






"""simple docstring"""


import numpy as np


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Any,			_lowerCamelCase					:	int=5,			_lowerCamelCase					:	Dict=5    ):

     '''simple docstring'''
     __A		     =					depth
     __A		     =					0
     __A		     =					None
     __A		     =					None
     __A		     =					min_leaf_size
     __A		     =					None


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if labels.ndim != 1:
       print('''Error: Input labels must be one dimensional'''    )

     return np.mean((labels - prediction) ** 2    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	int,			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if x.ndim != 1:
       print('''Error: Input data set must be one dimensional'''    )
       return
     if len(SCREAMING_SNAKE_CASE_    ) != len(SCREAMING_SNAKE_CASE_    ):
       print('''Error: X and y have different lengths'''    )
       return
     if y.ndim != 1:
       print('''Error: Data set labels must be one dimensional'''    )
       return

     if len(SCREAMING_SNAKE_CASE_    ) < 2 * self.min_leaf_size:
       __A		     =					np.mean(SCREAMING_SNAKE_CASE_    )
       return

     if self.depth == 1:
       __A		     =					np.mean(SCREAMING_SNAKE_CASE_    )
       return

     __A		     =					0
     __A		     =					self.mean_squared_error(SCREAMING_SNAKE_CASE_,			np.mean(SCREAMING_SNAKE_CASE_    )    ) * 2
     for i in range(len(SCREAMING_SNAKE_CASE_    )    ):
       if len(x[:i]    ) < self.min_leaf_size:
         continue
       elif len(x[i:]    ) < self.min_leaf_size:
         continue
       else:
         __A		     =					self.mean_squared_error(x[:i],			np.mean(y[:i]    )    )
         __A		     =					self.mean_squared_error(x[i:],			np.mean(y[i:]    )    )
         __A		     =					error_left + error_right
         if error < min_error:
           __A		     =					i
           __A		     =					error

     if best_split != 0:
       __A		     =					x[:best_split]
       __A		     =					y[:best_split]
       __A		     =					x[best_split:]
       __A		     =					y[best_split:]

       __A		     =					x[best_split]
       __A		     =					DecisionTree(
           depth=self.depth - 1,			min_leaf_size=self.min_leaf_size    )
       __A		     =					DecisionTree(
           depth=self.depth - 1,			min_leaf_size=self.min_leaf_size    )
       self.left.train(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    )
       self.right.train(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    )
     else:
       __A		     =					np.mean(SCREAMING_SNAKE_CASE_    )

     return






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     if self.prediction is not None:
       return self.prediction
     elif self.left or self.right is not None:
       if x >= self.decision_boundary:
         return self.right.predict(SCREAMING_SNAKE_CASE_    )
       else:
         return self.left.predict(SCREAMING_SNAKE_CASE_    )
     else:
       print('''Error: Decision tree not yet trained'''    )
       return None





class    snake_case						:


   '''simple docstring'''

   @staticmethod
   def 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	Tuple,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     __A		     =					float(0    )
     for label in labels:
       squared_error_sum += (label - prediction) ** 2

     return float(squared_error_sum / labels.size    )





def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					np.arange(-1.0						,			1.0						,			0.005					)
  __A		     =					np.sin(A_					)

  __A		     =					DecisionTree(depth=1_0						,			min_leaf_size=1_0					)
  tree.train(A_						,			A_					)

  __A		     =					(np.random.rand(1_0					) * 2) - 1
  __A		     =					np.array([tree.predict(A_					) for x in test_cases]					)
  __A		     =					np.mean((predictions - test_cases) ** 2					)

  print('''Test values: ''' + str(A_					)					)
  print('''Predictions: ''' + str(A_					)					)
  print('''Average error: ''' + str(A_					)					)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod(name='mean_squarred_error', verbose=True)






'''simple docstring'''





import copy

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


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "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 (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     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 (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										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'''   )

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

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[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 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     copy.deepcopy(self.__dict__   )
										snake_case							:  Tuple			=     self.text_encoder.to_dict()
										snake_case							:  str			=     self.audio_encoder.to_dict()
										snake_case							:  Dict			=     self.decoder.to_dict()
										snake_case							:  Optional[int]			=     self.__class__.model_type
										return output

