from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


__lowerCamelCase    :				Union[str, Any]							     =	logging.get_logger(__name__)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: str			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   b.T
  SCREAMING_SNAKE_CASE__    							=   np.sum(np.square(__UpperCamelCase			)				,  axis=1			)
  SCREAMING_SNAKE_CASE__    							=   np.sum(np.square(__UpperCamelCase			)				,  axis=0			)
  SCREAMING_SNAKE_CASE__    							=   np.matmul(__UpperCamelCase				,  __UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   aa[:, None] - 2 * ab + ba[None, :]
  return d



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: str			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   x.reshape(-1				,  3			)
  SCREAMING_SNAKE_CASE__    							=   squared_euclidean_distance(__UpperCamelCase				,  __UpperCamelCase			)
  return np.argmin(__UpperCamelCase				,  axis=1			)







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(					self    :       List[str] ,			_lowercase    :       Optional[Union[List[List[int]], np.ndarray]] = None ,			_lowercase    :       bool = True ,			_lowercase    :       Dict[str, int] = None ,			_lowercase    :       PILImageResampling = PILImageResampling.BILINEAR ,			_lowercase    :       bool = True ,			_lowercase    :       bool = True ,			**_lowercase    :       List[Any] ,			):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   size if size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__    							=   get_size_dict(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   np.array(_lowercase   ) if clusters is not None else None
     SCREAMING_SNAKE_CASE__    							=   do_resize
     SCREAMING_SNAKE_CASE__    							=   size
     SCREAMING_SNAKE_CASE__    							=   resample
     SCREAMING_SNAKE_CASE__    							=   do_normalize
     SCREAMING_SNAKE_CASE__    							=   do_color_quantize




   def 			__a		(					self    :       int ,			_lowercase    :       np.ndarray ,			_lowercase    :       Dict[str, int] ,			_lowercase    :       PILImageResampling = PILImageResampling.BILINEAR ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = None ,			**_lowercase    :       str ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   get_size_dict(_lowercase   )
     if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""   )
     return resize(
         _lowercase ,			size=(size["""height"""], size["""width"""]) ,			resample=_lowercase ,			data_format=_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       np.ndarray ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = None ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   rescale(image=_lowercase ,			scale=1 / 1_27.5 ,			data_format=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   image - 1
     return image




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       ImageInput ,			_lowercase    :       bool = None ,			_lowercase    :       Dict[str, int] = None ,			_lowercase    :       PILImageResampling = None ,			_lowercase    :       bool = None ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       Optional[Union[List[List[int]], np.ndarray]] = None ,			_lowercase    :       Optional[Union[str, TensorType]] = None ,			_lowercase    :       Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,			**_lowercase    :       List[str] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__    							=   size if size is not None else self.size
     SCREAMING_SNAKE_CASE__    							=   get_size_dict(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__    							=   do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__    							=   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
     SCREAMING_SNAKE_CASE__    							=   clusters if clusters is not None else self.clusters
     SCREAMING_SNAKE_CASE__    							=   np.array(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   make_list_of_images(_lowercase   )

     if not valid_images(_lowercase   ):
       raise ValueError(
           """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
           """torch.Tensor, tf.Tensor or jax.ndarray."""   )

     if do_resize and size is None or resample is None:
       raise ValueError("""Size and resample must be specified if do_resize is True."""   )

     if do_color_quantize and clusters is None:
       raise ValueError("""Clusters must be specified if do_color_quantize is True."""   )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__    							=   [to_numpy_array(_lowercase   ) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__    							=   [self.resize(image=_lowercase ,			size=_lowercase ,			resample=_lowercase   ) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE__    							=   [self.normalize(image=_lowercase   ) for image in images]

     if do_color_quantize:
       SCREAMING_SNAKE_CASE__    							=   [to_channel_dimension_format(_lowercase ,			ChannelDimension.LAST   ) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       SCREAMING_SNAKE_CASE__    							=   np.array(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   color_quantize(_lowercase ,			_lowercase   ).reshape(images.shape[:-1]   )

       # flatten to (batch_size, height*width)
       SCREAMING_SNAKE_CASE__    							=   images.shape[0]
       SCREAMING_SNAKE_CASE__    							=   images.reshape(_lowercase ,			-1   )

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       SCREAMING_SNAKE_CASE__    							=   list(_lowercase   )
     else:
       SCREAMING_SNAKE_CASE__    							=   [to_channel_dimension_format(_lowercase ,			_lowercase   ) for image in images]

     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": images}
     return BatchFeature(data=_lowercase ,			tensor_type=_lowercase   )

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
   __lowerCamelCase    :				List[Any]							     =	datasets.load_iris()
   __lowerCamelCase    :				List[Any]							     =	iris.data[:, :2]
   __lowerCamelCase    :				Dict							     =	(iris.target != 0) * 1

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(10, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
