


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_SCREAMING_SNAKE_CASE     :					Optional[int]      =       logging.get_logger(__name__)

@add_end_docstrings(_lowercase      )
class     A__   (       _lowercase      ):







    """simple docstring"""

    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         super().__init__(*__SCREAMING_SNAKE_CASE     ,   **__SCREAMING_SNAKE_CASE       )
         requires_backends(self     ,   '''vision'''       )
         self.check_model_type(__SCREAMING_SNAKE_CASE       )
    def __call__(			self     ,   __snake_case     ,   **__snake_case       ):
         return super().__call__(__SCREAMING_SNAKE_CASE     ,   **__SCREAMING_SNAKE_CASE       )
    def 						a_     (			self     ,   **__snake_case       ):
         return {}, {}, {}
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							load_image(__SCREAMING_SNAKE_CASE       )
         snake_case											=							image.size
         snake_case											=							self.image_processor(images=__SCREAMING_SNAKE_CASE     ,   return_tensors=self.framework       )
         return model_inputs
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.model(**__SCREAMING_SNAKE_CASE       )
         return model_outputs




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							model_outputs.predicted_depth
         snake_case											=							torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1       )     ,   size=self.image_size[::-1]     ,   mode='''bicubic'''     ,   align_corners=__SCREAMING_SNAKE_CASE       )
         snake_case											=							prediction.squeeze().cpu().numpy()
         snake_case											=							(output * 2_5_5 / np.max(__SCREAMING_SNAKE_CASE       )).astype('''uint8'''       )
         snake_case											=							Image.fromarray(__SCREAMING_SNAKE_CASE       )
         snake_case											=							{}
         snake_case											=							predicted_depth
         snake_case											=							depth
         return output_dict






"""simple docstring"""




import math







class     lowercase:



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			0.0
         a__		=			0.0
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             da += math.pow((sample[i] - weights[0][i])     ,				2   )
             da += math.pow((sample[i] - weights[1][i])     ,				2   )
             return 0 if da > da else 1
         return 0




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     list[list[int | float]]:







         """simple docstring"""



         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights




def     __magic_name__		(					)     ->						None:
    # Training Examples ( m, n )
    a__		=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    a__		=			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    a__		=			SelfOrganizingMap()
    a__		=			3
    a__		=			0.5

    for _ in range(UpperCamelCase     ):
        for j in range(len(UpperCamelCase     )     ):
            # training sample
            a__		=			training_samples[j]

            # Compute the winning vector
            a__		=			self_organizing_map.get_winner(UpperCamelCase							,		UpperCamelCase     )

            # Update the winning vector
            a__		=			self_organizing_map.update(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # classify test sample
    a__		=			[0, 0, 0, 1]
    a__		=			self_organizing_map.get_winner(UpperCamelCase							,		UpperCamelCase     )

    # results
    print(f'Clusters that the test sample belongs to : {winner}'     )
    print(f'Weights that have been trained : {weights}'     )


# running the main() function
if __name__ == "__main__":
    main()


