





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


_snake_case  :    List[Any]						    =  False



class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


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






  @property
  def 				lowercase		(				self :		str     )							->						Any:
     return 1_2






  @property
  def 				lowercase		(				self :		List[str]     )							->						str:
     return 1_2






  @property
  def 				lowercase		(				self :		Tuple     )							->						Optional[int]:
     return 3_2






  @property
  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	VQModel(
         block_out_channels=[3_2, 6_4]					,  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[str]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
     return tokenizer






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						str:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	CLIPTextConfig(
         bos_token_id=0					,  eos_token_id=2					,  hidden_size=self.text_embedder_hidden_size					,  intermediate_size=3_7					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1_0_0_0					,  )
     return CLIPTextModel(lowerCAmelCase_     )






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

     __lowerCAmelCase								=	1_2
     __lowerCAmelCase								=	1_2

     __lowerCAmelCase								=	{
         'attention_bias': True,
         'cross_attention_dim': 3_2,
         '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': 3_2,
         'sample_size': width,
         'activation_fn': 'geglu-approximate',
     }

     __lowerCAmelCase								=	TransformeraDModel(**lowerCAmelCase_     )
     return model






  def 				lowercase		(				self :		Optional[Any]     )							->						Tuple:
     __lowerCAmelCase								=	'cpu'

     __lowerCAmelCase								=	self.dummy_vqvae
     __lowerCAmelCase								=	self.dummy_text_encoder
     __lowerCAmelCase								=	self.dummy_tokenizer
     __lowerCAmelCase								=	self.dummy_transformer
     __lowerCAmelCase								=	VQDiffusionScheduler(self.num_embed     )
     __lowerCAmelCase								=	LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase_     )

     __lowerCAmelCase								=	VQDiffusionPipeline(
         vqvae=lowerCAmelCase_					,  text_encoder=lowerCAmelCase_					,  tokenizer=lowerCAmelCase_					,  transformer=lowerCAmelCase_					,  scheduler=lowerCAmelCase_					,  learned_classifier_free_sampling_embeddings=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	'teddy bear playing in the pool'

     __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
     __lowerCAmelCase								=	pipe([prompt]					,  generator=lowerCAmelCase_					,  num_inference_steps=2					,  output_type='np'     )
     __lowerCAmelCase								=	output.images

     __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
     __lowerCAmelCase								=	pipe(
         [prompt]					,  generator=lowerCAmelCase_					,  output_type='np'					,  return_dict=lowerCAmelCase_					,  num_inference_steps=2     )[0]

     __lowerCAmelCase								=	image[0, -3:, -3:, -1]
     __lowerCAmelCase								=	image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 2_4, 2_4, 3)

     __lowerCAmelCase								=	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 				lowercase		(				self :		List[Any]     )							->						int:
     __lowerCAmelCase								=	'cpu'

     __lowerCAmelCase								=	self.dummy_vqvae
     __lowerCAmelCase								=	self.dummy_text_encoder
     __lowerCAmelCase								=	self.dummy_tokenizer
     __lowerCAmelCase								=	self.dummy_transformer
     __lowerCAmelCase								=	VQDiffusionScheduler(self.num_embed     )
     __lowerCAmelCase								=	LearnedClassifierFreeSamplingEmbeddings(
         learnable=lowerCAmelCase_					,  hidden_size=self.text_embedder_hidden_size					,  length=tokenizer.model_max_length     )

     __lowerCAmelCase								=	VQDiffusionPipeline(
         vqvae=lowerCAmelCase_					,  text_encoder=lowerCAmelCase_					,  tokenizer=lowerCAmelCase_					,  transformer=lowerCAmelCase_					,  scheduler=lowerCAmelCase_					,  learned_classifier_free_sampling_embeddings=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	'teddy bear playing in the pool'

     __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
     __lowerCAmelCase								=	pipe([prompt]					,  generator=lowerCAmelCase_					,  num_inference_steps=2					,  output_type='np'     )
     __lowerCAmelCase								=	output.images

     __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
     __lowerCAmelCase								=	pipe(
         [prompt]					,  generator=lowerCAmelCase_					,  output_type='np'					,  return_dict=lowerCAmelCase_					,  num_inference_steps=2     )[0]

     __lowerCAmelCase								=	image[0, -3:, -3:, -1]
     __lowerCAmelCase								=	image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 2_4, 2_4, 3)

     __lowerCAmelCase								=	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 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


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






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

     __lowerCAmelCase								=	VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'     )
     __lowerCAmelCase								=	pipeline.to(lowerCAmelCase_     )
     pipeline.set_progress_bar_config(disable=lowerCAmelCase_     )

     # requires GPU generator for gumbel softmax
     # don't use GPU generator in tests though
     __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
     __lowerCAmelCase								=	pipeline(
         'teddy bear playing in the pool'					,  num_images_per_prompt=1					,  generator=lowerCAmelCase_					,  output_type='np'					,  )

     __lowerCAmelCase								=	output.images[0]

     assert image.shape == (2_5_6, 2_5_6, 3)
     assert np.abs(expected_image - image     ).max() < 2.0








from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .scheduling_consistency_models import CMStochasticIterativeScheduler
     from .scheduling_ddim import DDIMScheduler
     from .scheduling_ddim_inverse import DDIMInverseScheduler
     from .scheduling_ddim_parallel import DDIMParallelScheduler
     from .scheduling_ddpm import DDPMScheduler
     from .scheduling_ddpm_parallel import DDPMParallelScheduler
     from .scheduling_deis_multistep import DEISMultistepScheduler
     from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
     from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
     from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
     from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
     from .scheduling_euler_discrete import EulerDiscreteScheduler
     from .scheduling_heun_discrete import HeunDiscreteScheduler
     from .scheduling_ipndm import IPNDMScheduler
     from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
     from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler
     from .scheduling_repaint import RePaintScheduler
     from .scheduling_sde_ve import ScoreSdeVeScheduler
     from .scheduling_sde_vp import ScoreSdeVpScheduler
     from .scheduling_unclip import UnCLIPScheduler
     from .scheduling_unipc_multistep import UniPCMultistepScheduler
     from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
     from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .scheduling_ddim_flax import FlaxDDIMScheduler
     from .scheduling_ddpm_flax import FlaxDDPMScheduler
     from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
     from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
     from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
     from .scheduling_pndm_flax import FlaxPNDMScheduler
     from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
     from .scheduling_utils_flax import (
         FlaxKarrasDiffusionSchedulers,
         FlaxSchedulerMixin,
         FlaxSchedulerOutput,
         broadcast_to_shape_from_left,
     )


try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

