


import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch



class 					__lowerCamelCase	(unittest.TestCase		):







       @patch('sys.argv',['fakeprogrampath', 'env']			)
       def 						snake_case_					(	self:				Tuple			):






            '''simple docstring'''




            import transformers.commands.transformers_cli

            with CaptureStd() as cs:
                 transformers.commands.transformers_cli.main()
            self.assertIn('Python version',cs.out			)
            self.assertIn('Platform',cs.out			)
            self.assertIn('Using distributed or parallel set-up in script?',cs.out			)







       @is_pt_tf_cross_test
       @patch(
           'sys.argv',['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']			)
       def 						snake_case_					(	self:				List[Any]			):






            '''simple docstring'''




            import transformers.commands.transformers_cli

            shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj',ignore_errors=A_			)  # cleans potential past runs
            transformers.commands.transformers_cli.main()

            # The original repo has no TF weights -- if they exist, they were created by the CLI
            self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'			)			)







       @require_torch
       @patch('sys.argv',['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']			)
       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj',ignore_errors=A_			)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'			)			)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'			)			)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'			)			)







       @require_torch
       @patch(
           'sys.argv',[
               'fakeprogrampath',
               'download',
               'hf-internal-testing/test_dynamic_model_with_tokenizer',
               '--trust-remote-code',
               '--cache-dir',
               '/tmp',
           ],)
       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer',ignore_errors=A_			)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'			)			)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'			)			)
            self.assertTrue(
                os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'			)			)






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

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

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	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''')


