





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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							(		lowerCamelCase__						,			unittest.TestCase		):
							lowerCamelCase  :   Any    =KandinskyInpaintPipeline
							lowerCamelCase  :   Tuple    =["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							lowerCamelCase  :   List[Any]    =[
							    """prompt""",
							    """negative_prompt""",
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							lowerCamelCase  :   str    =[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """negative_prompt""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							lowerCamelCase  :   Any    =False






							@property
							def        __a  (  self )      ->					str:
								return 32






							@property
							def        __a  (  self )      ->					List[Any]:
								return 32






							@property
							def        __a  (  self )      ->					List[str]:
								return self.time_input_dim






							@property
							def        __a  (  self )      ->					Union[str, Any]:
								return self.time_input_dim * 4






							@property
							def        __a  (  self )      ->					Any:
								return 100






							@property
							def        __a  (  self )      ->					Tuple:
								a :						Optional[int]      =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
								return tokenizer






							@property
							def        __a  (  self )      ->					Optional[int]:
								torch.manual_seed(0 )
								a :						Union[str, Any]      =							MCLIPConfig(
								    numDims=self.cross_attention_dim ,   transformerDimensions=self.text_embedder_hidden_size ,   hidden_size=self.text_embedder_hidden_size ,   intermediate_size=37 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   vocab_size=1005 ,   )

								a :						Optional[Any]      =							MultilingualCLIP(SCREAMING_SNAKE_CASE__ )
								a :						List[Any]      =							text_encoder.eval()

								return text_encoder






							@property
							def        __a  (  self )      ->					int:
								torch.manual_seed(0 )

								a :						Any      =							{
								    '''in_channels''': 9,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								a :						Dict      =							UNetaDConditionModel(**SCREAMING_SNAKE_CASE__ )
								return model






							@property
							def        __a  (  self )      ->					str:
								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        __a  (  self )      ->					Optional[Any]:
								torch.manual_seed(0 )
								a :						List[Any]      =							VQModel(**self.dummy_movq_kwargs )
								return model






							def        __a  (  self )      ->					Any:
								a :						int      =							self.dummy_text_encoder
								a :						str      =							self.dummy_tokenizer
								a :						Dict      =							self.dummy_unet
								a :						Dict      =							self.dummy_movq

								a :						Optional[Any]      =							DDIMScheduler(
								    num_train_timesteps=1000 ,   beta_schedule="linear" ,   beta_start=0.00_085 ,   beta_end=0.012 ,   clip_sample=SCREAMING_SNAKE_CASE__ ,   set_alpha_to_one=SCREAMING_SNAKE_CASE__ ,   steps_offset=1 ,   prediction_type="epsilon" ,   thresholding=SCREAMING_SNAKE_CASE__ ,   )

								a :						int      =							{
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=0 )      ->					Any:
								a :						List[Any]      =							floats_tensor((1, self.cross_attention_dim) ,   rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
								a :						int      =							floats_tensor((1, self.cross_attention_dim) ,   rng=random.Random(seed + 1 ) ).to(SCREAMING_SNAKE_CASE__ )
								# create init_image
								a :						Optional[Any]      =							floats_tensor((1, 3, 64, 64) ,   rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
								a :						Any      =							image.cpu().permute(0 ,   2 ,   3 ,   1 )[0]
								a :						List[Any]      =							Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert("RGB" ).resize((256, 256) )
								# create mask
								a :						Tuple      =							np.ones((64, 64) ,   dtype=np.floataa )
								a :						List[str]      =							0

								if str(SCREAMING_SNAKE_CASE__ ).startswith("mps" ):
									a :						Dict      =							torch.manual_seed(SCREAMING_SNAKE_CASE__ )
								else:
									a :						Any      =							torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
								a :						Any      =							{
								    '''prompt''': '''horse''',
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 64,
								    '''width''': 64,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs






							def        __a  (  self )      ->					int:
								a :						int      =							'''cpu'''

								a :						Union[str, Any]      =							self.get_dummy_components()

								a :						Tuple      =							self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
								a :						Union[str, Any]      =							pipe.to(SCREAMING_SNAKE_CASE__ )

								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

								a :						int      =							pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ ) )
								a :						Optional[int]      =							output.images

								a :						Optional[int]      =							pipe(
								    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ ) ,   return_dict=SCREAMING_SNAKE_CASE__ ,   )[0]

								a :						Optional[int]      =							image[0, -3:, -3:, -1]
								a :						Optional[Any]      =							image_from_tuple[0, -3:, -3:, -1]

								print(f"""image.shape {image.shape}""" )

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

								a :						Any      =							np.array(
								    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786] )

								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()}"""






							def        __a  (  self )      ->					List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3 )


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






							def        __a  (  self )      ->					str:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def        __a  (  self )      ->					List[str]:
								a :						List[Any]      =							load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

								a :						str      =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
								a :						str      =							np.ones((768, 768) ,   dtype=np.floataa )
								a :						List[Any]      =							0

								a :						Optional[Any]      =							'''a hat'''

								a :						Tuple      =							KandinskyPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-prior" ,   torch_dtype=torch.floataa )
								pipe_prior.to(SCREAMING_SNAKE_CASE__ )

								a :						Union[str, Any]      =							KandinskyInpaintPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-inpaint" ,   torch_dtype=torch.floataa )
								a :						List[Any]      =							pipeline.to(SCREAMING_SNAKE_CASE__ )
								pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

								a :						Dict      =							torch.Generator(device="cpu" ).manual_seed(0 )
								a :						Any      =							pipe_prior(
								    SCREAMING_SNAKE_CASE__ ,   generator=SCREAMING_SNAKE_CASE__ ,   num_inference_steps=5 ,   negative_prompt="" ,   ).to_tuple()

								a :						Union[str, Any]      =							pipeline(
								    SCREAMING_SNAKE_CASE__ ,   image=SCREAMING_SNAKE_CASE__ ,   mask_image=SCREAMING_SNAKE_CASE__ ,   image_embeds=SCREAMING_SNAKE_CASE__ ,   negative_image_embeds=SCREAMING_SNAKE_CASE__ ,   generator=SCREAMING_SNAKE_CASE__ ,   num_inference_steps=100 ,   height=768 ,   width=768 ,   output_type="np" ,   )

								a :						Optional[Any]      =							output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ ,   SCREAMING_SNAKE_CASE__ )






"""simple docstring"""






# 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 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	cost_function(snake_case__       ,					snake_case__		)
						if iterations % 1_00 == 0:
									print(f"loss: {j} \t"		)  # printing the loss after every 100 iterations
			return theta


# In[68]:

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

					UpperCamelCase__				:Tuple			   =	0.1
					UpperCamelCase__				:List[str]			   =	logistic_reg(alpha, x, y, max_iterations=70_000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





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

					plt.legend()




					plt.show()

