





"""simple docstring"""
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 TvltImageProcessor






def  A__    (      UpperCamelCase ,  UpperCamelCase=10 ,  UpperCamelCase=10 ,  UpperCamelCase=False ,  UpperCamelCase=False	):
							A		=  []
							for i in range(image_processor_tester.num_frames	):
														video.append(np.random.randint(255 ,  size=(image_processor_tester.num_channels, width, height) ,  dtype=np.uinta	)	)

							if not numpify and not torchify:
														# PIL expects the channel dimension as last dimension
														A		=  [Image.fromarray(np.moveaxis(UpperCamelCase ,  0 ,  -1	)	) for frame in video]

							if torchify:
														A		=  [torch.from_numpy(UpperCamelCase	) for frame in video]

							return video






def  A__    (      UpperCamelCase ,  UpperCamelCase=False ,  UpperCamelCase=False ,  UpperCamelCase=False	):
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							A		=  []
							for i in range(image_processor_tester.batch_size	):
														if equal_resolution:
																					A		=  A		=  image_processor_tester.max_resolution
														else:
																					A,     A		=  np.random.choice(
																					    np.arange(image_processor_tester.min_resolution ,  image_processor_tester.max_resolution	) ,  2	)
																					A		=  prepare_video(
																					    image_processor_tester=UpperCamelCase ,  width=UpperCamelCase ,  height=UpperCamelCase ,  numpify=UpperCamelCase ,  torchify=UpperCamelCase ,  )
														video_inputs.append(UpperCamelCase	)

							return video_inputs






class  _UpperCAmelCase       (     unittest.TestCase			):


					def __init__( self      :Optional[int]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :List[str]=7					,	__UpperCamelCase      :Any=3					,	__UpperCamelCase      :Union[str, Any]=4					,	__UpperCamelCase      :int=18					,	__UpperCamelCase      :Union[str, Any]=30					,	__UpperCamelCase      :int=4_00					,	__UpperCamelCase      :Dict=True					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=[0.5, 0.5, 0.5]					,	__UpperCamelCase      :List[Any]=[0.5, 0.5, 0.5]					,	__UpperCamelCase      :List[str]=True					,	__UpperCamelCase      :int=None					,	):
												A		=  size if size is not None else {"shortest_edge": 18}
												A		=  crop_size if crop_size is not None else {"height": 18, "width": 18}

												A		=  parent
												A		=  batch_size
												A		=  num_channels
												A		=  num_frames
												A		=  image_size
												A		=  min_resolution
												A		=  max_resolution
												A		=  do_resize
												A		=  size
												A		=  do_normalize
												A		=  image_mean
												A		=  image_std
												A		=  do_center_crop
												A		=  crop_size


					def      lowerCamelCase	( self      :Union[str, Any]		):
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												}







@require_torch
@require_vision
class  _UpperCAmelCase       (     lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							TvltImageProcessor if is_vision_available() else None


					def      lowerCamelCase	( self      :str		):
												A		=  TvltImageProcessorTester(self		)


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


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.image_processing_class(**self.image_processor_dict		)
												self.assertTrue(hasattr(__UpperCamelCase					,	"image_mean"		)		)
												self.assertTrue(hasattr(__UpperCamelCase					,	"image_std"		)		)
												self.assertTrue(hasattr(__UpperCamelCase					,	"do_normalize"		)		)
												self.assertTrue(hasattr(__UpperCamelCase					,	"do_resize"		)		)
												self.assertTrue(hasattr(__UpperCamelCase					,	"do_center_crop"		)		)
												self.assertTrue(hasattr(__UpperCamelCase					,	"size"		)		)


					def      lowerCamelCase	( self      :Dict		):
												# Initialize image_processor
												A		=  self.image_processing_class(**self.image_processor_dict		)
												# create random PIL videos
												A		=  prepare_video_inputs(self.image_processor_tester					,	equal_resolution=__UpperCamelCase		)
												for video in video_inputs:
																			self.assertIsInstance(__UpperCamelCase					,	__UpperCamelCase		)
																			self.assertIsInstance(video[0]					,	Image.Image		)

												# Test not batched input
												A		=  image_processor(video_inputs[0]					,	return_tensors="pt"		).pixel_values
												self.assertEqual(
												    encoded_videos.shape					,	(
												        1,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )					,	)

												# Test batched
												A		=  image_processor(__UpperCamelCase					,	return_tensors="pt"		).pixel_values
												self.assertEqual(
												    encoded_videos.shape					,	(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )					,	)


					def      lowerCamelCase	( self      :List[str]		):
												# Initialize image_processor
												A		=  self.image_processing_class(**self.image_processor_dict		)
												# create random numpy tensors
												A		=  prepare_video_inputs(self.image_processor_tester					,	equal_resolution=__UpperCamelCase					,	numpify=__UpperCamelCase		)
												for video in video_inputs:
																			self.assertIsInstance(__UpperCamelCase					,	__UpperCamelCase		)
																			self.assertIsInstance(video[0]					,	np.ndarray		)

												# Test not batched input
												A		=  image_processor(video_inputs[0]					,	return_tensors="pt"		).pixel_values
												self.assertEqual(
												    encoded_videos.shape					,	(
												        1,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )					,	)

												# Test batched
												A		=  image_processor(__UpperCamelCase					,	return_tensors="pt"		).pixel_values
												self.assertEqual(
												    encoded_videos.shape					,	(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )					,	)


					def      lowerCamelCase	( self      :List[str]		):
												# Initialize image_processor
												A		=  self.image_processing_class(**self.image_processor_dict		)
												# create random PyTorch tensors
												A		=  prepare_video_inputs(self.image_processor_tester					,	equal_resolution=__UpperCamelCase					,	torchify=__UpperCamelCase		)
												for video in video_inputs:
																			self.assertIsInstance(__UpperCamelCase					,	__UpperCamelCase		)
																			self.assertIsInstance(video[0]					,	torch.Tensor		)

												# Test not batched input
												A		=  image_processor(video_inputs[0]					,	return_tensors="pt"		).pixel_values
												self.assertEqual(
												    encoded_videos.shape					,	(
												        1,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )					,	)

												# Test batched
												A		=  image_processor(__UpperCamelCase					,	return_tensors="pt"		).pixel_values
												self.assertEqual(
												    encoded_videos.shape					,	(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_frames,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )					,	)









"""simple docstring"""
import numpy as np
import requests






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






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  len_data

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






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.sum(np.square(UpperCamelCase	)	)
							A		=  sum_elem / (2 * len_data)
							return error






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  100_000
							A		=  0.0_00_15_50

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

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

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

							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  sum(abs(y - predicted_y[i]	) for i, y in enumerate(UpperCamelCase	)	)
							return total / len(UpperCamelCase	)






def  A__    (      ):
							A		=  collect_dataset()

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

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


if __name__ == "__main__":
		main()


