





"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor






class  _UpperCAmelCase       (     unittest.TestCase			):


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :str = False		):
												A		=  10

												if only_cross_attention:
																			A		=  12
												else:
																			# when only cross attention is not set, the cross attention dim must be the same as the query dim
																			A		=  query_dim

												return {
												    "query_dim": query_dim,
												    "cross_attention_dim": cross_attention_dim,
												    "heads": 2,
												    "dim_head": 4,
												    "added_kv_proj_dim": 6,
												    "norm_num_groups": 1,
												    "only_cross_attention": only_cross_attention,
												    "processor": AttnAddedKVProcessor(),
												}


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[Any]		):
												A		=  2

												A		=  torch.rand(lowerCAmelCase__					,	lowerCAmelCase__					,	3					,	2		)
												A		=  torch.rand(lowerCAmelCase__					,	4					,	lowerCAmelCase__		)
												A		=  None

												return {
												    "hidden_states": hidden_states,
												    "encoder_hidden_states": encoder_hidden_states,
												    "attention_mask": attention_mask,
												}


					def      lowerCamelCase	( self      :Tuple		):
												torch.manual_seed(0		)

												A		=  self.get_constructor_arguments(only_cross_attention=lowerCAmelCase__		)
												A		=  Attention(**lowerCAmelCase__		)

												self.assertTrue(attn.to_k is not None		)
												self.assertTrue(attn.to_v is not None		)

												A		=  self.get_forward_arguments(
												    query_dim=constructor_args["query_dim"]					,	added_kv_proj_dim=constructor_args["added_kv_proj_dim"]		)

												A		=  attn(**lowerCAmelCase__		)

												# only self attention

												torch.manual_seed(0		)

												A		=  self.get_constructor_arguments(only_cross_attention=lowerCAmelCase__		)
												A		=  Attention(**lowerCAmelCase__		)

												self.assertTrue(attn.to_k is None		)
												self.assertTrue(attn.to_v is None		)

												A		=  self.get_forward_arguments(
												    query_dim=constructor_args["query_dim"]					,	added_kv_proj_dim=constructor_args["added_kv_proj_dim"]		)

												A		=  attn(**lowerCAmelCase__		)

												self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()		)







class  _UpperCAmelCase       (     unittest.TestCase			):


					def      lowerCamelCase	( self      :Any		):
												A		=  DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"					,	safety_checker=lowerCAmelCase__		)

												A		=  pipe(
												    "foo"					,	num_inference_steps=2					,	generator=torch.Generator("cpu"		).manual_seed(0		)					,	output_type="np"					,	).images

												# the initial conversion succeeds
												A		=  DiffusionPipeline.from_pretrained(
												    "hf-internal-testing/tiny-stable-diffusion-pipe"					,	device_map="sequential"					,	safety_checker=lowerCAmelCase__		)

												A		=  pipe(
												    "foo"					,	num_inference_steps=2					,	generator=torch.Generator("cpu"		).manual_seed(0		)					,	output_type="np"					,	).images

												with tempfile.TemporaryDirectory() as tmpdir:
																			# save the converted model
																			pipe.save_pretrained(lowerCAmelCase__		)

																			# can also load the converted weights
																			A		=  DiffusionPipeline.from_pretrained(lowerCAmelCase__					,	device_map="sequential"					,	safety_checker=lowerCAmelCase__		)

												A		=  pipe(
												    "foo"					,	num_inference_steps=2					,	generator=torch.Generator("cpu"		).manual_seed(0		)					,	output_type="np"					,	).images

												self.assertTrue(np.allclose(lowerCAmelCase__					,	lowerCAmelCase__					,	atol=1e-5		)		)
												self.assertTrue(np.allclose(lowerCAmelCase__					,	lowerCAmelCase__					,	atol=1e-5		)		)









"""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()


