



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							:					Optional[Any]=OBJECTS ,   _UpperCAmelCase							:					List[Any]=ATTRIBUTES						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Optional[int]													=	[]
		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							:					List[str]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	OrderedDict()
		with open(_UpperCAmelCase ,   '''rb'''						) as f:
				__UpperCAmelCase    :     Optional[int]													=	pkl.load(_UpperCAmelCase						)['''model''']
		for k in copy.deepcopy(list(ckp.keys()						)						):
				__UpperCAmelCase    :     List[Any]													=	ckp.pop(_UpperCAmelCase						)
				if isinstance(_UpperCAmelCase ,   np.ndarray						):
						__UpperCAmelCase    :     Union[str, Any]													=	torch.tensor(_UpperCAmelCase						)
				else:
						assert isinstance(_UpperCAmelCase ,   torch.tensor						), type(_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	v
		return r

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       {}




			def __init__(   self							: int       ,       a_							: dict       ,       a_							: str = "root"       ,       a_							: Tuple=0 ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	name
					__UpperCAmelCase    :     List[str]													=	level
					__UpperCAmelCase    :     Optional[int]													=	{}
					for k, v in dictionary.items():
							if v is None:
									raise ValueError()
							__UpperCAmelCase    :     Any													=	copy.deepcopy(a_ )
							__UpperCAmelCase    :     Tuple													=	copy.deepcopy(a_ )
							if isinstance(a_       ,       a_ ):
									__UpperCAmelCase    :     Dict													=	Config(a_       ,       name=a_       ,       level=level + 1 )
							__UpperCAmelCase    :     int													=	v
							setattr(self       ,       a_       ,       a_ )

					__UpperCAmelCase    :     Any													=	d




			def __repr__(   self							: Union[str, Any] ):
					'''simple docstring'''





					return str(list((self._pointer.keys()) ) )




			def __setattr__(   self							: Tuple       ,       a_							: Union[str, Any]       ,       a_							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	val
					__UpperCAmelCase    :     Tuple													=	val
					__UpperCAmelCase    :     List[Any]													=	key.split('''.''' )
					__UpperCAmelCase    :     int													=	len(a_ ) - 1
					__UpperCAmelCase    :     List[str]													=	self._pointer
					if len(a_ ) > 1:
							for i, l in enumerate(a_ ):
									if hasattr(self       ,       a_ ) and isinstance(getattr(self       ,       a_ )       ,       a_ ):
											setattr(getattr(self       ,       a_ )       ,       '''.'''.join(levels[i:] )       ,       a_ )
									if l == last_level:
											__UpperCAmelCase    :     Dict													=	val
									else:
											__UpperCAmelCase    :     Optional[int]													=	pointer[l]




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					return self._pointer




			def  snake_case__     (   self							: str       ,       a_							: Union[str, Any]       ,       a_							: List[str] ):
					'''simple docstring'''





					with open(F'{file_name}'       ,       '''w''' ) as stream:
							dump(a_       ,       a_ )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: str       ,       a_							: List[Any] ):
					'''simple docstring'''





					with open(F'{file_name}'       ,       '''w''' ) as stream:
							json.dump(a_       ,       a_ )




			@staticmethod
			def  snake_case__     (   a_							: str ):
					'''simple docstring'''





					with open(a_ ) as stream:
							__UpperCAmelCase    :     Dict													=	load(a_       ,       Loader=a_ )
					return data




			def __str__(   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	'''    '''
					if self._name != "root":
							__UpperCAmelCase    :     Optional[Any]													=	F'{t * (self._level-1)}{self._name}:\n'
					else:
							__UpperCAmelCase    :     Union[str, Any]													=	''''''
					__UpperCAmelCase    :     str													=	self._level
					for i, (k, v) in enumerate(self._pointer.items() ):
							if isinstance(a_       ,       a_ ):
									r += F'{t * (self._level)}{v}\n'
									self._level += 1
							else:
									r += F'{t * (self._level)}{k}: {v} ({type(a_ ).__name__})\n'
							__UpperCAmelCase    :     Dict													=	level
					return r[:-1]




			@classmethod
			def  snake_case__     (   cls							: str       ,       a_							: str       ,       **a_							: Any ):
					'''simple docstring'''





					__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	cls.get_config_dict(a_       ,       **a_ )
					return cls(a_ )




			@classmethod
			def  snake_case__     (   cls							: Optional[Any]       ,       a_							: str       ,       **a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	kwargs.pop('''cache_dir'''       ,       a_ )
					__UpperCAmelCase    :     str													=	kwargs.pop('''force_download'''       ,       a_ )
					__UpperCAmelCase    :     Tuple													=	kwargs.pop('''resume_download'''       ,       a_ )
					__UpperCAmelCase    :     Any													=	kwargs.pop('''proxies'''       ,       a_ )
					__UpperCAmelCase    :     Tuple													=	kwargs.pop('''local_files_only'''       ,       a_ )

					if os.path.isdir(a_ ):
							__UpperCAmelCase    :     int													=	os.path.join(a_       ,       a_ )
					elif os.path.isfile(a_ ) or is_remote_url(a_ ):
							__UpperCAmelCase    :     Optional[int]													=	pretrained_model_name_or_path
					else:
							__UpperCAmelCase    :     Dict													=	hf_bucket_url(a_       ,       filename=a_       ,       use_cdn=a_ )

					try:
							# Load from URL or cache if already cached
							__UpperCAmelCase    :     Union[str, Any]													=	cached_path(
							    a_       ,       cache_dir=a_       ,       force_download=a_       ,       proxies=a_       ,       resume_download=a_       ,       local_files_only=a_       ,       )
							# Load config dict
							if resolved_config_file is None:
									raise EnvironmentError

							__UpperCAmelCase    :     Tuple													=	Config.load_yaml(a_ )

					except EnvironmentError:
							__UpperCAmelCase    :     Union[str, Any]													=	'''Can\'t load config for'''
							raise EnvironmentError(a_ )

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

					return Config.load_yaml(a_ ), kwargs

def        a						(      _UpperCAmelCase							:					Tuple						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	torch.load('''dump.pt''' ,   map_location=in_tensor.device						)
		__UpperCAmelCase    :     Optional[int]													=	in_tensor.numpy()
		__UpperCAmelCase    :     str													=	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()						)*1_00:.4f} %'
		    " element-wise mismatch"
		)
		raise Exception('''tensors are all good'''						)

		# Hugging face functions below

def        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	urlparse(_UpperCAmelCase						)
		return parsed.scheme in ("http", "https")

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		__UpperCAmelCase    :     int													=	'''/''' not in model_id
		if legacy_format:
				return f'{endpoint}/{model_id}-{filename}'
		else:
				return f'{endpoint}/{model_id}/{filename}'

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Tuple=None ,   _UpperCAmelCase							:					List[Any]=0 ,   _UpperCAmelCase							:					Optional[int]=None ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	'''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    :     Optional[int]													=	{'''user-agent''': ua}
		if resume_size > 0:
				__UpperCAmelCase    :     Optional[int]													=	'''bytes=%d-''' % (resume_size,)
		__UpperCAmelCase    :     List[str]													=	requests.get(_UpperCAmelCase ,   stream=_UpperCAmelCase ,   proxies=_UpperCAmelCase ,   headers=_UpperCAmelCase						)
		if response.status_code == 4_16:  # Range not satisfiable
				return
		__UpperCAmelCase    :     Optional[Any]													=	response.headers.get('''Content-Length'''						)
		__UpperCAmelCase    :     List[str]													=	resume_size + int(_UpperCAmelCase						) if content_length is not None else None
		__UpperCAmelCase    :     List[Any]													=	tqdm(
		    unit='''B''' ,   unit_scale=_UpperCAmelCase ,   total=_UpperCAmelCase ,   initial=_UpperCAmelCase ,   desc='''Downloading''' ,   )
		for chunk in response.iter_content(chunk_size=10_24						):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(_UpperCAmelCase						)						)
						temp_file.write(_UpperCAmelCase						)
		progress.close()

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Tuple=False ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Dict=10 ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Any=None ,   _UpperCAmelCase							:					str=False ,   ):







		'''simple docstring'''





		if cache_dir is None:
				__UpperCAmelCase    :     int													=	TRANSFORMERS_CACHE
		if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
				__UpperCAmelCase    :     Dict													=	str(_UpperCAmelCase						)

		os.makedirs(_UpperCAmelCase ,   exist_ok=_UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	None
		if not local_files_only:
				try:
						__UpperCAmelCase    :     Tuple													=	requests.head(_UpperCAmelCase ,   allow_redirects=_UpperCAmelCase ,   proxies=_UpperCAmelCase ,   timeout=_UpperCAmelCase						)
						if response.status_code == 2_00:
								__UpperCAmelCase    :     int													=	response.headers.get('''ETag'''						)
				except (EnvironmentError, requests.exceptions.Timeout):
						# etag is already None
						pass

		__UpperCAmelCase    :     Dict													=	url_to_filename(_UpperCAmelCase ,   _UpperCAmelCase						)

		# get cache path to put the file
		__UpperCAmelCase    :     Optional[int]													=	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    :     Union[str, Any]													=	[
						    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    :     Optional[int]													=	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    :     Any													=	cache_path + '''.incomplete'''

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

						__UpperCAmelCase    :     int													=	_resumable_file_manager
						if os.path.exists(_UpperCAmelCase						):
								__UpperCAmelCase    :     Dict													=	os.stat(_UpperCAmelCase						).st_size
						else:
								__UpperCAmelCase    :     Any													=	0
				else:
						__UpperCAmelCase    :     str													=	partial(tempfile.NamedTemporaryFile ,   dir=_UpperCAmelCase ,   delete=_UpperCAmelCase						)
						__UpperCAmelCase    :     List[Any]													=	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    :     str													=	{'''url''': url, '''etag''': etag}
				__UpperCAmelCase    :     Optional[int]													=	cache_path + '''.json'''
				with open(_UpperCAmelCase ,   '''w'''						) as meta_file:
						json.dump(_UpperCAmelCase ,   _UpperCAmelCase						)

		return cache_path

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	url.encode('''utf-8'''						)
		__UpperCAmelCase    :     int													=	shaaaa(_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	url_hash.hexdigest()

		if etag:
				__UpperCAmelCase    :     List[Any]													=	etag.encode('''utf-8'''						)
				__UpperCAmelCase    :     int													=	shaaaa(_UpperCAmelCase						)
				filename += "." + etag_hash.hexdigest()

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

		return filename

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]=None ,   _UpperCAmelCase							:					List[Any]=False ,   _UpperCAmelCase							:					Any=None ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					int=False ,   _UpperCAmelCase							:					Dict=False ,   ):







		'''simple docstring'''





		if cache_dir is None:
				__UpperCAmelCase    :     Tuple													=	TRANSFORMERS_CACHE
		if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
				__UpperCAmelCase    :     Optional[Any]													=	str(_UpperCAmelCase						)
		if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
				__UpperCAmelCase    :     Union[str, Any]													=	str(_UpperCAmelCase						)

		if is_remote_url(_UpperCAmelCase						):
				# URL, so get it from the cache (downloading if necessary)
				__UpperCAmelCase    :     int													=	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    :     Optional[Any]													=	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    :     Any													=	os.path.split(_UpperCAmelCase						)
				__UpperCAmelCase    :     List[Any]													=	output_file.replace('''.''' ,   '''-'''						) + '''-extracted'''
				__UpperCAmelCase    :     List[Any]													=	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    :     Tuple													=	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    :     Optional[int]													=	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							:					str ,   _UpperCAmelCase							:					int=","						):







		'''simple docstring'''





		assert isinstance(_UpperCAmelCase ,   _UpperCAmelCase						)
		if os.path.isfile(_UpperCAmelCase						):
				with open(_UpperCAmelCase						) as f:
						__UpperCAmelCase    :     List[str]													=	eval(f.read()						)
		else:
				__UpperCAmelCase    :     Tuple													=	requests.get(_UpperCAmelCase						)
				try:
						__UpperCAmelCase    :     Optional[int]													=	requests.json()
				except Exception:
						__UpperCAmelCase    :     Any													=	req.content.decode()
						assert data is not None, "could not connect"
						try:
								__UpperCAmelCase    :     Optional[Any]													=	eval(_UpperCAmelCase						)
						except Exception:
								__UpperCAmelCase    :     List[Any]													=	data.split('''\n'''						)
				req.close()
		return data

def        a						(      _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	requests.get(_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	np.array(Image.open(BytesIO(response.content						)						)						)
		return img

def        a						(      _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





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

def        a						(      ):







		'''simple docstring'''





		print(f'{os.path.abspath(os.path.join(_UpperCAmelCase ,   os.pardir						)						)}/demo.ipynb'						)

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[str]="RGB"						):







		'''simple docstring'''





		assert isinstance(_UpperCAmelCase ,   _UpperCAmelCase						)
		if os.path.isfile(_UpperCAmelCase						):
				__UpperCAmelCase    :     str													=	cva.imread(_UpperCAmelCase						)
		else:
				__UpperCAmelCase    :     Any													=	get_image_from_url(_UpperCAmelCase						)
				assert img is not None, f'could not connect to: {im}'
		__UpperCAmelCase    :     List[Any]													=	cva.cvtColor(_UpperCAmelCase ,   cva.COLOR_BGR2RGB						)
		if input_format == "RGB":
				__UpperCAmelCase    :     Dict													=	img[:, :, ::-1]
		return img

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Optional[int]=1						):







		'''simple docstring'''





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










import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       None




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.feature_extraction_class(**self.feat_extract_dict )
					__UpperCAmelCase    :     Optional[int]													=	json.loads(feat_extract.to_json_string() )
					for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]       ,       a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(a_       ,       '''feat_extract.json''' )
							feat_extract_first.to_json_file(a_ )
							__UpperCAmelCase    :     Any													=	self.feature_extraction_class.from_json_file(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     List[str]													=	feat_extract_first.save_pretrained(a_ )[0]
							check_json_file_has_correct_format(a_ )
							__UpperCAmelCase    :     Optional[Any]													=	self.feature_extraction_class.from_pretrained(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.feature_extraction_class()
					self.assertIsNotNone(a_ )





