"""simple docstring"""







_a					:					str=			[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

_a					:					int=			[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

_a					:					Union[str, Any]=			[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

_a					:					Any=			[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

_a					:					Optional[int]=			[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

_a					:					Optional[Any]=			[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

_a					:					Union[str, Any]=			[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

_a					:					str=			[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]






"""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					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



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



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					torch.manual_seed(0)
					__snake_case      :							Any						=			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    :				List[Any])   ->     Optional[int]:
					__snake_case      :							Dict						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					torch.manual_seed(0)
					__snake_case      :							Any						=			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=10_00     ,  )
					return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					torch.manual_seed(0)

					__snake_case      :							Any						=			12
					__snake_case      :							int						=			12

					__snake_case      :							List[Any]						=			{
					    '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      :							Union[str, Any]						=			TransformeraDModel(**_A)
					return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					__snake_case      :							Tuple						=			'cpu'

					__snake_case      :							List[str]						=			self.dummy_vqvae
					__snake_case      :							str						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Dict						=			self.dummy_transformer
					__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

					__snake_case      :							List[Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							List[Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Optional[Any]						=			'teddy bear playing in the pool'

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[int]						=			output.images

					__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							str						=			image[0, -3:, -3:, -1]
					__snake_case      :							Union[str, Any]						=			image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case      :							str						=			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

					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    :				Tuple)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			'cpu'

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

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							Union[str, Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[Any]						=			output.images

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Dict						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							int						=			image[0, -3:, -3:, -1]
					__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case      :							Optional[Any]						=			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

					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 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							List[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      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
					__snake_case      :							Tuple						=			pipeline.to(_A)
					pipeline.set_progress_bar_config(disable=_A)

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

					__snake_case      :							int						=			output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).max() < 2.0





