

'''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


UpperCamelCase_							=					False





class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										return 12







				@property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return 12







				@property
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										return 32







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







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										torch.manual_seed(0      )
										lowercase   :       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(UpperCAmelCase__      )







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										torch.manual_seed(0      )

										lowercase   :       Tuple								=12
										lowercase   :       Optional[Any]								=12

										lowercase   :       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''',
										}

										lowercase   :       List[Any]								=TransformeraDModel(**UpperCAmelCase__      )
										return model







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       int								='''cpu'''

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

										lowercase   :       str								=VQDiffusionPipeline(
										    vqvae=UpperCAmelCase__		,				text_encoder=UpperCAmelCase__		,				tokenizer=UpperCAmelCase__		,				transformer=UpperCAmelCase__		,				scheduler=UpperCAmelCase__		,				learned_classifier_free_sampling_embeddings=UpperCAmelCase__		,				)
										lowercase   :       Dict								=pipe.to(UpperCAmelCase__      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       Tuple								='''teddy bear playing in the pool'''

										lowercase   :       Any								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(0      )
										lowercase   :       Dict								=pipe([prompt]		,				generator=UpperCAmelCase__		,				num_inference_steps=2		,				output_type='''np'''      )
										lowercase   :       List[str]								=output.images

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

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

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

										lowercase   :       str								=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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Dict								='''cpu'''

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

										lowercase   :       Dict								=VQDiffusionPipeline(
										    vqvae=UpperCAmelCase__		,				text_encoder=UpperCAmelCase__		,				tokenizer=UpperCAmelCase__		,				transformer=UpperCAmelCase__		,				scheduler=UpperCAmelCase__		,				learned_classifier_free_sampling_embeddings=UpperCAmelCase__		,				)
										lowercase   :       int								=pipe.to(UpperCAmelCase__      )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

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

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

										lowercase   :       Optional[int]								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(0      )
										lowercase   :       Dict								=pipe(
										    [prompt]		,				generator=UpperCAmelCase__		,				output_type='''np'''		,				return_dict=UpperCAmelCase__		,				num_inference_steps=2      )[0]

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

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

										lowercase   :       List[Any]								=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 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''      )

										lowercase   :       Optional[Any]								=VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''      )
										lowercase   :       int								=pipeline.to(UpperCAmelCase__      )
										pipeline.set_progress_bar_config(disable=UpperCAmelCase__      )

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

										lowercase   :       Optional[Any]								=output.images[0]

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








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





