


from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE						:Optional[Any]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {
    """huggingface/time-series-transformer-tourism-monthly""": (
        """https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class      __lowerCAmelCase  (			_UpperCAmelCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'time_series_transformer'
  _SCREAMING_SNAKE_CASE		     =							{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[Any] = None     ,					_lowerCAmelCase							:							Optional[int] = None     ,					_lowerCAmelCase							:							List[Any] = "student_t"     ,					_lowerCAmelCase							:							Any = "nll"     ,					_lowerCAmelCase							:							Tuple = 1     ,					_lowerCAmelCase							:							Optional[int] = [1, 2, 3, 4, 5, 6, 7]     ,					_lowerCAmelCase							:							Union[str, Any] = "mean"     ,					_lowerCAmelCase							:							Optional[Any] = 0     ,					_lowerCAmelCase							:							Union[str, Any] = 0     ,					_lowerCAmelCase							:							str = 0     ,					_lowerCAmelCase							:							Union[str, Any] = 0     ,					_lowerCAmelCase							:							Union[str, Any] = None     ,					_lowerCAmelCase							:							List[str] = None     ,					_lowerCAmelCase							:							Union[str, Any] = 3_2     ,					_lowerCAmelCase							:							List[str] = 3_2     ,					_lowerCAmelCase							:							Tuple = 2     ,					_lowerCAmelCase							:							List[str] = 2     ,					_lowerCAmelCase							:							List[Any] = 2     ,					_lowerCAmelCase							:							str = 2     ,					_lowerCAmelCase							:							Tuple = True     ,					_lowerCAmelCase							:							str = "gelu"     ,					_lowerCAmelCase							:							Tuple = 6_4     ,					_lowerCAmelCase							:							str = 0.1     ,					_lowerCAmelCase							:							int = 0.1     ,					_lowerCAmelCase							:							List[Any] = 0.1     ,					_lowerCAmelCase							:							Optional[Any] = 0.1     ,					_lowerCAmelCase							:							Optional[int] = 0.1     ,					_lowerCAmelCase							:							Union[str, Any] = 1_0_0     ,					_lowerCAmelCase							:							Dict = 0.02     ,					_lowerCAmelCase							:							str=True     ,					**_lowerCAmelCase							:							Dict     ,					) ->			Any:




     """simple docstring"""
     # time series specific configuration
     snake_case_            =       prediction_length
     snake_case_            =       context_length or prediction_length
     snake_case_            =       distribution_output
     snake_case_            =       loss
     snake_case_            =       input_size
     snake_case_            =       num_time_features
     snake_case_            =       lags_sequence
     snake_case_            =       scaling
     snake_case_            =       num_dynamic_real_features
     snake_case_            =       num_static_real_features
     snake_case_            =       num_static_categorical_features
     if cardinality and num_static_categorical_features > 0:
        if len(SCREAMING_SNAKE_CASE_	) != num_static_categorical_features:
           raise ValueError(
               "The cardinality should be a list of the same length as `num_static_categorical_features`"	)
        snake_case_            =       cardinality
     else:
        snake_case_            =       [0]
     if embedding_dimension and num_static_categorical_features > 0:
        if len(SCREAMING_SNAKE_CASE_	) != num_static_categorical_features:
           raise ValueError(
               "The embedding dimension should be a list of the same length as `num_static_categorical_features`"	)
        snake_case_            =       embedding_dimension
     else:
        snake_case_            =       [min(5_0     ,					(cat + 1) // 2	) for cat in self.cardinality]
     snake_case_            =       num_parallel_samples

     # Transformer architecture configuration
     snake_case_            =       input_size * len(SCREAMING_SNAKE_CASE_	) + self._number_of_features
     snake_case_            =       d_model
     snake_case_            =       encoder_attention_heads
     snake_case_            =       decoder_attention_heads
     snake_case_            =       encoder_ffn_dim
     snake_case_            =       decoder_ffn_dim
     snake_case_            =       encoder_layers
     snake_case_            =       decoder_layers

     snake_case_            =       dropout
     snake_case_            =       attention_dropout
     snake_case_            =       activation_dropout
     snake_case_            =       encoder_layerdrop
     snake_case_            =       decoder_layerdrop

     snake_case_            =       activation_function
     snake_case_            =       init_std

     snake_case_            =       use_cache

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



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




     """simple docstring"""
     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
     )








'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "https://www.worldometers.info/coronavirus/"				)	->						covid_data:
     __lowerCamelCase  :      Union[str, Any]														=						'//div[@class = "maincounter-number"]/span/text()'
     return covid_data(*html.fromstring(requests.get(UpperCAmelCase_				).content				).xpath(UpperCAmelCase_				)				)


A__						:   str				=    """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))






