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 						lowercase    :




       def __init__(						self   :					Dict	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       (
                os.path.join(__a	,							config.EXTRACTED_DATASETS_DIR      ) if cache_dir else config.EXTRACTED_DATASETS_PATH
            )
            SCREAMING_SNAKE_CASE    			=       Extractor




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




       def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					bool      )					->				bool:



            '''simple docstring'''

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





       def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					bool = False      )					->				str:



            '''simple docstring'''

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



class 						lowercase    (     __lowercase     ):




       @classmethod
       @abstractmethod
       def 	__snake_case(						cls   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Path, str]	,							**_UpperCamelCase   :					Union[str, Any]      )					->				bool:



            '''simple docstring'''

            ...





       @staticmethod
       @abstractmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

            ...



class 						lowercase    (     __lowercase				,       __lowercase     ):
       lowercase__						:       List[bytes]								=							[]




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					int      )					->				List[Any]:



            '''simple docstring'''

            with open(__a	,							"rb"      ) as f:
                 return f.read(__a      )





       @classmethod
       def 	__snake_case(						cls   :					Dict	,							_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					bytes = b""      )					->				bool:



            '''simple docstring'''

            if not magic_number:
                 SCREAMING_SNAKE_CASE    			=       max(len(__a      ) for cls_magic_number in cls.magic_numbers      )
                 try:
                      SCREAMING_SNAKE_CASE    			=       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 						lowercase    (     __lowercase     ):




       @classmethod
       def 	__snake_case(						cls   :					Any	,							_UpperCamelCase   :					Union[Path, str]	,							**_UpperCamelCase   :					Dict      )					->				bool:



            '''simple docstring'''

            return tarfile.is_tarfile(__a      )




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Any      )					->				int:



            '''simple docstring'''

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

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

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

            SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

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



class 						lowercase    (     __lowercase     ):
       lowercase__						:       Tuple								=							[B'''\x1F\x8B''']




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

            with gzip.open(__a	,							"rb"      ) as gzip_file:
                 with open(__a	,							"wb"      ) as extracted_file:
                      shutil.copyfileobj(__a	,							__a      )



class 						lowercase    (     __lowercase     ):
       lowercase__						:       Any								=							[
           B'''PK\x03\x04''',
           B'''PK\x05\x06''',  # empty archive
           B'''PK\x07\x08''',  # spanned archive
       ]




       @classmethod
       def 	__snake_case(						cls   :					Any	,							_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					bytes = b""      )					->				bool:



            '''simple docstring'''

            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:
                      SCREAMING_SNAKE_CASE    			=       _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:
                                     SCREAMING_SNAKE_CASE    			=       fp.read(__a      )  # CD is where we expect it to be
                                     if len(__a      ) == sizeCentralDir:
                                          SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

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



class 						lowercase    (     __lowercase     ):
       lowercase__						:       Dict								=							[B'''\xFD\x37\x7A\x58\x5A\x00''']




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

            with lzma.open(__a      ) as compressed_file:
                 with open(__a	,							"wb"      ) as extracted_file:
                      shutil.copyfileobj(__a	,							__a      )



class 						lowercase    (     __lowercase     ):
       lowercase__						:       int								=							[B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00''']  # RAR_ID  # RAR5_ID




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

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

            os.makedirs(__a	,							exist_ok=__a      )
            SCREAMING_SNAKE_CASE    			=       rarfile.RarFile(__a      )
            rf.extractall(__a      )
            rf.close()



class 						lowercase    (     __lowercase     ):
       lowercase__						:       Union[str, Any]								=							[B'''\x28\xb5\x2F\xFD''']




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

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

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



class 						lowercase    (     __lowercase     ):
       lowercase__						:       Optional[int]								=							[B'''\x42\x5A\x68''']




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

            with bza.open(__a	,							"rb"      ) as compressed_file:
                 with open(__a	,							"wb"      ) as extracted_file:
                      shutil.copyfileobj(__a	,							__a      )



class 						lowercase    (     __lowercase     ):
       lowercase__						:       str								=							[B'''\x37\x7A\xBC\xAF\x27\x1C''']




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

            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 						lowercase    (     __lowercase     ):
       lowercase__						:       Optional[Any]								=							[B'''\x04\x22\x4D\x18''']




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]      )					->				None:



            '''simple docstring'''

            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 						lowercase    :
       lowercase__						:       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 	__snake_case(						cls   :					Optional[int]      )					->				List[str]:



            '''simple docstring'''

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




       @staticmethod
       def 	__snake_case(						_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					int      )					->				List[Any]:



            '''simple docstring'''

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




       @classmethod
       def 	__snake_case(						cls   :					Optional[int]	,							_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					bool = 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=__a	,							)
            SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						cls   :					Tuple	,							_UpperCamelCase   :					Union[Path, str]      )					->				str:  # <Added version="2.4.0"/>



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       cls._get_magic_number_max_length()
            SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						cls   :					Any	,							_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Union[Path, str]	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[BaseExtractor] = "deprecated"	,							)					->				None:



            '''simple docstring'''

            os.makedirs(os.path.dirname(__a      )	,							exist_ok=__a      )
            # Prevent parallel extractions
            SCREAMING_SNAKE_CASE    			=       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	,							)
                           SCREAMING_SNAKE_CASE    			=       extractor if extractor != """deprecated""" else extractor_format
                      else:
                           SCREAMING_SNAKE_CASE    			=       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      )





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict=False  ):
					SCREAMING_SNAKE_CASE    			=       []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")  )
										rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")  )
										rename_keys.append(
										    (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")  )
										rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")  )
										rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")  )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ("module.cls_token", "vit.embeddings.cls_token"),
					        ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
					        ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
					        ("module.pos_embed", "vit.embeddings.position_embeddings"),
					    ]  )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ("module.norm.weight", "layernorm.weight"),
										        ("module.norm.bias", "layernorm.bias"),
										    ]  )

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										SCREAMING_SNAKE_CASE    			=       [(pair[0], pair[1][4:]) if pair[1].startswith("vit"  ) else pair for pair in rename_keys]
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("norm.weight", "vit.layernorm.weight"),
										        ("norm.bias", "vit.layernorm.bias"),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ]  )

					return rename_keys





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							str=False  ):
					for i in range(config.num_hidden_layers  ):
										if base_model:
															SCREAMING_SNAKE_CASE    			=       ""
										else:
															SCREAMING_SNAKE_CASE    			=       "vit."
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"  )
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    : config.hidden_size, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[: config.hidden_size]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    -config.hidden_size :, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[-config.hidden_size :]





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):
					# projection head is used in the self-supervised pre-training in MSN,
					# for downstream task it's not needed.
					SCREAMING_SNAKE_CASE    			=       [
					    "module.fc.fc1.weight",
					    "module.fc.fc1.bias",
					    "module.fc.bn1.weight",
					    "module.fc.bn1.bias",
					    "module.fc.bn1.running_mean",
					    "module.fc.bn1.running_var",
					    "module.fc.bn1.num_batches_tracked",
					    "module.fc.fc2.weight",
					    "module.fc.fc2.bias",
					    "module.fc.bn2.weight",
					    "module.fc.bn2.bias",
					    "module.fc.bn2.running_mean",
					    "module.fc.bn2.running_var",
					    "module.fc.bn2.num_batches_tracked",
					    "module.fc.fc3.weight",
					    "module.fc.fc3.bias",
					]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Dict  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       ViTMSNConfig()
					SCREAMING_SNAKE_CASE    			=       1_0_0_0

					SCREAMING_SNAKE_CASE    			=       "datasets/huggingface/label-files"
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(UpperCAmelCase__      , UpperCAmelCase__  )      , "r"  )  )
					SCREAMING_SNAKE_CASE    			=       {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE    			=       idalabel
					SCREAMING_SNAKE_CASE    			=       {v: k for k, v in idalabel.items()}

					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       3_8_4
										SCREAMING_SNAKE_CASE    			=       1_5_3_6
										SCREAMING_SNAKE_CASE    			=       6
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       4
					elif "l7" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       7
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1

					SCREAMING_SNAKE_CASE    			=       ViTMSNModel(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["target_encoder"]

					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(size=config.image_size  )

					remove_projection_head(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , base_model=UpperCAmelCase__  )

					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
					read_in_q_k_v(UpperCAmelCase__      , UpperCAmelCase__      , base_model=UpperCAmelCase__  )

					model.load_state_dict(UpperCAmelCase__  )
					model.eval()

					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"

					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(UpperCAmelCase__      , stream=UpperCAmelCase__  ).raw  )
					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(
					    size=config.image_size      , image_mean=UpperCAmelCase__      , image_std=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       image_processor(images=UpperCAmelCase__      , return_tensors="pt"  )

					# forward pass
					torch.manual_seed(2  )
					SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       outputs.last_hidden_state

					# The following Colab Notebook was used to generate these outputs:
					# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-1.0915, -1.4876, -1.1809]]  )
					elif "b16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[14.2889, -18.9045, 11.7281]]  )
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[41.5028, -22.8681, 45.6475]]  )
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-4.3868, 5.2932, -0.4137]]  )
					else:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-0.1792, -0.6465, 2.4263]]  )

					# verify logits
					assert torch.allclose(last_hidden_state[:, 0, :3]      , UpperCAmelCase__      , atol=1e-4  )

					print(F"Saving model to {pytorch_dump_folder_path}"  )
					model.save_pretrained(UpperCAmelCase__  )

					print(F"Saving image processor to {pytorch_dump_folder_path}"  )
					image_processor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
							_lowerCamelCase					:   Optional[Any]							=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
							    type=str,
							    help='''URL of the checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							_lowerCamelCase					:   Optional[Any]							=		parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




