






import numpy as np
import requests






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=requests.get(
       """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
       """master/Week1/ADRvsRating.csv"""    )
   lowerCamelCase									=response.text.splitlines()
   lowerCamelCase									=[]
   for item in lines:
      lowerCamelCase									=item.split(""","""    )
      data.append(lowerCamelCase    )
   data.pop(0    )  # This is for removing the labels from the list
   lowerCamelCase									=np.matrix(lowerCamelCase    )
   return dataset






def  UpperCAmelCase__						(    lowerCamelCase	:			int			,lowerCamelCase	:			List[str]			,lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			int			,lowerCamelCase	:			Union[str, Any]    ):
   lowerCamelCase									=len_data

   lowerCamelCase									=np.dot(lowerCamelCase			,data_x.transpose()    )
   prod -= data_y.transpose()
   lowerCamelCase									=np.dot(lowerCamelCase			,lowerCamelCase    )
   lowerCamelCase									=theta - (alpha / n) * sum_grad
   return theta






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			int			,lowerCamelCase	:			Dict			,lowerCamelCase	:			Optional[Any]    ):
   lowerCamelCase									=np.dot(lowerCamelCase			,data_x.transpose()    )
   prod -= data_y.transpose()
   lowerCamelCase									=np.sum(np.square(lowerCamelCase    )    )
   lowerCamelCase									=sum_elem / (2 * len_data)
   return error






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			int    ):
   lowerCamelCase									=10_00_00
   lowerCamelCase									=0.0001550

   lowerCamelCase									=data_x.shape[1]
   lowerCamelCase									=data_x.shape[0] - 1

   lowerCamelCase									=np.zeros((1, no_features)    )

   for i in range(0			,lowerCamelCase    ):
      lowerCamelCase									=run_steep_gradient_descent(lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )
      lowerCamelCase									=sum_of_square_error(lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )
      print(f'''At Iteration {i + 1} - Error is {error:.5f}'''    )

   return theta






def  UpperCAmelCase__						(    lowerCamelCase	:			Any			,lowerCamelCase	:			Optional[int]    ):
   lowerCamelCase									=sum(abs(y - predicted_y[i]    ) for i, y in enumerate(lowerCamelCase    )    )
   return total / len(lowerCamelCase    )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=collect_dataset()

   lowerCamelCase									=data.shape[0]
   lowerCamelCase									=np.c_[np.ones(lowerCamelCase    ), data[:, :-1]].astype(lowerCamelCase    )
   lowerCamelCase									=data[:, -1].astype(lowerCamelCase    )

   lowerCamelCase									=run_linear_regression(lowerCamelCase			,lowerCamelCase    )
   lowerCamelCase									=theta.shape[1]
   print("""Resultant Feature vector : """    )
   for i in range(0			,lowerCamelCase    ):
      print(f'''{theta[0, i]:.5f}'''    )


if __name__ == "__main__":
   main()







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=None							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=[0.48145466, 0.4578275, 0.40821073]							, _UpperCAmelCase=[0.26862954, 0.26130258, 0.27577711]							, _UpperCAmelCase=True							, )		->				List[str]:
        lowerCamelCase									=size if size is not None else {"""height""": 20, """width""": 20}
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_resize
        lowerCamelCase									=size
        lowerCamelCase									=do_normalize
        lowerCamelCase									=image_mean
        lowerCamelCase									=image_std
        lowerCamelCase									=do_pad
        lowerCamelCase									=do_convert_rgb





     def 						lowerCamelCase							(	self  )		->				str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				Optional[int]:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    str 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=BlipImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        pass





     def 						lowerCamelCase							(	self  )		->				int:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Any 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=BlipImageProcessingTester(self							, num_channels=4  )
        lowerCamelCase									=3





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				List[str]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        pass





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )