


'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase     :Optional[int]												=  logging.get_logger(__name__)

__UpperCAmelCase     :List[Any]												=  {
    'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}



class       a							(						_UpperCamelCase			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					List[Any]				  =  'musicgen_decoder'
     SCREAMING_SNAKE_CASE			:					str				  =  ['past_key_values']

     def __init__(							self	:						Dict				,					snake_case	:						Optional[int]=2048				,					snake_case	:						Union[str, Any]=2048				,					snake_case	:						str=24				,					snake_case	:						Optional[Any]=4096				,					snake_case	:						List[str]=16				,					snake_case	:						int=0.0				,					snake_case	:						Tuple=True				,					snake_case	:						Any="gelu"				,					snake_case	:						Union[str, Any]=1024				,					snake_case	:						str=0.1				,					snake_case	:						int=0.0				,					snake_case	:						int=0.0				,					snake_case	:						List[str]=0.02				,					snake_case	:						List[Any]=False				,					snake_case	:						List[Any]=4				,					snake_case	:						Union[str, Any]=2048				,					snake_case	:						Tuple=2048				,					snake_case	:						Tuple=None				,					snake_case	:						List[Any]=False				,					**snake_case	:						str				,					)		->				Tuple:
          __UpperCAmelCase      :					List[str]    				=     vocab_size
          __UpperCAmelCase      :					Optional[Any]    				=     max_position_embeddings
          __UpperCAmelCase      :					str    				=     hidden_size
          __UpperCAmelCase      :					List[Any]    				=     ffn_dim
          __UpperCAmelCase      :					Optional[int]    				=     num_hidden_layers
          __UpperCAmelCase      :					Any    				=     num_attention_heads
          __UpperCAmelCase      :					int    				=     dropout
          __UpperCAmelCase      :					int    				=     attention_dropout
          __UpperCAmelCase      :					Union[str, Any]    				=     activation_dropout
          __UpperCAmelCase      :					Dict    				=     activation_function
          __UpperCAmelCase      :					Tuple    				=     initializer_factor
          __UpperCAmelCase      :					Tuple    				=     layerdrop
          __UpperCAmelCase      :					List[str]    				=     use_cache
          __UpperCAmelCase      :					Dict    				=     scale_embedding  # scale factor will be sqrt(d_model) if True
          __UpperCAmelCase      :					Optional[Any]    				=     num_codebooks
          super().__init__(
              pad_token_id=_SCREAMING_SNAKE_CASE				,					bos_token_id=_SCREAMING_SNAKE_CASE				,					eos_token_id=_SCREAMING_SNAKE_CASE				,					tie_word_embeddings=_SCREAMING_SNAKE_CASE				,					**_SCREAMING_SNAKE_CASE				,					)



class       a							(						_UpperCamelCase			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str				  =  'musicgen'
     SCREAMING_SNAKE_CASE			:					Union[str, Any]				  =  True

     def __init__(							self	:						int				,					**snake_case	:						Optional[int]     )		->				List[Any]:
          super().__init__(**_SCREAMING_SNAKE_CASE     )
          if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
               raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''     )

          __UpperCAmelCase      :					List[str]    				=     kwargs.pop('''text_encoder'''     )
          __UpperCAmelCase      :					Any    				=     text_encoder_config.pop('''model_type'''     )

          __UpperCAmelCase      :					Tuple    				=     kwargs.pop('''audio_encoder'''     )
          __UpperCAmelCase      :					Tuple    				=     audio_encoder_config.pop('''model_type'''     )

          __UpperCAmelCase      :					str    				=     kwargs.pop('''decoder'''     )

          __UpperCAmelCase      :					Union[str, Any]    				=     AutoConfig.for_model(_SCREAMING_SNAKE_CASE				,					**_SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Tuple    				=     AutoConfig.for_model(_SCREAMING_SNAKE_CASE				,					**_SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					List[Any]    				=     MusicgenDecoderConfig(**_SCREAMING_SNAKE_CASE     )
          __UpperCAmelCase      :					Dict    				=     True

     @classmethod
     def 					lowerCamelCase__					(							cls	:						List[str]				,					snake_case	:						Any				,					snake_case	:						int				,					snake_case	:						List[Any]				,					**snake_case	:						Tuple				,					)		->				Any:
          return cls(
              text_encoder=text_encoder_config.to_dict()				,					audio_encoder=audio_encoder_config.to_dict()				,					decoder=decoder_config.to_dict()				,					**_SCREAMING_SNAKE_CASE				,					)






     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				Tuple:
          __UpperCAmelCase      :					str    				=     copy.deepcopy(self.__dict__     )
          __UpperCAmelCase      :					int    				=     self.text_encoder.to_dict()
          __UpperCAmelCase      :					Union[str, Any]    				=     self.audio_encoder.to_dict()
          __UpperCAmelCase      :					List[Any]    				=     self.decoder.to_dict()
          __UpperCAmelCase      :					List[str]    				=     self.__class__.model_type
          return output
'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  int(''''''.join(c for c in ordinal if c in '''0123456789'''  )  )  # 1000th --> 1000
        print(
            F'''{ordinal} fibonacci number using matrix exponentiation is '''
            F'''{nth_fibonacci_matrix(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\n'''  )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






