





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 			__A(    __UpperCAmelCase    ,      __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							IFImgaImgPipeline
     __A							=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     __A							=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __A							=							PipelineTesterMixin.required_optional_params - {"latents"}

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self._get_dummy_components()

     def 						_UpperCamelCase  (   self,							A,							A=0   ):





            """simple docstring"""

            if str(A   ).startswith('''mps'''   ):
                   _UpperCamelCase														=    torch.manual_seed(A   )
            else:
                   _UpperCamelCase														=    torch.Generator(device=A   ).manual_seed(A   )

            _UpperCamelCase														=    floats_tensor((1, 3, 32, 32),							rng=random.Random(A   )   ).to(A   )

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

            return inputs

     def 						_UpperCamelCase  (   self   ):





            """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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )

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





            """simple docstring"""

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

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





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self._test_save_load_local()



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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













import numpy as np
import requests


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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

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

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

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

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

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

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


if __name__ == "__main__":
       main()






