"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = 42






class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = True







					@register_to_config
					def __init__(				self   :	Dict    ,lowerCamelCase__   :	int = 3    ,lowerCamelCase__   :	int = 3    ,lowerCamelCase__   :	Tuple[str] = ("DownEncoderBlock2D",)    ,lowerCamelCase__   :	Tuple[str] = ("UpDecoderBlock2D",)    ,lowerCamelCase__   :	Tuple[int] = (64,)    ,lowerCamelCase__   :	int = 1    ,lowerCamelCase__   :	str = "silu"    ,lowerCamelCase__   :	int = 4    ,lowerCamelCase__   :	int = 32    ,lowerCamelCase__   :	int = 32    ,lowerCamelCase__   :	float = 0.1_8_2_1_5    ,):
									super().__init__()

									# pass init params to Encoder
									UpperCAmelCase__             =							Encoder(
									    in_channels=lowerCamelCase__    ,out_channels=lowerCamelCase__    ,down_block_types=lowerCamelCase__    ,block_out_channels=lowerCamelCase__    ,layers_per_block=lowerCamelCase__    ,act_fn=lowerCamelCase__    ,norm_num_groups=lowerCamelCase__    ,double_z=lowerCamelCase__    ,)

									# pass init params to Decoder
									UpperCAmelCase__             =							Decoder(
									    in_channels=lowerCamelCase__    ,out_channels=lowerCamelCase__    ,up_block_types=lowerCamelCase__    ,block_out_channels=lowerCamelCase__    ,layers_per_block=lowerCamelCase__    ,norm_num_groups=lowerCamelCase__    ,act_fn=lowerCamelCase__    ,)

									UpperCAmelCase__             =							nn.Convad(2 * latent_channels    ,2 * latent_channels    ,1			)
									UpperCAmelCase__             =							nn.Convad(lowerCamelCase__    ,lowerCamelCase__    ,1			)

									UpperCAmelCase__             =							False
									UpperCAmelCase__             =							False

									# only relevant if vae tiling is enabled
									UpperCAmelCase__             =							self.config.sample_size
									UpperCAmelCase__             =							(
									    self.config.sample_size[0]
									    if isinstance(self.config.sample_size    ,(list, tuple)			)
									    else self.config.sample_size
									)
									UpperCAmelCase__             =							int(sample_size / (2 ** (len(self.config.block_out_channels			) - 1))			)
									UpperCAmelCase__             =							0.2_5







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	int=False			):
									if isinstance(lowerCamelCase__    ,(Encoder, Decoder)			):
													UpperCAmelCase__             =							value







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	bool = True			):
									UpperCAmelCase__             =							use_tiling







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									self.enable_tiling(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							True







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							False







					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							{}

									def fn_recursive_add_processors(lowerCamelCase__   :	str    ,lowerCamelCase__   :	torch.nn.Module    ,lowerCamelCase__   :	Dict[str, AttentionProcessor]			):
													if hasattr(lowerCamelCase__    ,'set_processor'			):
																	UpperCAmelCase__             =							module.processor

													for sub_name, child in module.named_children():
																	fn_recursive_add_processors(f'''{name}.{sub_name}'''    ,lowerCamelCase__    ,lowerCamelCase__			)

													return processors

									for name, module in self.named_children():
													fn_recursive_add_processors(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)

									return processors







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Union[AttentionProcessor, Dict[str, AttentionProcessor]]			):
									UpperCAmelCase__             =							len(self.attn_processors.keys()			)

									if isinstance(lowerCamelCase__    ,lowerCamelCase__			) and len(lowerCamelCase__			) != count:
													raise ValueError(
													    f'''A dict of processors was passed, but the number of processors {len(lowerCamelCase__			)} does not match the'''
													    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''			)

									def fn_recursive_attn_processor(lowerCamelCase__   :	str    ,lowerCamelCase__   :	torch.nn.Module    ,lowerCamelCase__   :	str			):
													if hasattr(lowerCamelCase__    ,'set_processor'			):
																	if not isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																					module.set_processor(lowerCamelCase__			)
																	else:
																					module.set_processor(processor.pop(f'''{name}.processor'''			)			)

													for sub_name, child in module.named_children():
																	fn_recursive_attn_processor(f'''{name}.{sub_name}'''    ,lowerCamelCase__    ,lowerCamelCase__			)

									for name, module in self.named_children():
													fn_recursive_attn_processor(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									self.set_attn_processor(AttnProcessor()			)







					@apply_forward_hook
					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = True			):
									if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
													return self.tiled_encode(lowerCamelCase__    ,return_dict=lowerCamelCase__			)

									if self.use_slicing and x.shape[0] > 1:
													UpperCAmelCase__             =							[self.encoder(lowerCamelCase__			) for x_slice in x.split(1			)]
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							self.encoder(lowerCamelCase__			)

									UpperCAmelCase__             =							self.quant_conv(lowerCamelCase__			)
									UpperCAmelCase__             =							DiagonalGaussianDistribution(lowerCamelCase__			)

									if not return_dict:
													return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = True			):
									if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
													return self.tiled_decode(lowerCamelCase__    ,return_dict=lowerCamelCase__			)

									UpperCAmelCase__             =							self.post_quant_conv(lowerCamelCase__			)
									UpperCAmelCase__             =							self.decoder(lowerCamelCase__			)

									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=lowerCamelCase__			)







					@apply_forward_hook
					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = True			):
									if self.use_slicing and z.shape[0] > 1:
													UpperCAmelCase__             =							[self._decode(lowerCamelCase__			).sample for z_slice in z.split(1			)]
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							self._decode(lowerCamelCase__			).sample

									if not return_dict:
													return (decoded,)

									return DecoderOutput(sample=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[int]			):
									UpperCAmelCase__             =							min(a.shape[2]    ,b.shape[2]    ,lowerCamelCase__			)
									for y in range(lowerCamelCase__			):
													UpperCAmelCase__             =							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							min(a.shape[3]    ,b.shape[3]    ,lowerCamelCase__			)
									for x in range(lowerCamelCase__			):
													UpperCAmelCase__             =							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
									return b







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = True			):
									UpperCAmelCase__             =							int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)			)
									UpperCAmelCase__             =							int(self.tile_latent_min_size * self.tile_overlap_factor			)
									UpperCAmelCase__             =							self.tile_latent_min_size - blend_extent

									# Split the image into 512x512 tiles and encode them separately.
									UpperCAmelCase__             =							[]
									for i in range(0    ,x.shape[2]    ,lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j in range(0    ,x.shape[3]    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																	UpperCAmelCase__             =							self.encoder(lowerCamelCase__			)
																	UpperCAmelCase__             =							self.quant_conv(lowerCamelCase__			)
																	row.append(lowerCamelCase__			)
													rows.append(lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for i, row in enumerate(lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j, tile in enumerate(lowerCamelCase__			):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																					UpperCAmelCase__             =							self.blend_v(rows[i - 1][j]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	if j > 0:
																					UpperCAmelCase__             =							self.blend_h(row[j - 1]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	result_row.append(tile[:, :, :row_limit, :row_limit]			)
													result_rows.append(torch.cat(lowerCamelCase__    ,dim=3			)			)

									UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=2			)
									UpperCAmelCase__             =							DiagonalGaussianDistribution(lowerCamelCase__			)

									if not return_dict:
													return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = True			):
									UpperCAmelCase__             =							int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)			)
									UpperCAmelCase__             =							int(self.tile_sample_min_size * self.tile_overlap_factor			)
									UpperCAmelCase__             =							self.tile_sample_min_size - blend_extent

									# Split z into overlapping 64x64 tiles and decode them separately.
									# The tiles have an overlap to avoid seams between tiles.
									UpperCAmelCase__             =							[]
									for i in range(0    ,z.shape[2]    ,lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j in range(0    ,z.shape[3]    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																	UpperCAmelCase__             =							self.post_quant_conv(lowerCamelCase__			)
																	UpperCAmelCase__             =							self.decoder(lowerCamelCase__			)
																	row.append(lowerCamelCase__			)
													rows.append(lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for i, row in enumerate(lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j, tile in enumerate(lowerCamelCase__			):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																					UpperCAmelCase__             =							self.blend_v(rows[i - 1][j]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	if j > 0:
																					UpperCAmelCase__             =							self.blend_h(row[j - 1]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	result_row.append(tile[:, :, :row_limit, :row_limit]			)
													result_rows.append(torch.cat(lowerCamelCase__    ,dim=3			)			)

									UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=2			)
									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[torch.Generator] = None    ,):
									UpperCAmelCase__             =							sample
									UpperCAmelCase__             =							self.encode(lowerCamelCase__			).latent_dist
									if sample_posterior:
													UpperCAmelCase__             =							posterior.sample(generator=lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							posterior.mode()
									UpperCAmelCase__             =							self.decode(lowerCamelCase__			).sample

									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=lowerCamelCase__			)




'''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       )

