



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase					:				Optional[Any]   	={
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				int   	=['LlamaTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				Tuple   	=['LlamaTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				int   	=[
		    'OpenLlamaForCausalLM',
		    'OpenLlamaModel',
		    'OpenLlamaPreTrainedModel',
		    'OpenLlamaForSequenceClassification',
		]


if TYPE_CHECKING:
		from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from transformers import LlamaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from transformers import LlamaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_open_llama import (
				    OpenLlamaForCausalLM,
				    OpenLlamaForSequenceClassification,
				    OpenLlamaModel,
				    OpenLlamaPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase					:				Dict   	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import numpy as np






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									if x.ndim != 1:
															print("""Error: Input data set must be one dimensional""" )
															return
									if len(lowercase_ ) != len(lowercase_ ):
															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(lowercase_ ) < 2 * self.min_leaf_size:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									lowerCAmelCase	:   List[str]					     =					0
									lowerCAmelCase	:   List[Any]					     =					self.mean_squared_error(lowercase_    ,  np.mean(lowercase_ ) ) * 2
									for i in range(len(lowercase_ ) ):
															if len(x[:i] ) < self.min_leaf_size:
																					continue
															elif len(x[i:] ) < self.min_leaf_size:
																					continue
															else:
																					lowerCAmelCase	:   int					     =					self.mean_squared_error(x[:i]    ,  np.mean(y[:i] ) )
																					lowerCAmelCase	:   Union[str, Any]					     =					self.mean_squared_error(x[i:]    ,  np.mean(y[i:] ) )
																					lowerCAmelCase	:   Tuple					     =					error_left + error_right
																					if error < min_error:
																											lowerCAmelCase	:   Tuple					     =					i
																											lowerCAmelCase	:   Tuple					     =					error

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

															lowerCAmelCase	:   Any					     =					x[best_split]
															lowerCAmelCase	:   str					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															lowerCAmelCase	:   List[str]					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															self.left.train(lowercase_    ,  lowercase_ )
															self.right.train(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   List[str]					     =					np.mean(lowercase_ )

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									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(lowercase_ )
															else:
																					return self.left.predict(lowercase_ )
									else:
															print("""Error: Decision tree not yet trained""" )
															return None






class 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

						print("""Test values: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Predictions: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Average error: """ + str(SCREAMING_SNAKE_CASE__  )  )


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

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

