


"""simple docstring"""







import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
				import torch

				__a			 =   True
except ImportError:
				__a			 =   False


try:
				from torch.hub import _get_torch_home

				__a			 =   _get_torch_home()
except ImportError:
				__a			 =   os.path.expanduser(
				    os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
				)

__a			 =   os.path.join(torch_cache_home, "transformers")

__a			 =   "https://cdn.huggingface.co"
__a			 =   "https://s3.amazonaws.com/models.huggingface.co/bert"
__a			 =   "/".join(str(Path(__file__).resolve()).split("/")[:-1])
__a			 =   os.path.join(PATH, "config.yaml")
__a			 =   os.path.join(PATH, "attributes.txt")
__a			 =   os.path.join(PATH, "objects.txt")
__a			 =   os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
__a			 =   os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
__a			 =   os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
__a			 =   "pytorch_model.bin"
__a			 =   "config.yaml"






def 					a__ (       _UpperCamelCase=OBJECTS	,			_UpperCamelCase=ATTRIBUTES ):





						"""simple docstring"""

						UpperCAmelCase__		=    []
						with open(_UpperCamelCase ) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(''',''' )[0].lower().strip() )

						UpperCAmelCase__		=    []
						with open(_UpperCamelCase ) as f:
												for object in f.readlines():
																		vg_attrs.append(object.split(''',''' )[0].lower().strip() )
						return vg_classes, vg_attrs






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    OrderedDict()
						with open(_UpperCamelCase	,			'''rb''' ) as f:
												UpperCAmelCase__		=    pkl.load(_UpperCamelCase )['''model''']
						for k in copy.deepcopy(list(ckp.keys() ) ):
												UpperCAmelCase__		=    ckp.pop(_UpperCamelCase )
												if isinstance(_UpperCamelCase	,			np.ndarray ):
																		UpperCAmelCase__		=    torch.tensor(_UpperCamelCase )
												else:
																		assert isinstance(_UpperCamelCase	,			torch.tensor ), type(_UpperCamelCase )
												UpperCAmelCase__		=    v
						return r
class 							lowerCamelCase       :


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      {}






							def __init__(						self :     Any		, lowerCamelCase_ :     dict		, lowerCamelCase_ :     str = "root"		, lowerCamelCase_ :     List[str]=0				):
													UpperCAmelCase__		=    name
													UpperCAmelCase__		=    level
													UpperCAmelCase__		=    {}
													for k, v in dictionary.items():
																			if v is None:
																									raise ValueError()
																			UpperCAmelCase__		=    copy.deepcopy(lowerCamelCase_				)
																			UpperCAmelCase__		=    copy.deepcopy(lowerCamelCase_				)
																			if isinstance(lowerCamelCase_		, lowerCamelCase_				):
																									UpperCAmelCase__		=    Config(lowerCamelCase_		, name=lowerCamelCase_		, level=level + 1				)
																			UpperCAmelCase__		=    v
																			setattr(self		, lowerCamelCase_		, lowerCamelCase_				)

													UpperCAmelCase__		=    d






							def __repr__(						self :     Union[str, Any]				):
													return str(list((self._pointer.keys())				)				)






							def __setattr__(						self :     List[str]		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     Union[str, Any]				):
													UpperCAmelCase__		=    val
													UpperCAmelCase__		=    val
													UpperCAmelCase__		=    key.split('''.'''				)
													UpperCAmelCase__		=    len(lowerCamelCase_				) - 1
													UpperCAmelCase__		=    self._pointer
													if len(lowerCamelCase_				) > 1:
																			for i, l in enumerate(lowerCamelCase_				):
																									if hasattr(self		, lowerCamelCase_				) and isinstance(getattr(self		, lowerCamelCase_				)		, lowerCamelCase_				):
																															setattr(getattr(self		, lowerCamelCase_				)		, '''.'''.join(levels[i:]				)		, lowerCamelCase_				)
																									if l == last_level:
																															UpperCAmelCase__		=    val
																									else:
																															UpperCAmelCase__		=    pointer[l]






							def 					lowerCAmelCase     (						self :     Tuple				):
													return self._pointer






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     Union[str, Any]		, lowerCamelCase_ :     List[Any]				):
													with open(f"""{file_name}"""		, '''w'''				) as stream:
																			dump(lowerCamelCase_		, lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[int]		, lowerCamelCase_ :     Union[str, Any]		, lowerCamelCase_ :     Optional[int]				):
													with open(f"""{file_name}"""		, '''w'''				) as stream:
																			json.dump(lowerCamelCase_		, lowerCamelCase_				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     Optional[Any]				):
													with open(lowerCamelCase_				) as stream:
																			UpperCAmelCase__		=    load(lowerCamelCase_		, Loader=lowerCamelCase_				)
													return data






							def __str__(						self :     Tuple				):
													UpperCAmelCase__		=    '''    '''
													if self._name != "root":
																			UpperCAmelCase__		=    f"""{t * (self._level-1)}{self._name}:\n"""
													else:
																			UpperCAmelCase__		=    ''''''
													UpperCAmelCase__		=    self._level
													for i, (k, v) in enumerate(self._pointer.items()				):
																			if isinstance(lowerCamelCase_		, lowerCamelCase_				):
																									r += f"""{t * (self._level)}{v}\n"""
																									self._level += 1
																			else:
																									r += f"""{t * (self._level)}{k}: {v} ({type(lowerCamelCase_				).__name__})\n"""
																			UpperCAmelCase__		=    level
													return r[:-1]






							@classmethod
							def 					lowerCAmelCase     (						cls :     List[str]		, lowerCamelCase_ :     str		, **lowerCamelCase_ :     str				):
													UpperCAmelCase__	,				UpperCAmelCase__		=    cls.get_config_dict(lowerCamelCase_		, **lowerCamelCase_				)
													return cls(lowerCamelCase_				)






							@classmethod
							def 					lowerCAmelCase     (						cls :     Tuple		, lowerCamelCase_ :     str		, **lowerCamelCase_ :     Dict				):
													UpperCAmelCase__		=    kwargs.pop('''cache_dir'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''force_download'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''resume_download'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''proxies'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''local_files_only'''		, lowerCamelCase_				)

													if os.path.isdir(lowerCamelCase_				):
																			UpperCAmelCase__		=    os.path.join(lowerCamelCase_		, lowerCamelCase_				)
													elif os.path.isfile(lowerCamelCase_				) or is_remote_url(lowerCamelCase_				):
																			UpperCAmelCase__		=    pretrained_model_name_or_path
													else:
																			UpperCAmelCase__		=    hf_bucket_url(lowerCamelCase_		, filename=lowerCamelCase_		, use_cdn=lowerCamelCase_				)

													try:
																			# Load from URL or cache if already cached
																			UpperCAmelCase__		=    cached_path(
																			    lowerCamelCase_		, cache_dir=lowerCamelCase_		, force_download=lowerCamelCase_		, proxies=lowerCamelCase_		, resume_download=lowerCamelCase_		, local_files_only=lowerCamelCase_		, )
																			# Load config dict
																			if resolved_config_file is None:
																									raise EnvironmentError

																			UpperCAmelCase__		=    Config.load_yaml(lowerCamelCase_				)

													except EnvironmentError:
																			UpperCAmelCase__		=    '''Can\'t load config for'''
																			raise EnvironmentError(lowerCamelCase_				)

													if resolved_config_file == config_file:
																			print('''loading configuration file from path'''				)
													else:
																			print('''loading configuration file cache'''				)

													return Config.load_yaml(lowerCamelCase_				), kwargs






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    torch.load('''dump.pt'''	,			map_location=in_tensor.device )
						UpperCAmelCase__		=    in_tensor.numpy()
						UpperCAmelCase__		=    out_tensor.numpy()[0]
						print(na.shape	,			na[0, 0, :5] )
						print(na.shape	,			na[0, 0, :5] )
						assert np.allclose(_UpperCamelCase	,			_UpperCamelCase	,			rtol=0.01	,			atol=0.1 ), (
						    F"""{sum([1 for x in np.isclose(_UpperCamelCase	,			_UpperCamelCase	,			rtol=0.01	,			atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %"""
						    " element-wise mismatch"
						)
						raise Exception('''tensors are all good''' )

						# Hugging face functions below






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    urlparse(_UpperCamelCase )
						return parsed.scheme in ("http", "https")






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=True ):





						"""simple docstring"""

						UpperCAmelCase__		=    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						UpperCAmelCase__		=    '''/''' not in model_id
						if legacy_format:
												return F"""{endpoint}/{model_id}-{filename}"""
						else:
												return F"""{endpoint}/{model_id}/{filename}"""






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=0	,			_UpperCamelCase=None	,			):





						"""simple docstring"""

						UpperCAmelCase__		=    '''python/{}'''.format(sys.version.split()[0] )
						if _torch_available:
												ua += "; torch/{}".format(torch.__version__ )
						if isinstance(_UpperCamelCase	,			_UpperCamelCase ):
												ua += "; " + "; ".join('''{}/{}'''.format(_UpperCamelCase	,			_UpperCamelCase ) for k, v in user_agent.items() )
						elif isinstance(_UpperCamelCase	,			_UpperCamelCase ):
												ua += "; " + user_agent
						UpperCAmelCase__		=    {'''user-agent''': ua}
						if resume_size > 0:
												UpperCAmelCase__		=    '''bytes=%d-''' % (resume_size,)
						UpperCAmelCase__		=    requests.get(_UpperCamelCase	,			stream=_UpperCamelCase	,			proxies=_UpperCamelCase	,			headers=_UpperCamelCase )
						if response.status_code == 416:  # Range not satisfiable
												return
						UpperCAmelCase__		=    response.headers.get('''Content-Length''' )
						UpperCAmelCase__		=    resume_size + int(_UpperCamelCase ) if content_length is not None else None
						UpperCAmelCase__		=    tqdm(
						    unit='''B'''	,			unit_scale=_UpperCamelCase	,			total=_UpperCamelCase	,			initial=_UpperCamelCase	,			desc='''Downloading'''	,			)
						for chunk in response.iter_content(chunk_size=1024 ):
												if chunk:  # filter out keep-alive new chunks
																		progress.update(len(_UpperCamelCase ) )
																		temp_file.write(_UpperCamelCase )
						progress.close()






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=10	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			):





						"""simple docstring"""

						if cache_dir is None:
												UpperCAmelCase__		=    TRANSFORMERS_CACHE
						if isinstance(_UpperCamelCase	,			_UpperCamelCase ):
												UpperCAmelCase__		=    str(_UpperCamelCase )

						os.makedirs(_UpperCamelCase	,			exist_ok=_UpperCamelCase )

						UpperCAmelCase__		=    None
						if not local_files_only:
												try:
																		UpperCAmelCase__		=    requests.head(_UpperCamelCase	,			allow_redirects=_UpperCamelCase	,			proxies=_UpperCamelCase	,			timeout=_UpperCamelCase )
																		if response.status_code == 200:
																								UpperCAmelCase__		=    response.headers.get('''ETag''' )
												except (EnvironmentError, requests.exceptions.Timeout):
																		# etag is already None
																		pass

						UpperCAmelCase__		=    url_to_filename(_UpperCamelCase	,			_UpperCamelCase )

						# get cache path to put the file
						UpperCAmelCase__		=    os.path.join(_UpperCamelCase	,			_UpperCamelCase )

						# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
						# try to get the last downloaded one
						if etag is None:
												if os.path.exists(_UpperCamelCase ):
																		return cache_path
												else:
																		UpperCAmelCase__		=    [
																		    file
																		    for file in fnmatch.filter(os.listdir(_UpperCamelCase )	,			filename + '''.*''' )
																		    if not file.endswith('''.json''' ) and not file.endswith('''.lock''' )
																		]
																		if len(_UpperCamelCase ) > 0:
																								return os.path.join(_UpperCamelCase	,			matching_files[-1] )
																		else:
																								# If files cannot be found and local_files_only=True,
																								# the models might've been found if local_files_only=False
																								# Notify the user about that
																								if local_files_only:
																														raise ValueError(
																														    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
																														    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
																														    ''' to False.''' )
																								return None

    # From now on, etag is not None.
						if os.path.exists(_UpperCamelCase ) and not force_download:
												return cache_path

						# Prevent parallel downloads of the same file with a lock.
						UpperCAmelCase__		=    cache_path + '''.lock'''
						with FileLock(_UpperCamelCase ):
												# If the download just completed while the lock was activated.
												if os.path.exists(_UpperCamelCase ) and not force_download:
																		# Even if returning early like here, the lock will be released.
																		return cache_path

												if resume_download:
																		UpperCAmelCase__		=    cache_path + '''.incomplete'''

																		@contextmanager
																		def _resumable_file_manager():
																								with open(_UpperCamelCase	,			'''a+b''' ) as f:
																														yield f

																		UpperCAmelCase__		=    _resumable_file_manager
																		if os.path.exists(_UpperCamelCase ):
																								UpperCAmelCase__		=    os.stat(_UpperCamelCase ).st_size
																		else:
																								UpperCAmelCase__		=    0
												else:
																		UpperCAmelCase__		=    partial(tempfile.NamedTemporaryFile	,			dir=_UpperCamelCase	,			delete=_UpperCamelCase )
																		UpperCAmelCase__		=    0

												# Download to temporary file, then copy to cache dir once finished.
												# Otherwise you get corrupt cache entries if the download gets interrupted.
												with temp_file_manager() as temp_file:
																		print(
																		    '''%s not found in cache or force_download set to True, downloading to %s'''	,			_UpperCamelCase	,			temp_file.name	,			)

																		http_get(
																		    _UpperCamelCase	,			_UpperCamelCase	,			proxies=_UpperCamelCase	,			resume_size=_UpperCamelCase	,			user_agent=_UpperCamelCase	,			)

												os.replace(temp_file.name	,			_UpperCamelCase )

												UpperCAmelCase__		=    {'''url''': url, '''etag''': etag}
												UpperCAmelCase__		=    cache_path + '''.json'''
												with open(_UpperCamelCase	,			'''w''' ) as meta_file:
																		json.dump(_UpperCamelCase	,			_UpperCamelCase )

						return cache_path






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None ):





						"""simple docstring"""

						UpperCAmelCase__		=    url.encode('''utf-8''' )
						UpperCAmelCase__		=    shaaaa(_UpperCamelCase )
						UpperCAmelCase__		=    url_hash.hexdigest()

						if etag:
												UpperCAmelCase__		=    etag.encode('''utf-8''' )
												UpperCAmelCase__		=    shaaaa(_UpperCamelCase )
												filename += "." + etag_hash.hexdigest()

						if url.endswith('''.h5''' ):
												filename += ".h5"

						return filename






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=False	,			_UpperCamelCase=False	,			):





						"""simple docstring"""

						if cache_dir is None:
												UpperCAmelCase__		=    TRANSFORMERS_CACHE
						if isinstance(_UpperCamelCase	,			_UpperCamelCase ):
												UpperCAmelCase__		=    str(_UpperCamelCase )
						if isinstance(_UpperCamelCase	,			_UpperCamelCase ):
												UpperCAmelCase__		=    str(_UpperCamelCase )

						if is_remote_url(_UpperCamelCase ):
												# URL, so get it from the cache (downloading if necessary)
												UpperCAmelCase__		=    get_from_cache(
												    _UpperCamelCase	,			cache_dir=_UpperCamelCase	,			force_download=_UpperCamelCase	,			proxies=_UpperCamelCase	,			resume_download=_UpperCamelCase	,			user_agent=_UpperCamelCase	,			local_files_only=_UpperCamelCase	,			)
						elif os.path.exists(_UpperCamelCase ):
												# File, and it exists.
												UpperCAmelCase__		=    url_or_filename
						elif urlparse(_UpperCamelCase ).scheme == "":
												# File, but it doesn't exist.
												raise EnvironmentError('''file {} not found'''.format(_UpperCamelCase ) )
						else:
												# Something unknown
												raise ValueError('''unable to parse {} as a URL or as a local path'''.format(_UpperCamelCase ) )

						if extract_compressed_file:
												if not is_zipfile(_UpperCamelCase ) and not tarfile.is_tarfile(_UpperCamelCase ):
																		return output_path

												# Path where we extract compressed archives
												# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
												UpperCAmelCase__	,				UpperCAmelCase__		=    os.path.split(_UpperCamelCase )
												UpperCAmelCase__		=    output_file.replace('''.'''	,			'''-''' ) + '''-extracted'''
												UpperCAmelCase__		=    os.path.join(_UpperCamelCase	,			_UpperCamelCase )

												if os.path.isdir(_UpperCamelCase ) and os.listdir(_UpperCamelCase ) and not force_extract:
																		return output_path_extracted

												# Prevent parallel extractions
												UpperCAmelCase__		=    output_path + '''.lock'''
												with FileLock(_UpperCamelCase ):
																		shutil.rmtree(_UpperCamelCase	,			ignore_errors=_UpperCamelCase )
																		os.makedirs(_UpperCamelCase )
																		if is_zipfile(_UpperCamelCase ):
																								with ZipFile(_UpperCamelCase	,			'''r''' ) as zip_file:
																														zip_file.extractall(_UpperCamelCase )
																														zip_file.close()
																		elif tarfile.is_tarfile(_UpperCamelCase ):
																								UpperCAmelCase__		=    tarfile.open(_UpperCamelCase )
																								tar_file.extractall(_UpperCamelCase )
																								tar_file.close()
																		else:
																								raise EnvironmentError('''Archive format of {} could not be identified'''.format(_UpperCamelCase ) )

												return output_path_extracted

						return output_path






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase="," ):





						"""simple docstring"""

						assert isinstance(_UpperCamelCase	,			_UpperCamelCase )
						if os.path.isfile(_UpperCamelCase ):
												with open(_UpperCamelCase ) as f:
																		UpperCAmelCase__		=    eval(f.read() )
						else:
												UpperCAmelCase__		=    requests.get(_UpperCamelCase )
												try:
																		UpperCAmelCase__		=    requests.json()
												except Exception:
																		UpperCAmelCase__		=    req.content.decode()
																		assert data is not None, "could not connect"
																		try:
																								UpperCAmelCase__		=    eval(_UpperCamelCase )
																		except Exception:
																								UpperCAmelCase__		=    data.split('''\n''' )
												req.close()
						return data






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    requests.get(_UpperCamelCase )
						UpperCAmelCase__		=    np.array(Image.open(BytesIO(response.content ) ) )
						return img






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    url.split('''/''' )[-1]
						if fn not in os.listdir(os.getcwd() ):
												wget.download(_UpperCamelCase )
						with open(_UpperCamelCase	,			'''rb''' ) as stream:
												UpperCAmelCase__		=    pkl.load(_UpperCamelCase )
						UpperCAmelCase__		=    weights.pop('''model''' )
						UpperCAmelCase__		=    {}
						for k, v in model.items():
												UpperCAmelCase__		=    torch.from_numpy(_UpperCamelCase )
												if "running_var" in k:
																		UpperCAmelCase__		=    torch.tensor([0] )
																		UpperCAmelCase__		=    k.replace('''running_var'''	,			'''num_batches_tracked''' )
																		UpperCAmelCase__		=    zero
						return new






def 					a__ (       ):





						"""simple docstring"""

						print(F"""{os.path.abspath(os.path.join(_UpperCamelCase	,			os.pardir ) )}/demo.ipynb""" )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase="RGB" ):





						"""simple docstring"""

						assert isinstance(_UpperCamelCase	,			_UpperCamelCase )
						if os.path.isfile(_UpperCamelCase ):
												UpperCAmelCase__		=    cva.imread(_UpperCamelCase )
						else:
												UpperCAmelCase__		=    get_image_from_url(_UpperCamelCase )
												assert img is not None, F"""could not connect to: {im}"""
						UpperCAmelCase__		=    cva.cvtColor(_UpperCamelCase	,			cva.COLOR_BGR2RGB )
						if input_format == "RGB":
												UpperCAmelCase__		=    img[:, :, ::-1]
						return img






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=1 ):





						"""simple docstring"""

						return (images[i : i + batch] for i in range(0	,			len(_UpperCamelCase )	,			_UpperCamelCase ))



"""simple docstring"""







import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

						with open(_UpperCamelCase	,			'''wb''' ) as f:
												with requests.get(_UpperCamelCase	,			stream=_UpperCamelCase ) as req:
																		for chunk in req.iter_content(chunk_size=2048 ):
																								f.write(_UpperCamelCase )






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitConfig()

						UpperCAmelCase__		=    400
						UpperCAmelCase__		=    '''huggingface/label-files'''
						UpperCAmelCase__		=    '''kinetics400-id2label.json'''

						UpperCAmelCase__		=    json.load(open(hf_hub_download(_UpperCamelCase	,			_UpperCamelCase	,			repo_type='''dataset''' )	,			'''r''' ) )
						UpperCAmelCase__		=    {int(_UpperCamelCase ): v for k, v in idalabel.items()}
						UpperCAmelCase__		=    idalabel
						UpperCAmelCase__		=    {v: k for k, v in idalabel.items()}
						return config






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''	,			filename='''eating_spaghetti_32_frames.npy'''	,			repo_type='''dataset''' )
						UpperCAmelCase__		=    np.load(_UpperCamelCase )
						return list(_UpperCamelCase )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if np.ndim(_UpperCamelCase ) == 2:
												return transform_attention_bias(_UpperCamelCase )

						elif np.ndim(_UpperCamelCase ) == 3:
												return transform_attention_kernel(_UpperCamelCase )

						else:
												raise Exception(F"""Invalid number of dimesions: {np.ndim(_UpperCamelCase )}""" )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return current.flatten()






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return np.reshape(_UpperCamelCase	,			(current.shape[0], current.shape[1] * current.shape[2]) ).T






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return np.reshape(_UpperCamelCase	,			(current.shape[0] * current.shape[1], current.shape[2]) ).T






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer'''][F"""encoderblock_{i}"""]

						UpperCAmelCase__		=    F"""encoder.layer.{i}."""
						UpperCAmelCase__		=    {
						    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
						    prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel'''] ),
						    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
						    prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel'''] ),
						    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
						    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
						    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
						    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
						    prefix
						    + '''attention.attention.query.bias''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''query''']['''bias'''] ),
						    prefix
						    + '''attention.attention.query.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel'''] ),
						    prefix
						    + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias'''] ),
						    prefix
						    + '''attention.attention.key.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel'''] ),
						    prefix
						    + '''attention.attention.value.bias''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''value''']['''bias'''] ),
						    prefix
						    + '''attention.attention.value.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel'''] ),
						    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
						    prefix
						    + '''attention.output.dense.weight''': transform_attention_output_weight(
						        state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel'''] ),
						}

						return new_state






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()] )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=False ):





						"""simple docstring"""

						UpperCAmelCase__		=    get_n_layers(_UpperCamelCase )

						UpperCAmelCase__		=    OrderedDict()

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

						UpperCAmelCase__		=    np.transpose(
						    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']	,			(4, 3, 0, 1, 2) )
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''cls''']
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
						    '''pos_embedding'''
						]

						for i in range(_UpperCamelCase ):
												new_state.update(transform_state_encoder_block(_UpperCamelCase	,			_UpperCamelCase ) )

						if classification_head:
												UpperCAmelCase__		=    {'''vivit.''' + k: v for k, v in new_state.items()}
												UpperCAmelCase__		=    np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel'''] )
												UpperCAmelCase__		=    np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias'''] )

						return {k: torch.tensor(_UpperCamelCase ) for k, v in new_state.items()}






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitImageProcessor()

						assert extractor.do_resize is True
						assert extractor.size == {"shortest_edge": 256}
						assert extractor.do_center_crop is True
						assert extractor.crop_size == {"width": 224, "height": 224}
						assert extractor.resample == PILImageResampling.BILINEAR

						# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
						# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
						# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
						assert extractor.do_normalize is False
						assert extractor.do_rescale is True
						assert extractor.rescale_factor == 1 / 255

						# zero-centering = True in original implementation
						assert extractor.do_zero_centering is True

						return extractor






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''checkpoint'''

						if not os.path.exists(_UpperCamelCase ):
												download_checkpoint(_UpperCamelCase )

						UpperCAmelCase__		=    restore_checkpoint(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    transform_state(_UpperCamelCase	,			classification_head=_UpperCamelCase )

						UpperCAmelCase__		=    get_vivit_config()

						assert config.image_size == 224
						assert config.num_frames == 32

						UpperCAmelCase__		=    VivitForVideoClassification(_UpperCamelCase )
						model.load_state_dict(_UpperCamelCase )
						model.eval()

						UpperCAmelCase__		=    get_processor()

						UpperCAmelCase__		=    prepare_video()
						UpperCAmelCase__		=    extractor(_UpperCamelCase	,			return_tensors='''pt''' )

						UpperCAmelCase__		=    model(**_UpperCamelCase )

						UpperCAmelCase__		=    torch.Size([1, 400] )
						UpperCAmelCase__		=    torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658] )

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :5]	,			_UpperCamelCase	,			atol=1E-4 ), outputs.logits[0, :5]

						model.save_pretrained(_UpperCamelCase )
						extractor.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
						__a			 =   argparse.ArgumentParser()

						parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

						__a			 =   parser.parse_args()
						convert(args.output_model_name)