
import warnings

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


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

A     :	Dict    = {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}







class 			__A( a			):
				snake_case_						   =    '''mvp'''
				snake_case_						   =    ['''past_key_values''']
				snake_case_						   =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__(     self		,     _snake_case=50_267		,     _snake_case=1_024		,     _snake_case=12		,     _snake_case=4_096		,     _snake_case=16		,     _snake_case=12		,     _snake_case=4_096		,     _snake_case=16		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case="gelu"		,     _snake_case=1_024		,     _snake_case=0.1		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=0.0		,     _snake_case=False		,     _snake_case=True		,     _snake_case=1		,     _snake_case=0		,     _snake_case=2		,     _snake_case=True		,     _snake_case=2		,     _snake_case=2		,     _snake_case=False		,     _snake_case=100		,     _snake_case=800		,     **_snake_case		,     )    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    vocab_size
								__a										=    max_position_embeddings
								__a										=    d_model
								__a										=    encoder_ffn_dim
								__a										=    encoder_layers
								__a										=    encoder_attention_heads
								__a										=    decoder_ffn_dim
								__a										=    decoder_layers
								__a										=    decoder_attention_heads
								__a										=    dropout
								__a										=    attention_dropout
								__a										=    activation_dropout
								__a										=    activation_function
								__a										=    init_std
								__a										=    encoder_layerdrop
								__a										=    decoder_layerdrop
								__a										=    classifier_dropout
								__a										=    use_cache
								__a										=    encoder_layers
								__a										=    scale_embedding  # scale factor will be sqrt(d_model) if True
								__a										=    use_prompt
								__a										=    prompt_length
								__a										=    prompt_mid_dim

								super().__init__(
								    pad_token_id=__SCREAMING_SNAKE_CASE		,     bos_token_id=__SCREAMING_SNAKE_CASE		,     eos_token_id=__SCREAMING_SNAKE_CASE		,     is_encoder_decoder=__SCREAMING_SNAKE_CASE		,     decoder_start_token_id=__SCREAMING_SNAKE_CASE		,     forced_eos_token_id=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE		,     )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''		,     __SCREAMING_SNAKE_CASE				):
												__a										=    self.bos_token_id
												warnings.warn(
												    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
												    '''The config can simply be saved and uploaded again to be fixed.'''				)


'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     _enforce_args(a__      ,    a__						)
     if n == 0:
          return 0
     __SCREAMING_SNAKE_CASE     =      float("""-inf"""						)
     for i in range(1      ,    n + 1						):
          __SCREAMING_SNAKE_CASE     =      max(
              a__      ,    prices[i - 1] + naive_cut_rod_recursive(n - i      ,    a__						)						)

     return max_revue







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     _enforce_args(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      [float("""-inf"""						) for _ in range(n + 1						)]
     return _top_down_cut_rod_recursive(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     if max_rev[n] >= 0:
          return max_rev[n]
     elif n == 0:
          return 0
     else:
          __SCREAMING_SNAKE_CASE     =      float("""-inf"""						)
          for i in range(1      ,    n + 1						):
               __SCREAMING_SNAKE_CASE     =      max(
                   a__      ,    prices[i - 1] + _top_down_cut_rod_recursive(n - i      ,    a__      ,    a__						)      ,    )

          __SCREAMING_SNAKE_CASE     =      max_revenue

     return max_rev[n]







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     _enforce_args(a__      ,    a__						)

     # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
     # length 0.
     __SCREAMING_SNAKE_CASE     =      [float("""-inf"""						) for _ in range(n + 1						)]
     __SCREAMING_SNAKE_CASE     =      0

     for i in range(1      ,    n + 1						):
          __SCREAMING_SNAKE_CASE     =      max_rev[i]
          for j in range(1      ,    i + 1						):
               __SCREAMING_SNAKE_CASE     =      max(a__      ,    prices[j - 1] + max_rev[i - j]						)

          __SCREAMING_SNAKE_CASE     =      max_revenue_i

     return max_rev[n]







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     if n < 0:
          __SCREAMING_SNAKE_CASE     =      F'n must be greater than or equal to 0. Got n = {n}'
          raise ValueError(a__						)

     if n > len(a__						):
          __SCREAMING_SNAKE_CASE     =      (
              """Each integral piece of rod must have a corresponding price. """
              F'Got n = {n} but length of prices = {len(a__						)}'
          )
          raise ValueError(a__						)







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [6, 10, 12, 15, 20, 23]
     __SCREAMING_SNAKE_CASE     =      len(a__						)

     # the best revenue comes from cutting the rod into 6 pieces, each
     # of length 1 resulting in a revenue of 6 * 6 = 36.
     __SCREAMING_SNAKE_CASE     =      36

     __SCREAMING_SNAKE_CASE     =      top_down_cut_rod(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      bottom_up_cut_rod(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      naive_cut_rod_recursive(a__      ,    a__						)

     assert expected_max_revenue == max_rev_top_down
     assert max_rev_top_down == max_rev_bottom_up
     assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()






