
'''simple docstring'''





from __future__ import annotations

from typing import Generic, TypeVar

a__						:				Tuple           =    TypeVar("T")


class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE):
 pass


class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE):
 pass


class 	UpperCamelCase__					(      Generic[T]):






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







       '''simple docstring'''



       __A    		=     []
       __A    		=     limit






 def __bool__(     self					:List[str]     )			->     bool:







       '''simple docstring'''



       return bool(self.stack     )






 def __str__(     self					:Any     )			->     str:







       '''simple docstring'''



       return str(self.stack     )






 def 		lowercase_  (     self					:int		,			_A					:T     )			->     None:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:List[str]     )			->     T:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:Dict     )			->     T:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:List[str]     )			->     bool:







       '''simple docstring'''



       return not bool(self.stack     )






 def 		lowercase_  (     self					:Tuple     )			->     bool:







       '''simple docstring'''



       return self.size() == self.limit






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       return len(self.stack     )






 def __contains__(     self					:Tuple		,			_A					:T     )			->     bool:







       '''simple docstring'''



       return item in self.stack





def     snake_case							(						)->     None:







      """simple docstring"""
      __A    		=     Stack(1_0		)
      assert bool(UpperCAmelCase		) is False
      assert stack.is_empty() is True
      assert stack.is_full() is False
      assert str(UpperCAmelCase		) == "[]"

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

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

      for i in range(1_0		):
            assert stack.size() == i
            stack.push(UpperCAmelCase		)

      assert bool(UpperCAmelCase		)
      assert not stack.is_empty()
      assert stack.is_full()
      assert str(UpperCAmelCase		) == str(list(range(1_0		)		)		)
      assert stack.pop() == 9
      assert stack.peek() == 8

      stack.push(1_0_0		)
      assert str(UpperCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_0_0]		)

      try:
            stack.push(2_0_0		)
            raise AssertionError  # This should not happen
      except StackOverflowError:
            assert True  # This should happen

      assert not stack.is_empty()
      assert stack.size() == 1_0

      assert 5 in stack
      assert 5_5 not in stack


if __name__ == "__main__":
      test_stack()








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference


@slow
@require_torch_gpu
class 	UpperCamelCase__					(      unittest.TestCase):






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







       '''simple docstring'''



       __A    		=     'runwayml/stable-diffusion-v1-5'
       __A    		=     TextToVideoZeroPipeline.from_pretrained(_A		,			torch_dtype=torch.floataa     ).to('cuda'     )
       __A    		=     DDIMScheduler.from_config(pipe.scheduler.config     )
       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )

       __A    		=     'A bear is playing a guitar on Times Square'
       __A    		=     pipe(prompt=_A		,			generator=_A     ).images

       __A    		=     load_pt(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'     )

       assert_mean_pixel_difference(_A		,			_A     )






