

'''simple docstring'''






import matplotlib.pyplot as plt
import numpy as np

def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     float   )							->       Optional[int]:
      """simple docstring"""

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

      return weights

def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     float   )							->       Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      str					=	weight_matrix(lowercase__  ,  lowercase__  ,  lowercase__   )
      SCREAMING_SNAKE_CASE_				:      str					=	np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
          x_train.T @ weight_mat @ y_train.T
      )

      return weight

def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     float   )							->       Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	np.zeros(len(lowercase__   )   )  # Initialize array of predictions
      for i, item in enumerate(lowercase__   ):
            SCREAMING_SNAKE_CASE_				:      Dict					=	item @ local_weight(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__   )

      return y_pred

def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     str   )							->       Union[str, Any]:
      """simple docstring"""

      import seaborn as sns

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	sns.load_dataset(lowercase__   )
      SCREAMING_SNAKE_CASE_				:      Any					=	np.array(data[x_name]   )
      SCREAMING_SNAKE_CASE_				:      str					=	np.array(data[y_name]   )

      SCREAMING_SNAKE_CASE_				:      Any					=	np.ones(len(lowercase__   )   )

      # pairing elements of one and x_data
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	np.column_stack((one, x_data)   )

      return x_train, x_data, y_data

def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     np.ndarray  ,  SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     str  ,  )							->       Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	np.sort(lowercase__  ,  axis=0   )
      plt.scatter(lowercase__  ,  lowercase__  ,  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(lowercase__   )
      plt.ylabel(lowercase__   )
      plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      snake_case_  ,     snake_case_  ,     snake_case_					  =	load_data('tips', 'total_bill', 'tip')
      snake_case_					  =	local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')




'''simple docstring'''






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int  ,  **SCREAMING_SNAKE_CASE_					:     int   )							->       str:
      """simple docstring"""

      return RunBeamCommand(
          args.dataset  ,  args.name  ,  args.cache_dir  ,  args.beam_pipeline_options  ,  args.data_dir  ,  args.all_configs  ,  args.save_info or args.save_infos  ,  args.ignore_verifications  ,  args.force_redownload  ,  **SCREAMING_SNAKE_CASE_  ,  )





class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):




    @staticmethod
    def   __lowerCamelCase				( lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	parser.add_parser("run_beam"    ,					help="Run a Beam dataset processing pipeline"      )
          run_beam_parser.add_argument("dataset"    ,					type=lowercase__    ,					help="Name of the dataset to download"      )
          run_beam_parser.add_argument("--name"    ,					type=lowercase__    ,					default=lowercase__    ,					help="Dataset config name"      )
          run_beam_parser.add_argument(
              "--cache_dir"    ,					type=lowercase__    ,					default=lowercase__    ,					help="Cache directory where the datasets are stored"    ,					)
          run_beam_parser.add_argument(
              "--beam_pipeline_options"    ,					type=lowercase__    ,					default=""    ,					help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"    ,					)
          run_beam_parser.add_argument(
              "--data_dir"    ,					type=lowercase__    ,					default=lowercase__    ,					help="Can be used to specify a manual directory to get the files from"    ,					)
          run_beam_parser.add_argument("--all_configs"    ,					action="store_true"    ,					help="Test all dataset configurations"      )
          run_beam_parser.add_argument("--save_info"    ,					action="store_true"    ,					help="Save the dataset infos file"      )
          run_beam_parser.add_argument(
              "--ignore_verifications"    ,					action="store_true"    ,					help="Run the test without checksums and splits checks"      )
          run_beam_parser.add_argument("--force_redownload"    ,					action="store_true"    ,					help="Force dataset redownload"      )
          # aliases
          run_beam_parser.add_argument("--save_infos"    ,					action="store_true"    ,					help="alias for save_info"      )
          run_beam_parser.set_defaults(func=lowercase__      )




    def __init__( self    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					**lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	dataset
          SCREAMING_SNAKE_CASE_				:      Dict					=	name
          SCREAMING_SNAKE_CASE_				:      str					=	cache_dir
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	beam_pipeline_options
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	data_dir
          SCREAMING_SNAKE_CASE_				:      Dict					=	all_configs
          SCREAMING_SNAKE_CASE_				:      List[str]					=	save_infos
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ignore_verifications
          SCREAMING_SNAKE_CASE_				:      int					=	force_redownload
          SCREAMING_SNAKE_CASE_				:      Tuple					=	config_kwargs







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          import apache_beam as beam

          if self._name is not None and self._all_configs:
                print("Both parameters `name` and `all_configs` can't be used at once."      )
                exit(1      )
          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Any					=	self._dataset, self._name
          SCREAMING_SNAKE_CASE_				:      List[str]					=	dataset_module_factory(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	import_main_class(dataset_module.module_path      )
          SCREAMING_SNAKE_CASE_				:      List[DatasetBuilder]					=	[]
          if self._beam_pipeline_options:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	beam.options.pipeline_options.PipelineOptions(
                    flags=[F"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","      ) if opt]      )
          else:
                SCREAMING_SNAKE_CASE_				:      Dict					=	None
          if self._all_configs and len(builder_cls.BUILDER_CONFIGS      ) > 0:
                for builder_config in builder_cls.BUILDER_CONFIGS:
                      builders.append(
                          builder_cls(
                              config_name=builder_config.name    ,					data_dir=self._data_dir    ,					hash=dataset_module.hash    ,					beam_options=lowercase__    ,					cache_dir=self._cache_dir    ,					base_path=dataset_module.builder_kwargs.get("base_path"      )    ,					)      )
          else:
                builders.append(
                    builder_cls(
                        config_name=lowercase__    ,					data_dir=self._data_dir    ,					beam_options=lowercase__    ,					cache_dir=self._cache_dir    ,					base_path=dataset_module.builder_kwargs.get("base_path"      )    ,					**self._config_kwargs    ,					)      )

          for builder in builders:
                builder.download_and_prepare(
                    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                    if not self._force_redownload
                    else DownloadMode.FORCE_REDOWNLOAD    ,					download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH      )    ,					verification_mode=VerificationMode.NO_CHECKS
                    if self._ignore_verifications
                    else VerificationMode.ALL_CHECKS    ,					try_from_hf_gcs=lowercase__    ,					)
                if self._save_infos:
                      builder._save_infos()

          print("Apache beam run successful."      )

          # If save_infos=True, the dataset infos file is created next to the loaded module file.
          # Let's move it to the original directory of the dataset script, to allow the user to
          # upload them on S3 at the same time afterwards.
          if self._save_infos:
                SCREAMING_SNAKE_CASE_				:      Dict					=	os.path.join(builder_cls.get_imported_module_dir()    ,					config.DATASETDICT_INFOS_FILENAME      )

                SCREAMING_SNAKE_CASE_				:      List[Any]					=	Path(lowercase__      ).name + ".py"

                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	os.path.join(lowercase__    ,					lowercase__      )
                if os.path.isfile(lowercase__      ):
                      SCREAMING_SNAKE_CASE_				:      Any					=	os.path.dirname(lowercase__      )
                elif os.path.isfile(lowercase__      ):
                      SCREAMING_SNAKE_CASE_				:      List[str]					=	path
                else:  # in case of a remote dataset
                      print(F"Dataset Infos file saved at {dataset_infos_path}"      )
                      exit(1      )

                # Move datasetinfo back to the user
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	os.path.join(lowercase__    ,					config.DATASETDICT_INFOS_FILENAME      )
                copyfile(lowercase__    ,					lowercase__      )
                print(F"Dataset Infos file saved at {user_dataset_infos_path}"      )

