






from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase					    =	TypeVar('''T''')

class        snake_case_				(							lowerCamelCase__   ):







   """simple docstring"""



   pass






class        snake_case_				(							lowerCamelCase__   ):







   """simple docstring"""



   pass






class        snake_case_				(							Generic[T]   ):







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						List[str] = 10						)	->		Any:


       """simple docstring"""



       _lowercase     			=						[]
       _lowercase     			=						limit

   def __bool__(     self:						List[Any]						)	->		Union[str, Any]:


       """simple docstring"""



       return bool(self.stack						)

   def __str__(     self:						Optional[int]						)	->		Optional[Any]:


       """simple docstring"""



       return str(self.stack						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						Union[str, Any]						)	->		List[str]:


       """simple docstring"""



       if len(self.stack						) >= self.limit:
           raise StackOverflowError
       self.stack.append(_UpperCAmelCase						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		int:


       """simple docstring"""



       if not self.stack:
           raise StackUnderflowError
       return self.stack.pop()

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		int:


       """simple docstring"""



       if not self.stack:
           raise StackUnderflowError
       return self.stack[-1]

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Union[str, Any]:


       """simple docstring"""



       return not bool(self.stack						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		List[Any]:


       """simple docstring"""



       return self.size() == self.limit

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Dict:


       """simple docstring"""



       return len(self.stack						)

   def __contains__(     self:						Optional[Any] ,							snake_case__:						str						)	->		List[str]:


       """simple docstring"""



       return item in self.stack






def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						Stack(10   )
    assert bool(_lowercase   ) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(_lowercase   ) == "[]"

    try:
        _lowercase     			=						stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        _lowercase     			=						stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10   ):
        assert stack.size() == i
        stack.push(_lowercase   )

    assert bool(_lowercase   )
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(_lowercase   ) == str(list(range(10   )   )   )
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100   )
    assert str(_lowercase   ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]   )

    try:
        stack.push(200   )
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
   test_stack()




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )