


from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase     =       TypeVar("""T""")







class lowerCAmelCase_		(					Generic[T]			):

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase   ):
       snake_case_			= data  # Assign data
       snake_case_			= None  # Initialize next as null
       snake_case_			= None  # Initialize prev as null







class lowerCAmelCase_		(					Generic[T]			):

 '''simple docstring'''

 def __init__(      self   ):
       snake_case_			= None



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if self.head is None:
             snake_case_			= Node(_UpperCAmelCase   )
       else:
             snake_case_			= Node(_UpperCAmelCase   )
             snake_case_			= new_node
             snake_case_			= self.head
             snake_case_			= None
             snake_case_			= new_node



 def 		UpperCamelCase__	(      self   ):
       if self.head is None:
             return None
       else:
             assert self.head is not None
             snake_case_			= self.head.data
             snake_case_			= self.head.next
             if self.head is not None:
                   snake_case_			= None
             return temp



 def 		UpperCamelCase__	(      self   ):
       return self.head.data if self.head is not None else None



 def __len__(      self   ):
       snake_case_			= self.head
       snake_case_			= 0
       while temp is not None:
             count += 1
             snake_case_			= temp.next
       return count



 def 		UpperCamelCase__	(      self   ):
       return self.head is None





 def 		UpperCamelCase__	(      self   ):
       print('''stack elements are:'''   )
       snake_case_			= self.head
       while temp is not None:
             print(temp.data						, end='''->'''   )
             snake_case_			= temp.next


# Code execution starts here
if __name__ == "__main__":
   # Start with the empty stack
   UpperCAmelCase     =       Stack()

   # Insert 4 at the beginning. So stack becomes 4->None
   print("""Stack operations using Doubly LinkedList""")
   stack.push(4)

   # Insert 5 at the beginning. So stack becomes 4->5->None
   stack.push(5)

   # Insert 6 at the beginning. So stack becomes 4->5->6->None
   stack.push(6)

   # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
   stack.push(7)

   # Print the stack
   stack.print_stack()

   # Print the top element
   print("""\nTop element is """, stack.top())

   # Print the stack size
   print("""Size of the stack is """, len(stack))

   # pop the top element
   stack.pop()

   # pop the top element
   stack.pop()

   # two elements have now been popped off
   stack.print_stack()

   # Print True if the stack is empty else False
   print("""\nstack is empty:""", stack.is_empty())



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						, )