



import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput

@dataclass
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      42






class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						CustomOutput(images=np.random.rand(1  ,	3  ,	4  ,	4					)					)

      # check every way of getting the attribute
      assert isinstance(outputs.images  ,	np.ndarray					)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs["images"]  ,	np.ndarray					)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]  ,	np.ndarray					)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						CustomOutput(images=[PIL.Image.new("RGB"  ,	(4, 4)					)]					)

      # check every way of getting the attribute
      assert isinstance(outputs.images  ,	UpperCamelCase					)
      assert isinstance(outputs.images[0]  ,	PIL.Image.Image					)
      assert isinstance(outputs["images"]  ,	UpperCamelCase					)
      assert isinstance(outputs["images"][0]  ,	PIL.Image.Image					)
      assert isinstance(outputs[0]  ,	UpperCamelCase					)
      assert isinstance(outputs[0][0]  ,	PIL.Image.Image					)






     def 						__snake_case     (							self			:				Optional[int]					)->Tuple:
      # test output reinitialization with a `dict` for compatibility with `accelerate`
      __SCREAMING_SNAKE_CASE							:     Dict							=						CustomOutput({"images": np.random.rand(1  ,	3  ,	4  ,	4					)}					)

      # check every way of getting the attribute
      assert isinstance(outputs.images  ,	np.ndarray					)
      assert outputs.images.shape == (1, 3, 4, 4)
      assert isinstance(outputs["images"]  ,	np.ndarray					)
      assert outputs["images"].shape == (1, 3, 4, 4)
      assert isinstance(outputs[0]  ,	np.ndarray					)
      assert outputs[0].shape == (1, 3, 4, 4)

      # test with a non-tensor attribute
      __SCREAMING_SNAKE_CASE							:     int							=						CustomOutput({"images": [PIL.Image.new("RGB"  ,	(4, 4)					)]}					)

      # check every way of getting the attribute
      assert isinstance(outputs.images  ,	UpperCamelCase					)
      assert isinstance(outputs.images[0]  ,	PIL.Image.Image					)
      assert isinstance(outputs["images"]  ,	UpperCamelCase					)
      assert isinstance(outputs["images"][0]  ,	PIL.Image.Image					)
      assert isinstance(outputs[0]  ,	UpperCamelCase					)
      assert isinstance(outputs[0][0]  ,	PIL.Image.Image					)











from __future__ import annotations

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

_lowerCamelCase         =	TypeVar("""T""", bound=bool)

class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     T							=						value
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None





     @property
     def 						__snake_case     (							self			:				Dict					)->T:
      return self._value






     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				SkewNode[T] | None  ,	UpperCamelCase			:				SkewNode[T] | None					)->SkewNode[T] | None:
      if not roota:
       return roota

      if not roota:
       return roota

      if roota.value > roota.value:
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						roota, roota

      __SCREAMING_SNAKE_CASE							:     int							=						roota
      __SCREAMING_SNAKE_CASE							:     str							=						roota.right
      __SCREAMING_SNAKE_CASE							:     int							=						roota.left
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						SkewNode.merge(UpperCamelCase  ,	UpperCamelCase					)

      return result






class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				Iterable[T] | None = ()					)->None:
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      if data:
       for item in data:
        self.insert(UpperCamelCase					)





     def __bool__(							self			:				str					)->bool:
      return self._root is not None





     def __iter__(							self			:				Any					)->Iterator[T]:
      __SCREAMING_SNAKE_CASE							:     list[Any]							=						[]
      while self:
       result.append(self.pop()					)

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

      return iter(UpperCamelCase					)





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     str							=						SkewNode.merge(self._root  ,	SkewNode(UpperCamelCase					)					)





     def 						__snake_case     (							self			:				List[Any]					)->T | None:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.top()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						(
          SkewNode.merge(self._root.left  ,	self._root.right					) if self._root else None
      )

      return result





     def 						__snake_case     (							self			:				Optional[int]					)->T:
      if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value






     def 						__snake_case     (							self			:				Dict					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						None


if __name__ == "__main__":
      import doctest

      doctest.testmod()






