
"""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

							lowerCamelCase__   					=		True
except ImportError:
							lowerCamelCase__   					=		False


try:
							from torch.hub import _get_torch_home

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

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

lowerCamelCase__   					=		"""https://cdn.huggingface.co"""
lowerCamelCase__   					=		"""https://s3.amazonaws.com/models.huggingface.co/bert"""
lowerCamelCase__   					=		"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
lowerCamelCase__   					=		os.path.join(PATH, """config.yaml""")
lowerCamelCase__   					=		os.path.join(PATH, """attributes.txt""")
lowerCamelCase__   					=		os.path.join(PATH, """objects.txt""")
lowerCamelCase__   					=		os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
lowerCamelCase__   					=		os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
lowerCamelCase__   					=		os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
lowerCamelCase__   					=		"""pytorch_model.bin"""
lowerCamelCase__   					=		"""config.yaml"""
def     __lowerCAmelCase				(_UpperCamelCase=OBJECTS       ,  _UpperCamelCase=ATTRIBUTES		):
	__lowerCAmelCase			:						Any									=      []
	with open(_UpperCamelCase		) as f:
		for object in f.readlines():
			vg_classes.append(object.split(','		)[0].lower().strip()		)

	__lowerCAmelCase			:						Union[str, Any]									=      []
	with open(_UpperCamelCase		) as f:
		for object in f.readlines():
			vg_attrs.append(object.split(','		)[0].lower().strip()		)
	return vg_classes, vg_attrs
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[Any]									=      OrderedDict()
	with open(_UpperCamelCase       ,  'rb'		) as f:
		__lowerCAmelCase			:						str									=      pkl.load(_UpperCamelCase		)['model']
	for k in copy.deepcopy(list(ckp.keys()		)		):
		__lowerCAmelCase			:						int									=      ckp.pop(_UpperCamelCase		)
		if isinstance(_UpperCamelCase       ,  np.ndarray		):
			__lowerCAmelCase			:						List[Any]									=      torch.tensor(_UpperCamelCase		)
		else:
			assert isinstance(_UpperCamelCase       ,  torch.tensor		), type(_UpperCamelCase		)
		__lowerCAmelCase			:						Dict									=      v
	return r




class  A__       :
		A_						:       int				   =	{}

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

			__lowerCAmelCase			:						str									=      d

		def __repr__(      self ):
			return str(list((self._pointer.keys()) ) )

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

		def 							__lowerCamelCase							(      self ):
			return self._pointer

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			with open(f"{file_name}"       ,		'w' ) as stream:
				dump(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			with open(f"{file_name}"       ,		'w' ) as stream:
				json.dump(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		@staticmethod
		def 							__lowerCamelCase							(      _SCREAMING_SNAKE_CASE ):
			with open(_SCREAMING_SNAKE_CASE ) as stream:
				__lowerCAmelCase			:						Dict									=      load(_SCREAMING_SNAKE_CASE       ,		Loader=_SCREAMING_SNAKE_CASE )
			return data

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

		@classmethod
		def 							__lowerCamelCase							(      cls       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      cls.get_config_dict(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )
			return cls(_SCREAMING_SNAKE_CASE )







		@classmethod
		def 							__lowerCamelCase							(      cls       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[Any]									=      kwargs.pop('cache_dir'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      kwargs.pop('force_download'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[int]									=      kwargs.pop('resume_download'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      kwargs.pop('proxies'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      kwargs.pop('local_files_only'       ,		_SCREAMING_SNAKE_CASE )

			if os.path.isdir(_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Optional[int]									=      os.path.join(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			elif os.path.isfile(_SCREAMING_SNAKE_CASE ) or is_remote_url(_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Tuple									=      pretrained_model_name_or_path
			else:
				__lowerCAmelCase			:						List[str]									=      hf_bucket_url(_SCREAMING_SNAKE_CASE       ,		filename=_SCREAMING_SNAKE_CASE       ,		use_cdn=_SCREAMING_SNAKE_CASE )

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

				__lowerCAmelCase			:						int									=      Config.load_yaml(_SCREAMING_SNAKE_CASE )

			except EnvironmentError:
				__lowerCAmelCase			:						Tuple									=      'Can\'t load config for'
				raise EnvironmentError(_SCREAMING_SNAKE_CASE )

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

			return Config.load_yaml(_SCREAMING_SNAKE_CASE ), kwargs



def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						str									=      torch.load('dump.pt'       ,  map_location=in_tensor.device		)
	__lowerCAmelCase			:						Optional[int]									=      in_tensor.numpy()
	__lowerCAmelCase			:						Dict									=      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     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[Any]									=      urlparse(_UpperCamelCase		)
	return parsed.scheme in ("http", "https")
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=True		):
	__lowerCAmelCase			:						str									=      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	__lowerCAmelCase			:						Any									=      '/' not in model_id
	if legacy_format:
		return F"{endpoint}/{model_id}-{filename}"
	else:
		return F"{endpoint}/{model_id}/{filename}"
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=None       ,  _UpperCamelCase=0       ,  _UpperCamelCase=None       ,  ):
	__lowerCAmelCase			:						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
	__lowerCAmelCase			:						Dict									=      {'user-agent': ua}
	if resume_size > 0:
		__lowerCAmelCase			:						Any									=      'bytes=%d-' % (resume_size,)
	__lowerCAmelCase			:						int									=      requests.get(_UpperCamelCase       ,  stream=_UpperCamelCase       ,  proxies=_UpperCamelCase       ,  headers=_UpperCamelCase		)
	if response.status_code == 416:  # Range not satisfiable
		return
	__lowerCAmelCase			:						Optional[Any]									=      response.headers.get('Content-Length'		)
	__lowerCAmelCase			:						List[str]									=      resume_size + int(_UpperCamelCase		) if content_length is not None else None
	__lowerCAmelCase			:						Tuple									=      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     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False       ,  _UpperCamelCase=None       ,  _UpperCamelCase=10       ,  _UpperCamelCase=False       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False       ,  ):
	if cache_dir is None:
		__lowerCAmelCase			:						Dict									=      TRANSFORMERS_CACHE
	if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
		__lowerCAmelCase			:						Tuple									=      str(_UpperCamelCase		)

	os.makedirs(_UpperCamelCase       ,  exist_ok=_UpperCamelCase		)

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

	__lowerCAmelCase			:						Any									=      url_to_filename(_UpperCamelCase       ,  _UpperCamelCase		)

	# get cache path to put the file
	__lowerCAmelCase			:						List[Any]									=      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:
			__lowerCAmelCase			:						int									=      [
			    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.
	__lowerCAmelCase			:						Tuple									=      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:
			__lowerCAmelCase			:						Tuple									=      cache_path + '.incomplete'

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

			__lowerCAmelCase			:						str									=      _resumable_file_manager
			if os.path.exists(_UpperCamelCase		):
				__lowerCAmelCase			:						Optional[int]									=      os.stat(_UpperCamelCase		).st_size
			else:
				__lowerCAmelCase			:						Optional[int]									=      0
		else:
			__lowerCAmelCase			:						Optional[int]									=      partial(tempfile.NamedTemporaryFile       ,  dir=_UpperCamelCase       ,  delete=_UpperCamelCase		)
			__lowerCAmelCase			:						Union[str, 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		)

		__lowerCAmelCase			:						Tuple									=      {'url': url, 'etag': etag}
		__lowerCAmelCase			:						List[str]									=      cache_path + '.json'
		with open(_UpperCamelCase       ,  'w'		) as meta_file:
			json.dump(_UpperCamelCase       ,  _UpperCamelCase		)

	return cache_path
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase=None		):
	__lowerCAmelCase			:						int									=      url.encode('utf-8'		)
	__lowerCAmelCase			:						List[Any]									=      shaaaa(_UpperCamelCase		)
	__lowerCAmelCase			:						Optional[int]									=      url_hash.hexdigest()

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

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

	return filename
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False       ,  _UpperCamelCase=None       ,  _UpperCamelCase=False       ,  _UpperCamelCase=False       ,  _UpperCamelCase=False       ,  ):
	if cache_dir is None:
		__lowerCAmelCase			:						str									=      TRANSFORMERS_CACHE
	if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
		__lowerCAmelCase			:						Optional[int]									=      str(_UpperCamelCase		)
	if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
		__lowerCAmelCase			:						List[Any]									=      str(_UpperCamelCase		)

	if is_remote_url(_UpperCamelCase		):
		# URL, so get it from the cache (downloading if necessary)
		__lowerCAmelCase			:						List[str]									=      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.
		__lowerCAmelCase			:						Union[str, 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/"
		__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[int]									=      os.path.split(_UpperCamelCase		)
		__lowerCAmelCase			:						List[str]									=      output_file.replace('.'       ,  '-'		) + '-extracted'
		__lowerCAmelCase			:						Union[str, 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
		__lowerCAmelCase			:						Dict									=      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		):
				__lowerCAmelCase			:						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     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase=","		):
	assert isinstance(_UpperCamelCase       ,  _UpperCamelCase		)
	if os.path.isfile(_UpperCamelCase		):
		with open(_UpperCamelCase		) as f:
			__lowerCAmelCase			:						Optional[int]									=      eval(f.read()		)
	else:
		__lowerCAmelCase			:						str									=      requests.get(_UpperCamelCase		)
		try:
			__lowerCAmelCase			:						Optional[int]									=      requests.json()
		except Exception:
			__lowerCAmelCase			:						Dict									=      req.content.decode()
			assert data is not None, "could not connect"
			try:
				__lowerCAmelCase			:						str									=      eval(_UpperCamelCase		)
			except Exception:
				__lowerCAmelCase			:						List[Any]									=      data.split('\n'		)
		req.close()
	return data
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						int									=      requests.get(_UpperCamelCase		)
	__lowerCAmelCase			:						Dict									=      np.array(Image.open(BytesIO(response.content		)		)		)
	return img
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						List[Any]									=      url.split('/'		)[-1]
	if fn not in os.listdir(os.getcwd()		):
		wget.download(_UpperCamelCase		)
	with open(_UpperCamelCase       ,  'rb'		) as stream:
		__lowerCAmelCase			:						Optional[int]									=      pkl.load(_UpperCamelCase		)
	__lowerCAmelCase			:						Tuple									=      weights.pop('model'		)
	__lowerCAmelCase			:						str									=      {}
	for k, v in model.items():
		__lowerCAmelCase			:						str									=      torch.from_numpy(_UpperCamelCase		)
		if "running_var" in k:
			__lowerCAmelCase			:						Optional[Any]									=      torch.tensor([0]		)
			__lowerCAmelCase			:						List[str]									=      k.replace('running_var'       ,  'num_batches_tracked'		)
			__lowerCAmelCase			:						Tuple									=      zero
	return new
def     __lowerCAmelCase				():
	print(F"{os.path.abspath(os.path.join(_UpperCamelCase       ,  os.pardir		)		)}/demo.ipynb"		)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase="RGB"		):
	assert isinstance(_UpperCamelCase       ,  _UpperCamelCase		)
	if os.path.isfile(_UpperCamelCase		):
		__lowerCAmelCase			:						List[str]									=      cva.imread(_UpperCamelCase		)
	else:
		__lowerCAmelCase			:						str									=      get_image_from_url(_UpperCamelCase		)
		assert img is not None, F"could not connect to: {im}"
	__lowerCAmelCase			:						Dict									=      cva.cvtColor(_UpperCamelCase       ,  cva.COLOR_BGR2RGB		)
	if input_format == "RGB":
		__lowerCAmelCase			:						Tuple									=      img[:, :, ::-1]
	return img
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase=1		):
	return (images[i : i + batch] for i in range(0       ,  len(_UpperCamelCase		)       ,  _UpperCamelCase		))






"""simple docstring"""




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a,       a      :		str	=				image.size
	a,       a      :		Tuple	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	a      :		Union[str, Any]	=				image.resize((w, h)    ,       resample=PIL_INTERPOLATION["lanczos"]					)
	a      :		int	=				np.array(_lowercase					).astype(np.floataa					) / 255.0
	a      :		List[str]	=				image[None].transpose(0    ,       3    ,       1    ,       2					)
	a      :		Dict	=				torch.from_numpy(_lowercase					)
	return 2.0 * image - 1.0







class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase__      ,					unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 1      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 0.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[Tuple, ImagePipelineOutput]:
								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		int	=				1
								elif isinstance(lowerCAmelCase__      ,					torch.Tensor      ):
									a      :		str	=				image.shape[0]
								else:
									raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__      )}"""      )

								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		Tuple	=				preprocess(lowerCAmelCase__      )

								a,       a      :		Optional[Any]	=				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a      :		Tuple	=				(batch_size, self.unet.config.in_channels // 2, height, width)
								a      :		List[str]	=				next(self.unet.parameters()      ).dtype

								a      :		Any	=				randn_tensor(lowerCAmelCase__      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				image.to(device=self.device      ,					dtype=lowerCAmelCase__      )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(lowerCAmelCase__      ,					device=self.device      )
								a      :		int	=				self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								a      :		str	=				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a      :		str	=				"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a      :		List[Any]	=				{}
								if accepts_eta:
									a      :		Any	=				eta

								for t in self.progress_bar(lowerCAmelCase__      ):
									# concat latents and low resolution image in the channel dimension.
									a      :		str	=				torch.cat([latents, image]      ,					dim=1      )
									a      :		int	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )
									# predict the noise residual
									a      :		Union[str, Any]	=				self.unet(lowerCAmelCase__      ,					lowerCAmelCase__      ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a      :		Tuple	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

								# decode the image latents with the VQVAE
								a      :		List[Any]	=				self.vqvae.decode(lowerCAmelCase__      ).sample
								a      :		int	=				torch.clamp(lowerCAmelCase__      ,					-1.0      ,					1.0      )
								a      :		Optional[int]	=				image / 2 + 0.5
								a      :		Optional[int]	=				image.cpu().permute(0      ,					2      ,					3      ,					1      ).numpy()

								if output_type == "pil":
									a      :		Union[str, Any]	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


