





import numpy as np
import requests






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






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		int  			=							len_data

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							np.dot(__lowerCamelCase,				data_x.transpose()		)
     prod -= data_y.transpose()
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							np.dot(__lowerCamelCase,				__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							theta - (alpha / n) * sum_grad
     return theta






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		str  			=							np.dot(__lowerCamelCase,				data_x.transpose()		)
     prod -= data_y.transpose()
     _SCREAMING_SNAKE_CASE				:		str  			=							np.sum(np.square(__lowerCamelCase		)		)
     _SCREAMING_SNAKE_CASE				:		Any  			=							sum_elem / (2 * len_data)
     return error






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		str  			=							100000
     _SCREAMING_SNAKE_CASE				:		Any  			=							0.000_1550

     _SCREAMING_SNAKE_CASE				:		Any  			=							data_x.shape[1]
     _SCREAMING_SNAKE_CASE				:		Any  			=							data_x.shape[0] - 1

     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							np.zeros((1, no_features)		)

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

     return theta






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Any  			=							sum(abs(y - predicted_y[i]		) for i, y in enumerate(__lowerCamelCase		)		)
     return total / len(__lowerCamelCase		)






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Any  			=							collect_dataset()

     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							data.shape[0]
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							np.c_[np.ones(__lowerCamelCase		), data[:, :-1]].astype(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							data[:, -1].astype(__lowerCamelCase		)

     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							run_linear_regression(__lowerCamelCase,				__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		str  			=							theta.shape[1]
     print("Resultant Feature vector : "		)
     for i in range(0,				__lowerCamelCase		):
          print(f"""{theta[0, i]:.5f}"""		)


if __name__ == "__main__":
       main()







"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

      a     :			Dict				=   {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_save_load_optional_components()





    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,		reason='XFormers attention is only available with CUDA and `xformers` installed'				,		)
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_floataa_inference(expected_max_diff=1E-1			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2				,		)




