





"""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      (			a_						:int=OBJECTS   ,       a_						:str=ATTRIBUTES)		->		Any:
				__a :						Dict			       =  []
				with open(a_) as f:
								for object in f.readlines():
												vg_classes.append(object.split(''',''')[0].lower().strip())

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

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




class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  {}







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

									__a :						Tuple			       =  d







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







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







					def  _lowerCamelCase		(						self     ):
									return self._pointer







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







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







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase     ):
									with open(_UpperCAmelCase     ) as stream:
													__a :						int			       =  load(_UpperCAmelCase	,			Loader=_UpperCAmelCase     )
									return data







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







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									__a :						Tuple			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )
									return cls(_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									__a :						Dict			       =  kwargs.pop('''cache_dir'''	,			_UpperCAmelCase     )
									__a :						int			       =  kwargs.pop('''force_download'''	,			_UpperCAmelCase     )
									__a :						Tuple			       =  kwargs.pop('''resume_download'''	,			_UpperCAmelCase     )
									__a :						Dict			       =  kwargs.pop('''proxies'''	,			_UpperCAmelCase     )
									__a :						Tuple			       =  kwargs.pop('''local_files_only'''	,			_UpperCAmelCase     )

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

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

													__a :						Tuple			       =  Config.load_yaml(_UpperCAmelCase     )

									except EnvironmentError:
													__a :						Tuple			       =  '''Can\'t load config for'''
													raise EnvironmentError(_UpperCAmelCase     )

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

									return Config.load_yaml(_UpperCAmelCase     ), kwargs






def     __A      (			a_						:Any)		->		Union[str, Any]:
				__a :						Optional[int]			       =  torch.load('''dump.pt'''   ,       map_location=in_tensor.device)
				__a :						List[str]			       =  in_tensor.numpy()
				__a :						List[Any]			       =  out_tensor.numpy()[0]
				print(na.shape   ,       na[0, 0, :5])
				print(na.shape   ,       na[0, 0, :5])
				assert np.allclose(a_   ,       a_   ,       rtol=0.0_1   ,       atol=0.1), (
				    F"""{sum([1 for x in np.isclose(a_   ,       a_   ,       rtol=0.0_1   ,       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      (			a_						:List[Any])		->		List[Any]:
				__a :						List[str]			       =  urlparse(a_)
				return parsed.scheme in ("http", "https")

def     __A      (			a_						:str   ,       a_						:str   ,       a_						:str=True)		->		str:
				__a :						Any			       =  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				__a :						Optional[int]			       =  '''/''' not in model_id
				if legacy_format:
								return F"""{endpoint}/{model_id}-{filename}"""
				else:
								return F"""{endpoint}/{model_id}/{filename}"""

def     __A      (			a_						:Optional[Any]   ,       a_						:Optional[Any]   ,       a_						:List[str]=None   ,       a_						:Optional[Any]=0   ,       a_						:Optional[Any]=None   ,       )		->		int:
				__a :						List[str]			       =  '''python/{}'''.format(sys.version.split()[0])
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__)
				if isinstance(a_   ,       a_):
								ua += "; " + "; ".join('''{}/{}'''.format(a_   ,       a_) for k, v in user_agent.items())
				elif isinstance(a_   ,       a_):
								ua += "; " + user_agent
				__a :						Optional[Any]			       =  {'''user-agent''': ua}
				if resume_size > 0:
								__a :						Dict			       =  '''bytes=%d-''' % (resume_size,)
				__a :						Dict			       =  requests.get(a_   ,       stream=a_   ,       proxies=a_   ,       headers=a_)
				if response.status_code == 4_16:  # Range not satisfiable
								return
				__a :						Tuple			       =  response.headers.get('''Content-Length''')
				__a :						Dict			       =  resume_size + int(a_) if content_length is not None else None
				__a :						Optional[int]			       =  tqdm(
				    unit='''B'''   ,       unit_scale=a_   ,       total=a_   ,       initial=a_   ,       desc='''Downloading'''   ,       )
				for chunk in response.iter_content(chunk_size=10_24):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(a_))
												temp_file.write(a_)
				progress.close()

def     __A      (			a_						:Dict   ,       a_						:Tuple=None   ,       a_						:Optional[Any]=False   ,       a_						:List[Any]=None   ,       a_						:Union[str, Any]=10   ,       a_						:Union[str, Any]=False   ,       a_						:List[Any]=None   ,       a_						:Dict=False   ,       )		->		Any:
				if cache_dir is None:
								__a :						int			       =  TRANSFORMERS_CACHE
				if isinstance(a_   ,       a_):
								__a :						List[str]			       =  str(a_)

				os.makedirs(a_   ,       exist_ok=a_)

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

				__a :						Any			       =  url_to_filename(a_   ,       a_)

				# get cache path to put the file
				__a :						Tuple			       =  os.path.join(a_   ,       a_)

				# 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(a_):
												return cache_path
								else:
												__a :						Tuple			       =  [
												    file
												    for file in fnmatch.filter(os.listdir(a_)   ,       filename + '''.*''')
												    if not file.endswith('''.json''') and not file.endswith('''.lock''')
												]
												if len(a_) > 0:
																return os.path.join(a_   ,       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(a_) and not force_download:
								return cache_path

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

								if resume_download:
												__a :						Optional[int]			       =  cache_path + '''.incomplete'''

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

												__a :						Optional[int]			       =  _resumable_file_manager
												if os.path.exists(a_):
																__a :						Optional[int]			       =  os.stat(a_).st_size
												else:
																__a :						Any			       =  0
								else:
												__a :						List[str]			       =  partial(tempfile.NamedTemporaryFile   ,       dir=a_   ,       delete=a_)
												__a :						int			       =  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'''   ,       a_   ,       temp_file.name   ,       )

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

								os.replace(temp_file.name   ,       a_)

								__a :						Dict			       =  {'''url''': url, '''etag''': etag}
								__a :						Optional[Any]			       =  cache_path + '''.json'''
								with open(a_   ,       '''w''') as meta_file:
												json.dump(a_   ,       a_)

				return cache_path

def     __A      (			a_						:Union[str, Any]   ,       a_						:List[str]=None)		->		Any:
				__a :						Union[str, Any]			       =  url.encode('''utf-8''')
				__a :						Tuple			       =  shaaaa(a_)
				__a :						Optional[Any]			       =  url_hash.hexdigest()

				if etag:
								__a :						Optional[Any]			       =  etag.encode('''utf-8''')
								__a :						Tuple			       =  shaaaa(a_)
								filename += "." + etag_hash.hexdigest()

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

				return filename

def     __A      (			a_						:Optional[Any]   ,       a_						:Optional[int]=None   ,       a_						:Tuple=False   ,       a_						:Optional[int]=None   ,       a_						:Union[str, Any]=False   ,       a_						:Tuple=None   ,       a_						:List[Any]=False   ,       a_						:str=False   ,       a_						:List[Any]=False   ,       )		->		Any:
				if cache_dir is None:
								__a :						Dict			       =  TRANSFORMERS_CACHE
				if isinstance(a_   ,       a_):
								__a :						int			       =  str(a_)
				if isinstance(a_   ,       a_):
								__a :						List[Any]			       =  str(a_)

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

				if extract_compressed_file:
								if not is_zipfile(a_) and not tarfile.is_tarfile(a_):
												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/"
								__a :						List[str]			       =  os.path.split(a_)
								__a :						Optional[Any]			       =  output_file.replace('''.'''   ,       '''-''') + '''-extracted'''
								__a :						Optional[int]			       =  os.path.join(a_   ,       a_)

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

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

								return output_path_extracted

				return output_path

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

def     __A      (			a_						:List[str])		->		str:
				__a :						List[Any]			       =  requests.get(a_)
				__a :						Tuple			       =  np.array(Image.open(BytesIO(response.content)))
				return img

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

def     __A      (			)		->		int:
				print(F"""{os.path.abspath(os.path.join(a_   ,       os.pardir))}/demo.ipynb""")

def     __A      (			a_						:Optional[int]   ,       a_						:List[Any]="RGB")		->		str:
				assert isinstance(a_   ,       a_)
				if os.path.isfile(a_):
								__a :						str			       =  cva.imread(a_)
				else:
								__a :						Dict			       =  get_image_from_url(a_)
								assert img is not None, F"""could not connect to: {im}"""
				__a :						Tuple			       =  cva.cvtColor(a_   ,       cva.COLOR_BGR2RGB)
				if input_format == "RGB":
								__a :						Dict			       =  img[:, :, ::-1]
				return img






def     __A      (			a_						:Optional[int]   ,       a_						:str=1)		->		List[str]:
				return (images[i : i + batch] for i in range(0   ,       len(a_)   ,       a_))






"""simple docstring"""






from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A 			=  logging.get_logger(__name__)

A 			=  {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''autoformer'''
					__lowerCAmelCase  =  {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					    '''num_hidden_layers''': '''encoder_layers''',
					}







					def __init__(						self	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "student_t"	,			_UpperCAmelCase = "nll"	,			_UpperCAmelCase = 1	,			_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 64	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = "gelu"	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 100	,			_UpperCAmelCase = 0.0_2	,			_UpperCAmelCase = True	,			_UpperCAmelCase=True	,			_UpperCAmelCase = 10	,			_UpperCAmelCase = 25	,			_UpperCAmelCase = 3	,			**_UpperCAmelCase	,			):
									# time series specific configuration
									__a :						Optional[int]			       =  prediction_length
									__a :						Optional[int]			       =  context_length if context_length is not None else prediction_length
									__a :						Union[str, Any]			       =  distribution_output
									__a :						str			       =  loss
									__a :						Optional[Any]			       =  input_size
									__a :						str			       =  num_time_features
									__a :						Optional[Any]			       =  lags_sequence
									__a :						Any			       =  scaling
									__a :						Optional[Any]			       =  num_dynamic_real_features
									__a :						str			       =  num_static_real_features
									__a :						Any			       =  num_static_categorical_features
									if cardinality is not None and num_static_categorical_features > 0:
													if len(_UpperCAmelCase     ) != num_static_categorical_features:
																	raise ValueError(
																	    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''     )
													__a :						List[str]			       =  cardinality
									else:
													__a :						Any			       =  [0]
									if embedding_dimension is not None and num_static_categorical_features > 0:
													if len(_UpperCAmelCase     ) != num_static_categorical_features:
																	raise ValueError(
																	    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''     )
													__a :						Any			       =  embedding_dimension
									else:
													__a :						Tuple			       =  [min(50	,			(cat + 1) // 2     ) for cat in self.cardinality]
									__a :						Union[str, Any]			       =  num_parallel_samples

									# Transformer architecture configuration
									__a :						Union[str, Any]			       =  input_size * len(self.lags_sequence     ) + self._number_of_features
									__a :						List[Any]			       =  d_model
									__a :						List[str]			       =  encoder_attention_heads
									__a :						Union[str, Any]			       =  decoder_attention_heads
									__a :						Optional[int]			       =  encoder_ffn_dim
									__a :						List[str]			       =  decoder_ffn_dim
									__a :						List[str]			       =  encoder_layers
									__a :						Optional[int]			       =  decoder_layers

									__a :						Union[str, Any]			       =  dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						Optional[Any]			       =  activation_dropout
									__a :						List[str]			       =  encoder_layerdrop
									__a :						Any			       =  decoder_layerdrop

									__a :						Union[str, Any]			       =  activation_function
									__a :						Union[str, Any]			       =  init_std

									__a :						List[str]			       =  use_cache

									# Autoformer
									__a :						int			       =  label_length
									__a :						List[Any]			       =  moving_average
									__a :						str			       =  autocorrelation_factor

									super().__init__(is_encoder_decoder=_UpperCAmelCase	,			**_UpperCAmelCase     )







					@property
					def  _lowerCamelCase		(						self     ):
									return (
									    sum(self.embedding_dimension     )
									    + self.num_dynamic_real_features
									    + self.num_time_features
									    + self.num_static_real_features
									    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
									)