




'''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 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):



 def  SCREAMING_SNAKE_CASE(	self ,		A = False			):






        '''simple docstring'''




        lowercase	:		str    =   10

        if only_cross_attention:
               lowercase	:		Union[str, Any]    =   12
        else:
               # when only cross attention is not set, the cross attention dim must be the same as the query dim
               lowercase	:		List[str]    =   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  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   2

        lowercase	:		Optional[Any]    =   torch.rand(A ,		A ,		3 ,		2			)
        lowercase	:		List[Any]    =   torch.rand(A ,		4 ,		A			)
        lowercase	:		Tuple    =   None

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





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        torch.manual_seed(0			)

        lowercase	:		int    =   self.get_constructor_arguments(only_cross_attention=A			)
        lowercase	:		List[Any]    =   Attention(**A			)

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

        lowercase	:		Optional[int]    =   self.get_forward_arguments(
            query_dim=constructor_args['''query_dim'''] ,		added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']			)

        lowercase	:		List[Any]    =   attn(**A			)

        # only self attention

        torch.manual_seed(0			)

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

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

        lowercase	:		str    =   self.get_forward_arguments(
            query_dim=constructor_args['''query_dim'''] ,		added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']			)

        lowercase	:		Optional[Any]    =   attn(**A			)

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






class 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   DiffusionPipeline.from_pretrained('''hf-internal-testing/tiny-stable-diffusion-pipe''' ,		safety_checker=A			)

        lowercase	:		List[Any]    =   pipe(
            '''foo''' ,		num_inference_steps=2 ,		generator=torch.Generator('''cpu'''			).manual_seed(0			) ,		output_type='''np''' ,		).images

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

        lowercase	:		str    =   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
               lowercase	:		Tuple    =   DiffusionPipeline.from_pretrained(A ,		device_map='''sequential''' ,		safety_checker=A			)

        lowercase	:		Tuple    =   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'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




