




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 						UpperCAmelCase_    (      self :List[Any]			,   lowerCamelCase :bool = False   )  ->      Any:
       UpperCAmelCase__							=    10

       if only_cross_attention:
        UpperCAmelCase__							=    12
       else:
        # when only cross attention is not set, the cross attention dim must be the same as the query dim
        UpperCAmelCase__							=    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 						UpperCAmelCase_    (      self :List[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :Union[str, Any]   )  ->      Optional[Any]:
       UpperCAmelCase__							=    2

       UpperCAmelCase__							=    torch.rand(_a			,   _a			,   3			,   2   )
       UpperCAmelCase__							=    torch.rand(_a			,   4			,   _a   )
       UpperCAmelCase__							=    None

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







      def 						UpperCAmelCase_    (      self :int   )  ->      Tuple:
       # self and cross attention

       torch.manual_seed(0   )

       UpperCAmelCase__							=    self.get_constructor_arguments(only_cross_attention=_a   )
       UpperCAmelCase__							=    Attention(**_a   )

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

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

       UpperCAmelCase__							=    attn(**_a   )

       # only self attention

       torch.manual_seed(0   )

       UpperCAmelCase__							=    self.get_constructor_arguments(only_cross_attention=_a   )
       UpperCAmelCase__							=    Attention(**_a   )

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

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

       UpperCAmelCase__							=    attn(**_a   )

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



class       _UpperCamelCase   (					unittest.TestCase		):
      def 						UpperCAmelCase_    (      self :int   )  ->      List[str]:
       UpperCAmelCase__							=    DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe"			,   safety_checker=_a   )

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

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

       UpperCAmelCase__							=    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
        UpperCAmelCase__							=    DiffusionPipeline.from_pretrained(_a			,   device_map="sequential"			,   safety_checker=_a   )

       UpperCAmelCase__							=    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   )   )










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





