
"""simple docstring"""
from .number_theory.prime_numbers import next_prime






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = None    ,     ):
      _snake_case  :    Optional[int]            = size_table
      _snake_case  :    Optional[int]            = [None] * self.size_table
      _snake_case  :    int            = 0.75 if lim_charge is None else lim_charge
      _snake_case  :    List[Any]            = 1 if charge_factor is None else charge_factor
      _snake_case  :    list            = []
      _snake_case  :    dict            = {}
    def        lowerCamelCase__						(     self  ):
      return self._keys
    def        lowerCamelCase__						(     self  ):
      return sum(1 for slot in self.values if slot is not None  ) / (
          self.size_table * self.charge_factor
      )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return key % self.size_table
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      print(F'step {step_ord}'  )
      print(list(range(len(self.values  )  )  )  )
      print(self.values  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = 1
      _snake_case  :    Tuple            = values
      for value in values:
        self.insert_data(snake_case_  )
        self._step_by_step(snake_case_  )
        i += 1
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Tuple            = data
      _snake_case  :    str            = data
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None  ):
      _snake_case  :    Union[str, Any]            = self.hash_function(key + 1  )

      while self.values[new_key] is not None and self.values[new_key] != key:
        if self.values.count(snake_case_  ) > 0:
          _snake_case  :    Optional[int]            = self.hash_function(new_key + 1  )
        else:
          _snake_case  :    List[str]            = None
          break

      return new_key
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = [value for value in self.values if value is not None]
      _snake_case  :    Tuple            = next_prime(self.size_table    ,     factor=2  )
      self._keys.clear()
      _snake_case  :    int            = [None] * self.size_table  # hell's pointers D: don't DRY ;/
      for value in survivor_values:
        self.insert_data(snake_case_  )


    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    List[str]            = self.hash_function(snake_case_  )

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

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

      else:
        _snake_case  :    int            = self._collision_resolution(snake_case_    ,     snake_case_  )
        if collision_resolution is not None:
          self._set_value(snake_case_    ,     snake_case_  )
        else:
          self.rehashing()
          self.insert_data(snake_case_  )



"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     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  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


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

