'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""LayoutLMv2Config""", """LayoutLMv3Config"""}

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

      __UpperCamelCase:  str						=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      __UpperCamelCase:  Any						=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            __UpperCamelCase:  int						=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            __UpperCamelCase:  List[Any]						=						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }




      def      _A	(       self						:				Union[str, Any]						,					A						:				int						,					A						:				Tuple						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    ZeroShotClassificationPipeline(
                 model=A						,					tokenizer=A						,					candidate_labels=["polics", "health"]       )
             return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]




      def      _A	(       self						:				Any						,					A						:				Optional[int]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels="politics"       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             # No kwarg
             _UpperCAmelCase     :  int							      =    classifier("Who are you voting for in 2020?"						,					["politics"]       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             _UpperCAmelCase     :  Dict							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels=["politics"]       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             _UpperCAmelCase     :  Tuple							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels="politics, public health"       )
             self.assertEqual(
                 A						,					{"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}       )
             self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]       )       )						,					1.0       )

             _UpperCAmelCase     :  Any							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health"]       )
             self.assertEqual(
                 A						,					{"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}       )
             self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]       )       )						,					1.0       )

             _UpperCAmelCase     :  Tuple							      =    classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels="politics"						,					hypothesis_template="This text is about {}"       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             # https://github.com/huggingface/transformers/issues/13846
             _UpperCAmelCase     :  Union[str, Any]							      =    classifier(["I am happy"]						,					["positive", "negative"]       )
             self.assertEqual(
                 A						,					[
                     {"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}
                     for i in range(1       )
                 ]						,					)
             _UpperCAmelCase     :  Dict							      =    classifier(["I am happy", "I am sad"]						,					["positive", "negative"]       )
             self.assertEqual(
                 A						,					[
                     {"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}
                     for i in range(2       )
                 ]						,					)

             with self.assertRaises(A       ):
                    classifier(""						,					candidate_labels="politics"       )

             with self.assertRaises(A       ):
                    classifier(A						,					candidate_labels="politics"       )

             with self.assertRaises(A       ):
                    classifier("Who are you voting for in 2020?"						,					candidate_labels=""       )

             with self.assertRaises(A       ):
                    classifier("Who are you voting for in 2020?"						,					candidate_labels=A       )

             with self.assertRaises(A       ):
                    classifier(
                        "Who are you voting for in 2020?"						,					candidate_labels="politics"						,					hypothesis_template="Not formatting template"						,					)

             with self.assertRaises(A       ):
                    classifier(
                        "Who are you voting for in 2020?"						,					candidate_labels="politics"						,					hypothesis_template=A						,					)

             self.run_entailment_id(A       )




      def      _A	(       self						:				Tuple						,					A						:				Pipeline       ):
             _UpperCAmelCase     :  Tuple							      =    zero_shot_classifier.model.config
             _UpperCAmelCase     :  Optional[int]							      =    config.labelaid
             _UpperCAmelCase     :  Dict							      =    zero_shot_classifier.entailment_id

             _UpperCAmelCase     :  Optional[Any]							      =    {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
             self.assertEqual(zero_shot_classifier.entailment_id						,					-1       )

             _UpperCAmelCase     :  Any							      =    {"entailment": 0, "neutral": 1, "contradiction": 2}
             self.assertEqual(zero_shot_classifier.entailment_id						,					0       )

             _UpperCAmelCase     :  List[Any]							      =    {"ENTAIL": 0, "NON-ENTAIL": 1}
             self.assertEqual(zero_shot_classifier.entailment_id						,					0       )

             _UpperCAmelCase     :  List[Any]							      =    {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
             self.assertEqual(zero_shot_classifier.entailment_id						,					2       )

             _UpperCAmelCase     :  Optional[int]							      =    original_labelaid
             self.assertEqual(A						,					zero_shot_classifier.entailment_id       )




      @require_torch
      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    pipeline(
                 "zero-shot-classification"						,					model="sshleifer/tiny-distilbert-base-cased-distilled-squad"						,					framework="pt"						,					)
             # There was a regression in 4.10 for this
             # Adding a test so we don't make the mistake again.
             # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
             zero_shot_classifier(
                 "Who are you voting for in 2020?" * 100						,					candidate_labels=["politics", "public health", "science"]       )




      @require_torch
      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    pipeline(
                 "zero-shot-classification"						,					model="sshleifer/tiny-distilbert-base-cased-distilled-squad"						,					framework="pt"						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["science", "public health", "politics"],
                     "scores": [0.333, 0.333, 0.333],
                 }						,					)




      @require_tf
      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    pipeline(
                 "zero-shot-classification"						,					model="sshleifer/tiny-distilbert-base-cased-distilled-squad"						,					framework="tf"						,					)
             _UpperCAmelCase     :  Union[str, Any]							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["science", "public health", "politics"],
                     "scores": [0.333, 0.333, 0.333],
                 }						,					)




      @slow
      @require_torch
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Any							      =    pipeline("zero-shot-classification"						,					model="roberta-large-mnli"						,					framework="pt"       )
             _UpperCAmelCase     :  Any							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["politics", "public health", "science"],
                     "scores": [0.976, 0.015, 0.009],
                 }						,					)
             _UpperCAmelCase     :  Optional[int]							      =    zero_shot_classifier(
                 "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
                 " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
                 " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
                 " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
                 " machine translation tasks show these models to be superior in quality while being more parallelizable"
                 " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
                 " English-to-German translation task, improving over the existing best results, including ensembles by"
                 " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
                 " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
                 " fraction of the training costs of the best models from the literature. We show that the Transformer"
                 " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
                 " large and limited training data."						,					candidate_labels=["machine learning", "statistics", "translation", "vision"]						,					multi_label=A						,					)
             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": (
                         "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                         " networks in an encoder-decoder configuration. The best performing models also connect the"
                         " encoder and decoder through an attention mechanism. We propose a new simple network"
                         " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                         " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                         " superior in quality while being more parallelizable and requiring significantly less time to"
                         " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                         " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                         " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                         " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                         " costs of the best models from the literature. We show that the Transformer generalizes well to"
                         " other tasks by applying it successfully to English constituency parsing both with large and"
                         " limited training data."
                     ),
                     "labels": ["translation", "machine learning", "vision", "statistics"],
                     "scores": [0.817, 0.713, 0.018, 0.018],
                 }						,					)





      @slow
      @require_tf
      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Tuple							      =    pipeline("zero-shot-classification"						,					model="roberta-large-mnli"						,					framework="tf"       )
             _UpperCAmelCase     :  Optional[int]							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["politics", "public health", "science"],
                     "scores": [0.976, 0.015, 0.009],
                 }						,					)
             _UpperCAmelCase     :  Optional[int]							      =    zero_shot_classifier(
                 "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
                 " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
                 " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
                 " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
                 " machine translation tasks show these models to be superior in quality while being more parallelizable"
                 " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
                 " English-to-German translation task, improving over the existing best results, including ensembles by"
                 " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
                 " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
                 " fraction of the training costs of the best models from the literature. We show that the Transformer"
                 " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
                 " large and limited training data."						,					candidate_labels=["machine learning", "statistics", "translation", "vision"]						,					multi_label=A						,					)
             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": (
                         "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                         " networks in an encoder-decoder configuration. The best performing models also connect the"
                         " encoder and decoder through an attention mechanism. We propose a new simple network"
                         " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                         " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                         " superior in quality while being more parallelizable and requiring significantly less time to"
                         " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                         " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                         " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                         " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                         " costs of the best models from the literature. We show that the Transformer generalizes well to"
                         " other tasks by applying it successfully to English constituency parsing both with large and"
                         " limited training data."
                     ),
                     "labels": ["translation", "machine learning", "vision", "statistics"],
                     "scores": [0.817, 0.713, 0.018, 0.018],
                 }						,					)


'''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       )

