


"""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


__lowerCamelCase      :Union[str, Any]	    =		get_logger(__name__)


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Dict     ,		__a:						Optional[str] = None	)->    Tuple:
           lowerCamelCase					:					Optional[Any]        =	(
               os.path.join(__a     ,		config.EXTRACTED_DATASETS_DIR	) if cache_dir else config.EXTRACTED_DATASETS_PATH
           )
           lowerCamelCase					:					Tuple        =	Extractor

     def 		a__						(	self:						Optional[int]     ,		__a:						str	)->    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"
           lowerCamelCase					:					List[str]        =	os.path.abspath(__a	)
           return os.path.join(self.extract_dir     ,		hash_url_to_filename(__a	)	)

     def 		a__						(	self:						str     ,		__a:						str     ,		__a:						bool	)->    bool:
           return force_extract or (
               not os.path.isfile(__a	) and not (os.path.isdir(__a	) and os.listdir(__a	))
           )







     def 		a__						(	self:						str     ,		__a:						str     ,		__a:						bool = False	)->    str:
           lowerCamelCase					:					Optional[int]        =	self.extractor.infer_extractor_format(__a	)
           if not extractor_format:
                 return input_path
           lowerCamelCase					:					List[str]        =	self._get_output_path(__a	)
           if self._do_extract(__a     ,		__a	):
                 self.extractor.extract(__a     ,		__a     ,		__a	)
           return output_path


class 			A__	(						__lowercase):


     """simple docstring"""

     @classmethod
     @abstractmethod
     def 		a__						(	cls:						Union[str, Any]     ,		__a:						Union[Path, str]     ,		**__a:						Union[str, Any]	)->    bool:
           ...







     @staticmethod
     @abstractmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           ...


class 			A__	(						__lowercase ,     __lowercase):


     """simple docstring"""
     snake_case__	:					List[bytes]													=[]

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						int	)->    List[Any]:
           with open(__a     ,		"""rb"""	) as f:
                 return f.read(__a	)







     @classmethod
     def 		a__						(	cls:						Dict     ,		__a:						Union[Path, str]     ,		__a:						bytes = b""	)->    bool:
           if not magic_number:
                 lowerCamelCase					:					Dict        =	max(len(__a	) for cls_magic_number in cls.magic_numbers	)
                 try:
                       lowerCamelCase					:					str        =	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 			A__	(						__lowercase):


     """simple docstring"""

     @classmethod
     def 		a__						(	cls:						Any     ,		__a:						Union[Path, str]     ,		**__a:						Dict	)->    bool:
           return tarfile.is_tarfile(__a	)

     @staticmethod
     def 		a__						(	__a:						Any     ,		__a:						Any	)->    int:

           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:						int     ,		__a:						str	) -> bool:
                 # Links are interpreted relative to the directory containing the link
                 lowerCamelCase					:					Optional[int]        =	resolved(os.path.join(__a     ,		os.path.dirname(info.name	)	)	)
                 return badpath(info.linkname     ,		base=__a	)

           lowerCamelCase					:					Dict        =	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]	)->    None:
           os.makedirs(__a     ,		exist_ok=__a	)
           lowerCamelCase					:					Any        =	tarfile.open(__a	)
           tar_file.extractall(__a     ,		members=TarExtractor.safemembers(__a     ,		__a	)	)
           tar_file.close()


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=[B'''\x1F\x8B''']

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           with gzip.open(__a     ,		"""rb"""	) as gzip_file:
                 with open(__a     ,		"""wb"""	) as extracted_file:
                       shutil.copyfileobj(__a     ,		__a	)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Any													=[
         B'''PK\x03\x04''',
         B'''PK\x05\x06''',  # empty archive
         B'''PK\x07\x08''',  # spanned archive
     ]

     @classmethod
     def 		a__						(	cls:						Any     ,		__a:						Union[Path, str]     ,		__a:						bytes = b""	)->    bool:
           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:
                       lowerCamelCase					:					Dict        =	_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:
                                         lowerCamelCase					:					Union[str, Any]        =	fp.read(__a	)  # CD is where we expect it to be
                                         if len(__a	) == sizeCentralDir:
                                               lowerCamelCase					:					int        =	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]	)->    None:
           os.makedirs(__a     ,		exist_ok=__a	)
           with zipfile.ZipFile(__a     ,		"""r"""	) as zip_file:
                 zip_file.extractall(__a	)
                 zip_file.close()


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Dict													=[B'''\xFD\x37\x7A\x58\x5A\x00''']

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           with lzma.open(__a	) as compressed_file:
                 with open(__a     ,		"""wb"""	) as extracted_file:
                       shutil.copyfileobj(__a     ,		__a	)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					int													=[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]	)->    None:
           if not config.RARFILE_AVAILABLE:
                 raise ImportError("""Please pip install rarfile"""	)
           import rarfile

           os.makedirs(__a     ,		exist_ok=__a	)
           lowerCamelCase					:					Optional[int]        =	rarfile.RarFile(__a	)
           rf.extractall(__a	)
           rf.close()


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Union[str, Any]													=[B'''\x28\xb5\x2F\xFD''']

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           if not config.ZSTANDARD_AVAILABLE:
                 raise ImportError("""Please pip install zstandard"""	)
           import zstandard as zstd

           lowerCamelCase					:					Tuple        =	zstd.ZstdDecompressor()
           with open(__a     ,		"""rb"""	) as ifh, open(__a     ,		"""wb"""	) as ofh:
                 dctx.copy_stream(__a     ,		__a	)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[int]													=[B'''\x42\x5A\x68''']

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           with bza.open(__a     ,		"""rb"""	) as compressed_file:
                 with open(__a     ,		"""wb"""	) as extracted_file:
                       shutil.copyfileobj(__a     ,		__a	)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					str													=[B'''\x37\x7A\xBC\xAF\x27\x1C''']

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           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 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=[B'''\x04\x22\x4D\x18''']

     @staticmethod
     def 		a__						(	__a:						Union[Path, str]     ,		__a:						Union[Path, str]	)->    None:
           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 			A__	:


     """simple docstring"""
     #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
     snake_case__	:					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:						Optional[int]	)->    List[str]:
           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	)->    List[Any]:
           try:
                 return MagicNumberBaseExtractor.read_magic_number(__a     ,		magic_number_length=__a	)
           except OSError:
                 return b""

     @classmethod
     def 		a__						(	cls:						Optional[int]     ,		__a:						Union[Path, str]     ,		__a:						bool = False	)->    bool:
           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     ,		)
           lowerCamelCase					:					Dict        =	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:						Tuple     ,		__a:						Union[Path, str]	)->    str:  # <Added version="2.4.0"/>
           lowerCamelCase					:					Optional[int]        =	cls._get_magic_number_max_length()
           lowerCamelCase					:					int        =	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:						Any     ,		__a:						Union[Path, str]     ,		__a:						Union[Path, str]     ,		__a:						Optional[str] = None     ,		__a:						Optional[BaseExtractor] = "deprecated"     ,		)->    None:
           os.makedirs(os.path.dirname(__a	)     ,		exist_ok=__a	)
           # Prevent parallel extractions
           lowerCamelCase					:					int        =	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     ,		)
                             lowerCamelCase					:					Any        =	extractor if extractor != """deprecated""" else extractor_format
                       else:
                             lowerCamelCase					:					Optional[int]        =	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	)








"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Dict	    =		logging.get_logger()




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						LevitConfig			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						bool = True    )   ->      Dict:
      print(F'Converting {name}...'    )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        lowerCamelCase					:					Optional[Any]        =	timm.create_model("""levit_128s"""			,				pretrained=UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Dict        =	timm.create_model("""levit_128"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 192:
                  lowerCamelCase					:					Tuple        =	timm.create_model("""levit_192"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 256:
                  lowerCamelCase					:					Optional[int]        =	timm.create_model("""levit_256"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 384:
                  lowerCamelCase					:					Dict        =	timm.create_model("""levit_384"""			,				pretrained=UpperCamelCase__    )

            from_model.eval()
            lowerCamelCase					:					Optional[Any]        =	LevitForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Tuple        =	OrderedDict()

            lowerCamelCase					:					Optional[Any]        =	from_model.state_dict()
            lowerCamelCase					:					str        =	list(from_model.state_dict().keys()    )
            lowerCamelCase					:					List[Any]        =	list(our_model.state_dict().keys()    )
            print(len(UpperCamelCase__    )			,				len(UpperCamelCase__    )    )
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					str        =	weights[og_keys[i]]
            our_model.load_state_dict(UpperCamelCase__    )

            lowerCamelCase					:					int        =	torch.randn((2, 3, 224, 224)    )
            lowerCamelCase					:					Any        =	from_model(UpperCamelCase__    )
            lowerCamelCase					:					List[Any]        =	our_model(UpperCamelCase__    ).logits

      assert torch.allclose(UpperCamelCase__			,				UpperCamelCase__    ), "The model logits don't match the original one."

      lowerCamelCase					:					Dict        =	name
      print(UpperCamelCase__    )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name    )
            lowerCamelCase					:					Optional[int]        =	LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name    )

            print(F'Pushed {checkpoint_name}'    )




def   snake_case		(     UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						bool = True    )   ->      Optional[int]:
      lowerCamelCase					:					Optional[Any]        =	"""imagenet-1k-id2label.json"""
      lowerCamelCase					:					List[Any]        =	1000
      lowerCamelCase					:					Dict        =	(1, num_labels)

      lowerCamelCase					:					List[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[int]        =	num_labels
      lowerCamelCase					:					List[str]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Any        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}

      lowerCamelCase					:					List[Any]        =	idalabel
      lowerCamelCase					:					str        =	{v: k for k, v in idalabel.items()}

      lowerCamelCase					:					Tuple        =	partial(UpperCamelCase__			,				num_labels=UpperCamelCase__			,				idalabel=UpperCamelCase__			,				labelaid=UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	{
          """levit-128S""": 128,
          """levit-128""": 128,
          """levit-192""": 192,
          """levit-256""": 256,
          """levit-384""": 384,
      }

      lowerCamelCase					:					List[Any]        =	{
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 6, 8]			,				depths=[2, 3, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 8, 12]			,				depths=[4, 4, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]			,				num_attention_heads=[3, 5, 6]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]			,				num_attention_heads=[4, 6, 8]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]			,				num_attention_heads=[6, 9, 12]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0.1			,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				names_to_config[model_name]			,				UpperCamelCase__			,				UpperCamelCase__    )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      return config, expected_shape


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default=None,
        type=str,
        help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='levit-dump-folder/',
        type=Path,
        required=False,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )

    __lowerCamelCase      :List[Any]	    =		parser.parse_args()
    __lowerCamelCase      :Path	    =		args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




