
"""simple docstring"""



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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__          = logging.get_logger(__name__)


if is_vision_available():
 import PIL

class       lowerCAmelCase__				(						A_	):
     __a						=					["""pixel_values"""]







     def __init__(							self			:			List[str]    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Dict[str, int] = None    ,      _lowerCamelCase			:			PILImageResampling = PILImageResampling.BICUBIC    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Dict[str, int] = None    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Union[int, float] = 1 / 255    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			bool = True    ,      **_lowerCamelCase			:			Optional[Any]    ,      ):
       super().__init__(**_lowerCamelCase			)
       _snake_case					     =       size if size is not None else {'''shortest_edge''': 224}
       _snake_case					     =       get_size_dict(_lowerCamelCase    ,      default_to_square=_lowerCamelCase			)
       _snake_case					     =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       _snake_case					     =       get_size_dict(_lowerCamelCase    ,      default_to_square=_lowerCamelCase    ,      param_name='''crop_size'''			)

       _snake_case					     =       do_resize
       _snake_case					     =       size
       _snake_case					     =       resample
       _snake_case					     =       do_center_crop
       _snake_case					     =       crop_size
       _snake_case					     =       do_rescale
       _snake_case					     =       rescale_factor
       _snake_case					     =       do_normalize
       _snake_case					     =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case					     =       image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case					     =       do_convert_rgb







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Dict[str, int]    ,      _lowerCamelCase			:			PILImageResampling = PILImageResampling.BICUBIC    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			List[str]    ,      ):
       _snake_case					     =       get_size_dict(_lowerCamelCase    ,      default_to_square=_lowerCamelCase			)
       if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
       _snake_case					     =       get_resize_output_image_size(_lowerCamelCase    ,      size=size['''shortest_edge''']    ,      default_to_square=_lowerCamelCase			)
       return resize(_lowerCamelCase    ,      size=_lowerCamelCase    ,      resample=_lowerCamelCase    ,      data_format=_lowerCamelCase    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Dict[str, int]    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			List[str]    ,      ):
       _snake_case					     =       get_size_dict(_lowerCamelCase			)
       if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''			)
       return center_crop(_lowerCamelCase    ,      size=(size['''height'''], size['''width'''])    ,      data_format=_lowerCamelCase    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Union[int, float]    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			Any    ,      ):
       return rescale(_lowerCamelCase    ,      scale=_lowerCamelCase    ,      data_format=_lowerCamelCase    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			np.ndarray    ,      _lowerCamelCase			:			Union[float, List[float]]    ,      _lowerCamelCase			:			Union[float, List[float]]    ,      _lowerCamelCase			:			Optional[Union[str, ChannelDimension]] = None    ,      **_lowerCamelCase			:			Any    ,      ):
       return normalize(_lowerCamelCase    ,      mean=_lowerCamelCase    ,      std=_lowerCamelCase    ,      data_format=_lowerCamelCase    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			ImageInput    ,      _lowerCamelCase			:			bool = None    ,      _lowerCamelCase			:			Dict[str, int] = None    ,      _lowerCamelCase			:			PILImageResampling = None    ,      _lowerCamelCase			:			bool = None    ,      _lowerCamelCase			:			int = None    ,      _lowerCamelCase			:			bool = None    ,      _lowerCamelCase			:			float = None    ,      _lowerCamelCase			:			bool = None    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			Optional[Union[float, List[float]]] = None    ,      _lowerCamelCase			:			bool = None    ,      _lowerCamelCase			:			Optional[Union[str, TensorType]] = None    ,      _lowerCamelCase			:			Optional[ChannelDimension] = ChannelDimension.FIRST    ,      **_lowerCamelCase			:			Optional[Any]    ,      ):
       _snake_case					     =       do_resize if do_resize is not None else self.do_resize
       _snake_case					     =       size if size is not None else self.size
       _snake_case					     =       get_size_dict(_lowerCamelCase    ,      param_name='''size'''    ,      default_to_square=_lowerCamelCase			)
       _snake_case					     =       resample if resample is not None else self.resample
       _snake_case					     =       do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case					     =       crop_size if crop_size is not None else self.crop_size
       _snake_case					     =       get_size_dict(_lowerCamelCase    ,      param_name='''crop_size'''    ,      default_to_square=_lowerCamelCase			)
       _snake_case					     =       do_rescale if do_rescale is not None else self.do_rescale
       _snake_case					     =       rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case					     =       do_normalize if do_normalize is not None else self.do_normalize
       _snake_case					     =       image_mean if image_mean is not None else self.image_mean
       _snake_case					     =       image_std if image_std is not None else self.image_std
       _snake_case					     =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case					     =       make_list_of_images(_lowerCamelCase			)

       if not valid_images(_lowerCamelCase			):
         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:
         raise ValueError('''Size must be specified if do_resize is True.'''			)

       if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

       if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case					     =       [convert_to_rgb(_lowerCamelCase			) for image in images]

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

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

       if do_center_crop:
         _snake_case					     =       [self.center_crop(image=_lowerCamelCase    ,      size=_lowerCamelCase			) for image in images]

       if do_rescale:
         _snake_case					     =       [self.rescale(image=_lowerCamelCase    ,      scale=_lowerCamelCase			) for image in images]

       if do_normalize:
         _snake_case					     =       [self.normalize(image=_lowerCamelCase    ,      mean=_lowerCamelCase    ,      std=_lowerCamelCase			) for image in images]

       _snake_case					     =       [to_channel_dimension_format(_lowerCamelCase    ,      _lowerCamelCase			) for image in images]

       _snake_case					     =       {'''pixel_values''': images}
       return BatchFeature(data=_lowerCamelCase    ,      tensor_type=_lowerCamelCase			)





"""simple docstring"""



# 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 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[Any]:
  return 1 / (1 + np.exp(-z		))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  return (-y * np.log(__lowerCamelCase		) - (1 - y) * np.log(1 - h		)).mean()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict		)     ->					List[str]:
  _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
  return np.sum(y * scores - np.log(1 + np.exp(__lowerCamelCase		)		)		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str=7_00_00		)     ->					Optional[Any]:
  _snake_case					     =       np.zeros(x.shape[1]		)

  for iterations in range(__lowerCamelCase		):
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       np.dot(x.T ,					h - y		) / y.size
    _snake_case					     =       theta - alpha * gradient  # updating the weights
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       cost_function(__lowerCamelCase ,					__lowerCamelCase		)
    if iterations % 1_00 == 0:
      print(f'''loss: {j} \t'''		)  # printing the loss after every 100 iterations
  return theta


# In[68]:

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

 UpperCAmelCase__          = 0.1
 UpperCAmelCase__          = logistic_reg(alpha, x, y, max_iterations=70000)
 print('theta: ', theta)  # printing the theta i.e our weights vector
 def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
   return sigmoid_function(
       np.dot(__lowerCamelCase ,					__lowerCamelCase		)		)  # 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')
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 0].min(), x[:, 0].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 1].min(), x[:, 1].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 UpperCAmelCase__          = np.c_[xxa.ravel(), xxa.ravel()]
 UpperCAmelCase__          = predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

 plt.legend()

 plt.show()



