






'''simple docstring'''




def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     list[str]:
		return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_) - ngram_size + 1)]


if __name__ == "__main__":
		from doctest import testmod

		testmod()












'''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


lowerCAmelCase__          =						False







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	@property
	def      __UpperCamelCase							(      self   :						int):
			return 12

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 12

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return 32

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

	@property
	def      __UpperCamelCase							(      self   :						List[str]):
			torch.manual_seed(0)
			UpperCamelCase__				:					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=1_000  ,					)
			return CLIPTextModel(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			torch.manual_seed(0)

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

			UpperCamelCase__				:					Union[str, 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',
			}

			UpperCamelCase__				:					Tuple					=					TransformeraDModel(**UpperCAmelCase_)
			return model

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					List[Any]					=					'cpu'

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

			UpperCamelCase__				:					int					=					VQDiffusionPipeline(
			    vqvae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					transformer=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					learned_classifier_free_sampling_embeddings=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					Optional[Any]					=					pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe([prompt]  ,					generator=UpperCAmelCase_  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					Optional[Any]					=					output.images

			UpperCamelCase__				:					int					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					num_inference_steps=2)[0]

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

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

			UpperCamelCase__				:					Any					=					np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92])

			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      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Optional[int]					=					'cpu'

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

			UpperCamelCase__				:					str					=					VQDiffusionPipeline(
			    vqvae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					transformer=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					learned_classifier_free_sampling_embeddings=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					str					=					pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					'teddy bear playing in the pool'

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

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					num_inference_steps=2)[0]

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

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

			UpperCamelCase__				:					str					=					np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88])

			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       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Any):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[Any]					=					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

			UpperCamelCase__				:					List[Any]					=					VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
			UpperCamelCase__				:					Any					=					pipeline.to(UpperCAmelCase_)
			pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

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

			UpperCamelCase__				:					int					=					output.images[0]

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




