




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class        UpperCamelCase_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   KandinskyVaaPipeline
    UpperCAmelCase__													=   [
        '''image_embeds''',
        '''negative_image_embeds''',
    ]
    UpperCAmelCase__													=   ['''image_embeds''', '''negative_image_embeds''']
    UpperCAmelCase__													=   [
        '''generator''',
        '''height''',
        '''width''',
        '''latents''',
        '''guidance_scale''',
        '''num_inference_steps''',
        '''return_dict''',
        '''guidance_scale''',
        '''num_images_per_prompt''',
        '''output_type''',
        '''return_dict''',
    ]
    UpperCAmelCase__													=   False


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           return 32


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           return 32


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           return self.time_input_dim


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           return self.time_input_dim * 4


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           return 100


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[Any]:





           '''simple docstring'''

           torch.manual_seed(0)

           A__   				=					{
               '''in_channels''': 4,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''image''',
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''encoder_hid_dim''': self.text_embedder_hidden_size,
               '''encoder_hid_dim_type''': '''image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           A__   				=					UNetaDConditionModel(**UpperCAmelCase__)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[int]:





           '''simple docstring'''

           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->str:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					VQModel(**self.dummy_movq_kwargs)
           return model


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[Any]:





           '''simple docstring'''

           A__   				=					self.dummy_unet
           A__   				=					self.dummy_movq

           A__   				=					DDIMScheduler(
               num_train_timesteps=1_000      ,				beta_schedule='''linear'''      ,				beta_start=0.00085      ,				beta_end=0.012      ,				clip_sample=UpperCAmelCase__      ,				set_alpha_to_one=UpperCAmelCase__      ,				steps_offset=1      ,				prediction_type='''epsilon'''      ,				thresholding=UpperCAmelCase__      ,				)

           A__   				=					{
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }
           return components


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Union[str, Any]=0)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					floats_tensor((1, self.text_embedder_hidden_size)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, self.text_embedder_hidden_size)      ,				rng=random.Random(seed + 1)).to(
               UpperCAmelCase__)
           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)
           A__   				=					{
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 64,
               '''width''': 64,
               '''guidance_scale''': 4.0,
               '''num_inference_steps''': 2,
               '''output_type''': '''np''',
           }
           return inputs





    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[int]:





           '''simple docstring'''

           A__   				=					'''cpu'''

           A__   				=					self.get_dummy_components()

           A__   				=					self.pipeline_class(**UpperCAmelCase__)
           A__   				=					pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					pipe(**self.get_dummy_inputs(UpperCAmelCase__))
           A__   				=					output.images

           A__   				=					pipe(
               **self.get_dummy_inputs(UpperCAmelCase__)      ,				return_dict=UpperCAmelCase__      ,				)[0]

           A__   				=					image[0, -3:, -3:, -1]
           A__   				=					image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__   				=					np.array(
               [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046])

           assert (
               np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
           ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
           ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



@slow
@require_torch_gpu
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[int]:





           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''')

           A__   				=					KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''      ,				torch_dtype=torch.floataa)
           pipe_prior.to(UpperCAmelCase__)

           A__   				=					KandinskyVaaPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''      ,				torch_dtype=torch.floataa)
           A__   				=					pipeline.to(UpperCAmelCase__)
           pipeline.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''red cat, 4k photo'''

           A__   				=					torch.Generator(device='''cuda''').manual_seed(0)
           A__				,							A__   				=					pipe_prior(
               UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=5      ,				negative_prompt=''''''      ,				).to_tuple()

           A__   				=					torch.Generator(device='''cuda''').manual_seed(0)
           A__   				=					pipeline(
               image_embeds=UpperCAmelCase__      ,				negative_image_embeds=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=100      ,				output_type='''np'''      ,				)

           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)

           assert_mean_pixel_difference(UpperCAmelCase__      ,				UpperCAmelCase__)








# 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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

       for iterations in range(lowercase_		):
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					np.dot(x.T				,       h - y		) / y.size
              A__   				=					theta - alpha * gradient  # updating the weights
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					cost_function(lowercase_				,       lowercase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

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

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # 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""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


