelCase							,     lowerCAmelCase      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""\x37\x7A\xBC\xAF\x27\x1C"""]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if not config.PY7ZR_AVAILABLE:
              raise ImportError('Please pip install py7zr'      )
        import pyazr

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





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						[b"""\x04\x22\x4D\x18"""]




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if not config.LZ4_AVAILABLE:
              raise ImportError('Please pip install lz4'      )
        import lza.frame

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





class 				__lowerCAmelCase  :
  #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
  _a		    =						{
      "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      )						->				str:
        '''simple docstring'''






        return max(
            len(lowerCAmelCase      )
            for extractor in cls.extractors.values()
            if issubclass(lowerCAmelCase							,     lowerCAmelCase      )
            for extractor_magic_number in extractor.magic_numbers      )




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				Optional[Any]:
        '''simple docstring'''






        try:
              return MagicNumberBaseExtractor.read_magic_number(lowerCAmelCase							,     magic_number_length=lowerCAmelCase      )
        except OSError:
              return b""




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				bool:
        '''simple docstring'''






        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=lowerCAmelCase							,     )
        _lowercase			 =cls.infer_extractor_format(lowerCAmelCase      )
        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							,     lowerCAmelCase      )						->				str:  # <Added version="2.4.0"/>
        '''simple docstring'''






        _lowercase			 =cls._get_magic_number_max_length()
        _lowercase			 =cls._read_magic_number(lowerCAmelCase							,     lowerCAmelCase      )
        for extractor_format, extractor in cls.extractors.items():
              if extractor.is_extractable(lowerCAmelCase							,     magic_number=lowerCAmelCase      ):
                    return extractor_format




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     lowerCAmelCase = "deprecated"							,     )						->				None:
        '''simple docstring'''






        os.makedirs(os.path.dirname(lowerCAmelCase      )							,     exist_ok=lowerCAmelCase      )
        # Prevent parallel extractions
        _lowercase			 =str(Path(lowerCAmelCase      ).with_suffix('.lock'      )      )
        with FileLock(lowerCAmelCase      ):
              shutil.rmtree(lowerCAmelCase							,     ignore_errors=lowerCAmelCase      )
              if extractor_format or extractor != "deprecated":
                    if extractor != "deprecated" or not isinstance(lowerCAmelCase							,     lowerCAmelCase      ):  # 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=lowerCAmelCase							,     )
                          _lowercase			 =extractor if extractor != 'deprecated' else extractor_format
                    else:
                          _lowercase			 =cls.extractors[extractor_format]
                    return extractor.extract(lowerCAmelCase							,     lowerCAmelCase      )
              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=lowerCAmelCase							,     )
                    for extractor in cls.extractors.values():
                          if extractor.is_extractable(lowerCAmelCase      ):
                                return extractor.extract(lowerCAmelCase							,     lowerCAmelCase      )


