


'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Tuple:
    UpperCAmelCase__     :						Tuple						=							1.5
    UpperCAmelCase__     :						Union[str, Any]						=							int(factor * num_class_images		)
    UpperCAmelCase__     :						List[Any]						=							ClipClient(
        url='''https://knn.laion.ai/knn-service'''    ,   indice_name='''laion_400m'''    ,   num_images=snake_case__    ,   aesthetic_weight=0.1		)

    os.makedirs(F"""{class_data_dir}/images"""    ,   exist_ok=snake_case__		)
    if len(list(Path(F"""{class_data_dir}/images"""		).iterdir()		)		) >= num_class_images:
        return

    while True:
        UpperCAmelCase__     :						Dict						=							client.query(text=snake_case__		)
        if len(snake_case__		) >= factor * num_class_images or num_images > 1E4:
            break
        else:
            UpperCAmelCase__     :						Any						=							int(factor * num_images		)
            UpperCAmelCase__     :						str						=							ClipClient(
                url='''https://knn.laion.ai/knn-service'''    ,   indice_name='''laion_400m'''    ,   num_images=snake_case__    ,   aesthetic_weight=0.1    ,   )

    UpperCAmelCase__     :						Optional[int]						=							0
    UpperCAmelCase__     :						Tuple						=							0
    UpperCAmelCase__     :						List[Any]						=							tqdm(desc='''downloading real regularization images'''    ,   total=snake_case__		)

    with open(F"""{class_data_dir}/caption.txt"""    ,   '''w'''		) as fa, open(F"""{class_data_dir}/urls.txt"""    ,   '''w'''		) as fa, open(
        F"""{class_data_dir}/images.txt"""    ,   '''w'''		) as fa:
        while total < num_class_images:
            UpperCAmelCase__     :						Any						=							class_images[count]
            count += 1
            try:
                UpperCAmelCase__     :						Dict						=							requests.get(images['''url''']		)
                if img.status_code == 2_00:
                    UpperCAmelCase__     :						Tuple						=							Image.open(BytesIO(img.content		)		)
                    with open(F"""{class_data_dir}/images/{total}.jpg"""    ,   '''wb'''		) as f:
                        f.write(img.content		)
                    fa.write(images['''caption'''] + '''\n'''		)
                    fa.write(images['''url'''] + '''\n'''		)
                    fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n'''		)
                    total += 1
                    pbar.update(1		)
                else:
                    continue
            except Exception:
                continue
    return





def    a__			(   )       ->      List[str]:
    UpperCAmelCase__     :						List[str]						=							argparse.ArgumentParser(''''''    ,   add_help=snake_case__		)
    parser.add_argument('''--class_prompt'''    ,   help='''text prompt to retrieve images'''    ,   required=snake_case__    ,   type=snake_case__		)
    parser.add_argument('''--class_data_dir'''    ,   help='''path to save images'''    ,   required=snake_case__    ,   type=snake_case__		)
    parser.add_argument('''--num_class_images'''    ,   help='''number of images to download'''    ,   default=2_00    ,   type=snake_case__		)
    return parser.parse_args()


if __name__ == "__main__":
  UpperCamelCase__          = parse_args()
  retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


'''simple docstring'''




import numpy

# List of input, output pairs
lowercase     :							Any       						=     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase     :							str       						=     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowercase     :							Union[str, Any]       						=     [2, 4, 1, 5]
lowercase     :							Any       						=     len(train_data)
lowercase     :							Optional[int]       						=     0.0_09







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__="train"		):




    '''simple docstring'''



    return calculate_hypothesis_value(snake_case__						,							snake_case__		) - output(
        snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    for i in range(len(snake_case__		) - 1		):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0]		)
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0]		)
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__=m		):




    '''simple docstring'''



    A			:  List[Any]   =		0
    for i in range(snake_case__		):
        if index == -1:
            summation_value += _error(snake_case__		)
        else:
            summation_value += _error(snake_case__		) * train_data[i][0][index]
    return summation_value







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		summation_of_cost_derivative(snake_case__						,							snake_case__		) / m
    return cost_derivative_value







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A			:  Dict   =		0.00_00_02
    A			:  Optional[Any]   =		0
    A			:  int   =		0
    while True:
        j += 1
        A			:  List[str]   =		[0, 0, 0, 0]
        for i in range(0						,							len(snake_case__		)		):
            A			:  Union[str, Any]   =		get_cost_derivative(i - 1		)
            A			:  Tuple   =		(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            snake_case__						,							snake_case__						,							atol=snake_case__						,							rtol=snake_case__						,							):
            break
        A			:  List[Any]   =		temp_parameter_vector
    print(('''Number of iterations:''', j)		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    for i in range(len(snake_case__		)		):
        print(('''Actual output value:''', output(snake_case__						,							'''test'''		))		)
        print(('''Hypothesis output:''', calculate_hypothesis_value(snake_case__						,							'''test'''		))		)


if __name__ == "__main__":
      run_gradient_descent()
      print('\nTesting gradient descent for a linear hypothesis function.\n')
      test_gradient_descent()




