



import tempfile
import unittest

import numpy as np
import torch

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


class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	def      a__ (		self					,   A = False			) ->							Union[str, Any]:
				A:      Optional[Any]														=   10

				if only_cross_attention:
							A:      int														=   12
				else:
							# when only cross attention is not set, the cross attention dim must be the same as the query dim
							A:      Tuple														=   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      a__ (		self					,   A					,   A			) ->							Any:
				A:      Dict														=   2

				A:      int														=   torch.rand(A					,   A					,   3					,   2			)
				A:      Any														=   torch.rand(A					,   4					,   A			)
				A:      Union[str, Any]														=   None

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





	def      a__ (		self			) ->							Optional[int]:
				torch.manual_seed(0			)

				A:      List[str]														=   self.get_constructor_arguments(only_cross_attention=A			)
				A:      str														=   Attention(**A			)

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

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

				A:      Tuple														=   attn(**A			)

				# only self attention

				torch.manual_seed(0			)

				A:      str														=   self.get_constructor_arguments(only_cross_attention=A			)
				A:      int														=   Attention(**A			)

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

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

				A:      str														=   attn(**A			)

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







class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	def      a__ (		self			) ->							int:
				A:      Optional[int]														=   DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""					,   safety_checker=A			)

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

				# the initial conversion succeeds
				A:      Union[str, Any]														=   DiffusionPipeline.from_pretrained(
				    """hf-internal-testing/tiny-stable-diffusion-pipe"""					,   device_map="""sequential"""					,   safety_checker=A			)

				A:      int														=   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(A			)

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

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

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










'''simple docstring'''
import numpy as np
import requests



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







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



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			A:      Union[str, Any]														=   len_data

			A:      Tuple														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
			prod -= data_y.transpose()
			A:      Optional[Any]														=   np.dot(lowerCamelCase__							,							lowerCamelCase__				)
			A:      Optional[Any]														=   theta - (alpha / n) * sum_grad
			return theta



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Union[str, Any]							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A:      str														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
			prod -= data_y.transpose()
			A:      Any														=   np.sum(np.square(lowerCamelCase__				)				)
			A:      List[Any]														=   sum_elem / (2 * len_data)
			return error



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A:      Dict														=   100000
			A:      Dict														=   0.0_00_15_50

			A:      Union[str, Any]														=   data_x.shape[1]
			A:      Tuple														=   data_x.shape[0] - 1

			A:      str														=   np.zeros((1, no_features)				)

			for i in range(0							,							lowerCamelCase__				):
						A:      List[str]														=   run_steep_gradient_descent(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)
						A:      str														=   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__   :      List[Any]							,							lowerCamelCase__   :      List[str]				):


			'''simple docstring'''







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



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      Optional[Any]														=   collect_dataset()

			A:      str														=   data.shape[0]
			A:      Union[str, Any]														=   np.c_[np.ones(lowerCamelCase__				), data[:, :-1]].astype(lowerCamelCase__				)
			A:      Any														=   data[:, -1].astype(lowerCamelCase__				)

			A:      Dict														=   run_linear_regression(lowerCamelCase__							,							lowerCamelCase__				)
			A:      Union[str, Any]														=   theta.shape[1]
			print("""Resultant Feature vector : """				)
			for i in range(0							,							lowerCamelCase__				):
						print(f'{theta[0, i]:.5f}'				)


if __name__ == "__main__":
					main()





