
'''simple docstring'''





from .number_theory.prime_numbers import next_prime


class 	UpperCamelCase__					:






 def __init__(     self					:Dict		,			_A					:int		,			_A					:int | None = None		,			_A					:float | None = None		,			)			->     None:







       '''simple docstring'''



       __A    		=     size_table
       __A    		=     [None] * self.size_table
       __A    		=     0.75 if lim_charge is None else lim_charge
       __A    		=     1 if charge_factor is None else charge_factor
       __A    		=     []
       __A    		=     {}






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







       '''simple docstring'''



       return self._keys






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







       '''simple docstring'''



       return sum(1 for slot in self.values if slot is not None     ) / (
           self.size_table * self.charge_factor
       )






 def 		lowercase_  (     self					:List[str]		,			_A					:int     )			->     Optional[int]:







       '''simple docstring'''



       return key % self.size_table






 def 		lowercase_  (     self					:List[Any]		,			_A					:int     )			->     str:







       '''simple docstring'''



       print(F'step {step_ord}'     )
       print(list(range(len(self.values     )     )     )     )
       print(self.values     )






 def 		lowercase_  (     self					:Optional[int]		,			_A					:Optional[int]     )			->     Union[str, Any]:







       '''simple docstring'''



       __A    		=     1
       __A    		=     values
       for value in values:
             self.insert_data(_A     )
             self._step_by_step(_A     )
             i += 1






 def 		lowercase_  (     self					:Dict		,			_A					:Union[str, Any]		,			_A					:Tuple     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     data
       __A    		=     data






 def 		lowercase_  (     self					:Optional[int]		,			_A					:Dict		,			_A					:List[Any]=None     )			->     int:







       '''simple docstring'''



       __A    		=     self.hash_function(key + 1     )

       while self.values[new_key] is not None and self.values[new_key] != key:
             if self.values.count(_A     ) > 0:
                   __A    		=     self.hash_function(new_key + 1     )
             else:
                   __A    		=     None
                   break

       return new_key






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







       '''simple docstring'''



       __A    		=     [value for value in self.values if value is not None]
       __A    		=     next_prime(self.size_table		,			factor=2     )
       self._keys.clear()
       __A    		=     [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
             self.insert_data(_A     )






 def 		lowercase_  (     self					:List[str]		,			_A					:Dict     )			->     int:







       '''simple docstring'''



       __A    		=     self.hash_function(_A     )

       if self.values[key] is None:
             self._set_value(_A		,			_A     )

       elif self.values[key] == data:
             pass

       else:
             __A    		=     self._collision_resolution(_A		,			_A     )
             if collision_resolution is not None:
                   self._set_value(_A		,			_A     )
             else:
                   self.rehashing()
                   self.insert_data(_A     )








'''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     )






