

UpperCamelCase__  :      List[str]			=    [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCamelCase__  :      int			=    [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCamelCase__  :      Dict			=    {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		str       ):





				"""simple docstring"""


				assert len(str(_SCREAMING_SNAKE_CASE       )       ) > 2, "year should be in YYYY format"
				assert 1 <= month <= 12, "month should be between 1 to 12"
				assert 1 <= day <= 31, "day should be between 1 to 31"

				# Doomsday algorithm:
				SCREAMING_SNAKE_CASE_									=						year // 100
				SCREAMING_SNAKE_CASE_									=						(5 * (century % 4) + 2) % 7
				SCREAMING_SNAKE_CASE_									=						year % 100
				SCREAMING_SNAKE_CASE_									=						centurian % 12
				SCREAMING_SNAKE_CASE_									=						(
				    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
				) % 7
				SCREAMING_SNAKE_CASE_									=						(
				    DOOMSDAY_NOT_LEAP[month - 1]
				    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
				    else DOOMSDAY_LEAP[month - 1]
				)
				SCREAMING_SNAKE_CASE_									=						(dooms_day + day - day_anchor) % 7
				return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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


UpperCamelCase__  :      List[Any]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[str]			=    {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Any						 =  'biogpt'

  def __init__(					self						,		_A=42384						,		_A=1024						,		_A=24						,		_A=16						,		_A=4096						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=1024						,		_A=0.0_2						,		_A=1E-12						,		_A=True						,		_A=True						,		_A=0.0						,		_A=0.0						,		_A=1						,		_A=0						,		_A=2						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						scale_embedding
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						layerdrop
      SCREAMING_SNAKE_CASE_									=						activation_dropout
      super().__init__(pad_token_id=_A						,		bos_token_id=_A						,		eos_token_id=_A						,		**_A)


