




"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''gpt_bigcode'''
 UpperCAmelCase       :	str     =      ['''past_key_values''']
 UpperCAmelCase       :	Dict     =      {
     '''hidden_size''': '''n_embd''',
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			Dict=50_257       ,				_UpperCAmelCase			:			List[Any]=1_024       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			Any=12       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			str="gelu_pytorch_tanh"       ,				_UpperCAmelCase			:			str=0.1       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[Any]=1E-5       ,				_UpperCAmelCase			:			List[Any]=0.02       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[Any]=50_256       ,				_UpperCAmelCase			:			Dict=50_256       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Any=True       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  vocab_size
       _A     =  n_positions
       _A     =  n_embd
       _A     =  n_layer
       _A     =  n_head
       _A     =  n_inner
       _A     =  activation_function
       _A     =  resid_pdrop
       _A     =  embd_pdrop
       _A     =  attn_pdrop
       _A     =  layer_norm_epsilon
       _A     =  initializer_range
       _A     =  scale_attn_weights
       _A     =  use_cache
       _A     =  attention_softmax_in_fpaa
       _A     =  scale_attention_softmax_in_fpaa
       _A     =  multi_query

       _A     =  bos_token_id
       _A     =  eos_token_id

       super().__init__(bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)







"""simple docstring"""

import math
from datetime import datetime, timedelta






def 		_snake_case	(   _snake_case				:   int				)       ->   datetime:






      '''simple docstring'''


      _A     =  year % 19
      _A     =  year % 4
      _A     =  year % 7
      _A     =  math.floor(year / 1_00				)
      _A     =  math.floor((13 + 8 * leap_day_inhibits) / 25				)
      _A     =  leap_day_inhibits / 4
      _A     =  (
          15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
      ) % 30
      _A     =  (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

      # days to be added to March 21
      _A     =  (19 * metonic_cycle + secular_moon_shift) % 30

      # PHM -> Paschal Full Moon
      _A     =  (
          2 * julian_leap_year
          + 4 * non_leap_year
          + 6 * days_to_add
          + century_starting_point
      ) % 7

      if days_to_add == 29 and days_from_phm_to_sunday == 6:
            return datetime(_snake_case							,    4							,    19				)
      elif days_to_add == 28 and days_from_phm_to_sunday == 6:
            return datetime(_snake_case							,    4							,    18				)
      else:
            return datetime(_snake_case							,    3							,    22				) + timedelta(
                days=int(days_to_add + days_from_phm_to_sunday				)				)


if __name__ == "__main__":
       for year in (1_994, 2_000, 2_010, 2_021, 2_023):
              a							      =     '''will be''' if year > datetime.now().year else '''was'''
              print(F'''Easter in {year} {tense} {gauss_easter(year)}''')

