



"""simple docstring"""







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

A		=						TypeVar('KEY')
A		=						TypeVar('VAL')

@dataclass(frozen=SCREAMING_SNAKE_CASE__     ,	slots=SCREAMING_SNAKE_CASE__)
class       _a  ( Generic[KEY, VAL]):
			__magic_name__     					=     42
			__magic_name__     					=     42



class       _a  ( _Item):



			def __init__(			self							:				int		)							->					None:
									super().__init__(_lowercase   ,	_lowercase		)



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



A		=						_DeletedItem()

class       _a  ( MutableMapping[KEY, VAL]):



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



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				KEY		)							->					int:
									return hash(_lowercase		) % len(self._buckets		)



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				int		)							->					int:
									return (ind + 1) % len(self._buckets		)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				int   ,	_lowercase							:				KEY   ,	_lowercase							:				VAL		)							->					bool:
									snake_case   :			Union[str, Any]						    = self._buckets[ind]
									if not stored:
															snake_case   :			Optional[Any]						    = _Item(_lowercase   ,	_lowercase		)
															self._len += 1
															return True
									elif stored.key == key:
															snake_case   :			Optional[int]						    = _Item(_lowercase   ,	_lowercase		)
															return True
									else:
															return False



			def 				__lowercase					(			self							:				Tuple		)							->					bool:
									snake_case   :			Optional[Any]						    = len(self._buckets		) * self._capacity_factor
									return len(self		) >= int(_lowercase		)



			def 				__lowercase					(			self							:				int		)							->					bool:
									if len(self._buckets		) <= self._initial_block_size:
															return False
									snake_case   :			Optional[Any]						    = len(self._buckets		) * self._capacity_factor / 2
									return len(self		) < limit



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				int		)							->					None:
									snake_case   :			List[Any]						    = self._buckets
									snake_case   :			Tuple						    = [None] * new_size
									snake_case   :			List[str]						    = 0
									for item in old_buckets:
															if item:
																					self._add_item(item.key   ,	item.val		)



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



			def 				__lowercase					(			self							:				Any		)							->					None:
									self._resize(len(self._buckets		) // 2		)



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				KEY		)							->					Iterator[int]:
									snake_case   :			List[Any]						    = self._get_bucket_index(_lowercase		)
									for _ in range(len(self._buckets		)		):
															yield ind
															snake_case   :			Any						    = self._get_next_ind(_lowercase		)



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				KEY   ,	_lowercase							:				VAL		)							->					None:
									for ind in self._iterate_buckets(_lowercase		):
															if self._try_set(_lowercase   ,	_lowercase   ,	_lowercase		):
																					break



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

									self._add_item(_lowercase   ,	_lowercase		)



			def __delitem__(			self							:				Optional[int]   ,	_lowercase							:				KEY		)							->					None:
									for ind in self._iterate_buckets(_lowercase		):
															snake_case   :			Dict						    = self._buckets[ind]
															if item is None:
																					raise KeyError(_lowercase		)
															if item is _deleted:
																					continue
															if item.key == key:
																					snake_case   :			Any						    = _deleted
																					self._len -= 1
																					break
									if self._is_sparse():
															self._size_down()



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



			def __len__(			self							:				Union[str, Any]		)							->					int:
									return self._len



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



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








"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = VQModel(
									    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1E-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModel(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									snake_case   :			Tuple						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			Any						    = image[0, -3:, -3:, -1]
									snake_case   :			Any						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



