


'''simple docstring'''

import matplotlib.pyplot as plt
import numpy as np



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







   UpperCamelCase__			    =    len(_UpperCamelCase )  # Number of training samples
   UpperCamelCase__			    =    np.eye(_UpperCamelCase )  # Initialize weights as identity matrix
   for j in range(_UpperCamelCase ):
      UpperCamelCase__			    =    point - x_train[j]
      UpperCamelCase__			    =    np.exp(diff @ diff.T / (-2.0 * tau**2) )

   return weights



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







   UpperCamelCase__			    =    weight_matrix(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
   UpperCamelCase__			    =    np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					float )		->       np.ndarray:





   '''simple docstring'''







   UpperCamelCase__			    =    np.zeros(len(_UpperCamelCase ) )  # Initialize array of predictions
   for i, item in enumerate(_UpperCamelCase ):
      UpperCamelCase__			    =    item @ local_weight(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )

   return y_pred



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str )		->       tuple[np.ndarray, np.ndarray, np.ndarray]:





   '''simple docstring'''







   import seaborn as sns

   UpperCamelCase__			    =    sns.load_dataset(_UpperCamelCase )
   UpperCamelCase__			    =    np.array(data[x_name] )
   UpperCamelCase__			    =    np.array(data[y_name] )

   UpperCamelCase__			    =    np.ones(len(_UpperCamelCase ) )

   # pairing elements of one and x_data
   UpperCamelCase__			    =    np.column_stack((one, x_data) )

   return x_train, x_data, y_data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					np.ndarray     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					str     ,		)		->       None:





   '''simple docstring'''







   UpperCamelCase__			    =    np.sort(_UpperCamelCase     ,		axis=0 )
   plt.scatter(_UpperCamelCase     ,		_UpperCamelCase     ,		color="blue" )
   plt.plot(
       x_train_sorted[:, 1]     ,		preds[x_train[:, 1].argsort(0 )]     ,		color="yellow"     ,		linewidth=5     ,		)
   plt.title("Local Weighted Regression" )
   plt.xlabel(_UpperCamelCase )
   plt.ylabel(_UpperCamelCase )
   plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # Demo with a dataset from the seaborn module
       __lowercase						,__lowercase						,__lowercase:		Dict    						=  load_data("tips", "total_bill", "tip")
       __lowercase:		Optional[int]    						=  local_weight_regression(training_data_x, tip, 5)
       plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")






'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["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__)




