





"""simple docstring"""


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					DDIMPipeline
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  SCREAMING_SNAKE_CASE__							:List[str]	     =					PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Union[str, Any]:
        torch.manual_seed(0	)
        _UpperCamelCase      :	List[Any]						=       UNetaDModel(
            block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=3					,   out_channels=3					,   down_block_types=("DownBlock2D", "AttnDownBlock2D")					,   up_block_types=("AttnUpBlock2D", "UpBlock2D")					,   )
        _UpperCamelCase      :	List[Any]						=       DDIMScheduler()
        _UpperCamelCase      :	Any						=       {"unet": unet, "scheduler": scheduler}
        return components

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       Union[str, Any]					,   __a   :       int=0	)		->				Dict:
        if str(__a	).startswith("mps"	):
              _UpperCamelCase      :	List[str]						=       torch.manual_seed(__a	)
        else:
              _UpperCamelCase      :	Tuple						=       torch.Generator(device=__a	).manual_seed(__a	)
        _UpperCamelCase      :	Tuple						=       {
            "batch_size": 1,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Union[str, Any]:
        _UpperCamelCase      :	Union[str, Any]						=       "cpu"

        _UpperCamelCase      :	Any						=       self.get_dummy_components()
        _UpperCamelCase      :	Any						=       self.pipeline_class(**__a	)
        pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)

        _UpperCamelCase      :	List[str]						=       self.get_dummy_inputs(__a	)
        _UpperCamelCase      :	Tuple						=       pipe(**__a	).images
        _UpperCamelCase      :	Union[str, Any]						=       image[0, -3:, -3:, -1]

        self.assertEqual(image.shape					,   (1, 32, 32, 3)	)
        _UpperCamelCase      :	List[Any]						=       np.array(
            [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]	)
        _UpperCamelCase      :	Dict						=       np.abs(image_slice.flatten() - expected_slice	).max()
        self.assertLessEqual(__a					,   1e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				List[Any]:
        super().test_save_load_local(expected_max_difference=3e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Dict:
        super().test_save_load_optional_components(expected_max_difference=3e-3	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)





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







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Tuple:
        _UpperCamelCase      :	List[Any]						=       "google/ddpm-cifar10-32"

        _UpperCamelCase      :	Tuple						=       UNetaDModel.from_pretrained(__a	)
        _UpperCamelCase      :	str						=       DDIMScheduler()

        _UpperCamelCase      :	Union[str, Any]						=       DDIMPipeline(unet=__a					,   scheduler=__a	)
        ddim.to(__a	)
        ddim.set_progress_bar_config(disable=__a	)

        _UpperCamelCase      :	Dict						=       torch.manual_seed(0	)
        _UpperCamelCase      :	Union[str, Any]						=       ddim(generator=__a					,   eta=0.0					,   output_type="numpy"	).images

        _UpperCamelCase      :	Optional[Any]						=       image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase      :	Any						=       np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[Any]:
        _UpperCamelCase      :	Any						=       "google/ddpm-ema-bedroom-256"

        _UpperCamelCase      :	Optional[int]						=       UNetaDModel.from_pretrained(__a	)
        _UpperCamelCase      :	Optional[Any]						=       DDIMScheduler.from_pretrained(__a	)

        _UpperCamelCase      :	int						=       DDIMPipeline(unet=__a					,   scheduler=__a	)
        ddpm.to(__a	)
        ddpm.set_progress_bar_config(disable=__a	)

        _UpperCamelCase      :	int						=       torch.manual_seed(0	)
        _UpperCamelCase      :	Tuple						=       ddpm(generator=__a					,   output_type="numpy"	).images

        _UpperCamelCase      :	List[Any]						=       image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        _UpperCamelCase      :	int						=       np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







"""simple docstring"""


from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCamelCase__				  =   TypeVar("KEY")
lowerCamelCase__				  =   TypeVar("VAL")



@dataclass(frozen=_UpperCamelCase		,  slots=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						Generic[KEY, VAL]   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:KEY
  SCREAMING_SNAKE_CASE__							:VAL





class      __SCREAMING_SNAKE_CASE    (						_Item   ):







  '''simple docstring'''


  def __init__(		self   :       List[str]	)		->				None:
        super().__init__(__a					,   __a	)


  def __bool__(		self   :       Dict	)		->				bool:
        return False





lowerCamelCase__				  =   _DeletedItem()



class      __SCREAMING_SNAKE_CASE    (						MutableMapping[KEY, VAL]   ):







  '''simple docstring'''


  def __init__(		self   :       int					,   __a   :       int = 8					,   __a   :       float = 0.75	)		->				None:
        _UpperCamelCase      :	str						=       initial_block_size
        _UpperCamelCase      :	list[_Item | None]						=       [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        _UpperCamelCase      :	List[str]						=       capacity_factor
        _UpperCamelCase      :	Dict						=       0

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       KEY	)		->				int:
        return hash(__a	) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       int	)		->				int:
        return (ind + 1) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				bool:
        _UpperCamelCase      :	List[Any]						=       self._buckets[ind]
        if not stored:
              _UpperCamelCase      :	Tuple						=       _Item(__a					,   __a	)
              self._len += 1
              return True
        elif stored.key == key:
              _UpperCamelCase      :	Union[str, Any]						=       _Item(__a					,   __a	)
              return True
        else:
              return False

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				bool:
        _UpperCamelCase      :	Any						=       len(self._buckets	) * self._capacity_factor
        return len(self	) >= int(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				bool:
        if len(self._buckets	) <= self._initial_block_size:
              return False
        _UpperCamelCase      :	List[str]						=       len(self._buckets	) * self._capacity_factor / 2
        return len(self	) < limit

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       int	)		->				None:
        _UpperCamelCase      :	Any						=       self._buckets
        _UpperCamelCase      :	List[Any]						=       [None] * new_size
        _UpperCamelCase      :	List[str]						=       0
        for item in old_buckets:
              if item:
                    self._add_item(item.key					,   item.val	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				None:
        self._resize(len(self._buckets	) * 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				None:
        self._resize(len(self._buckets	) // 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       KEY	)		->				Iterator[int]:
        _UpperCamelCase      :	str						=       self._get_bucket_index(__a	)
        for _ in range(len(self._buckets	)	):
              yield ind
              _UpperCamelCase      :	Tuple						=       self._get_next_ind(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        for ind in self._iterate_buckets(__a	):
              if self._try_set(__a					,   __a					,   __a	):
                    break

  def __setitem__(		self   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        if self._is_full():
              self._size_up()

        self._add_item(__a					,   __a	)

  def __delitem__(		self   :       str					,   __a   :       KEY	)		->				None:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    raise KeyError(__a	)
              if item is _deleted:
                    continue
              if item.key == key:
                    _UpperCamelCase      :	List[Any]						=       _deleted
                    self._len -= 1
                    break
        if self._is_sparse():
              self._size_down()

  def __getitem__(		self   :       str					,   __a   :       KEY	)		->				VAL:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    break
              if item is _deleted:
                    continue
              if item.key == key:
                    return item.val
        raise KeyError(__a	)

  def __len__(		self   :       List[Any]	)		->				int:
        return self._len

  def __iter__(		self   :       List[str]	)		->				Iterator[KEY]:
        yield from (item.key for item in self._buckets if item)


  def __repr__(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       " ,".join(
            F'''{item.key}: {item.val}''' for item in self._buckets if item	)
        return F'''HashMap({val_string})'''
