
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowercase_											=	logging.get_logger(__name__)


def     a__			(    snake_case   ,	snake_case = True    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Any			=						{}
  for name in model.attn_processors.keys():
    __SCREAMING_SNAKE_CASE     :					str			=						name.endswith('''attn2.processor'''    ) and not (
        name.startswith('''transformer_in'''    ) or '''temp_attentions''' in name.split('''.'''    )
    )
    __SCREAMING_SNAKE_CASE     :					int			=						model.config.cross_attention_dim if has_cross_attention else None
    if name.startswith('''mid_block'''    ):
      __SCREAMING_SNAKE_CASE     :					List[str]			=						model.config.block_out_channels[-1]
    elif name.startswith('''up_blocks'''    ):
      __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						int(name[len('''up_blocks.'''    )]    )
      __SCREAMING_SNAKE_CASE     :					List[Any]			=						list(reversed(model.config.block_out_channels    )    )[block_id]
    elif name.startswith('''down_blocks'''    ):
      __SCREAMING_SNAKE_CASE     :					Dict			=						int(name[len('''down_blocks.'''    )]    )
      __SCREAMING_SNAKE_CASE     :					Any			=						model.config.block_out_channels[block_id]
    elif name.startswith('''transformer_in'''    ):
      # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
      __SCREAMING_SNAKE_CASE     :					List[Any]			=						8 * model.config.attention_head_dim

    __SCREAMING_SNAKE_CASE     :					Dict			=						LoRAAttnProcessor(hidden_size=snake_case   ,	cross_attention_dim=snake_case    )
    __SCREAMING_SNAKE_CASE     :					List[str]			=						lora_attn_procs[name].to(model.device    )

    if mock_weights:
      # add 1 to weights to mock trained weights
      with torch.no_grad():
        lora_attn_procs[name].to_q_lora.up.weight += 1
        lora_attn_procs[name].to_k_lora.up.weight += 1
        lora_attn_procs[name].to_v_lora.up.weight += 1
        lora_attn_procs[name].to_out_lora.up.weight += 1

  return lora_attn_procs





@skip_mps
class   __UpperCamelCase				(    lowerCAmelCase__				,		lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	UNetaDConditionModel
     lowerCAmelCase_     =	'''sample'''



     @property
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						4
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						4
       __SCREAMING_SNAKE_CASE     :					int			=						4
       __SCREAMING_SNAKE_CASE     :					Tuple			=						(32, 32)

       __SCREAMING_SNAKE_CASE     :					str			=						floats_tensor((batch_size, num_channels, num_frames) + sizes				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor([10]				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						floats_tensor((batch_size, 4, 32)				).to(_A				)

       return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return (4, 4, 32, 32)



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       return (4, 4, 32, 32)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						{
           '''block_out_channels''': (32, 64),
           '''down_block_types''': (
               '''CrossAttnDownBlock3D''',
               '''DownBlock3D''',
           ),
           '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
           '''cross_attention_dim''': 32,
           '''attention_head_dim''': 8,
           '''out_channels''': 4,
           '''in_channels''': 4,
           '''layers_per_block''': 1,
           '''sample_size''': 32,
       }
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.dummy_input
       return init_dict, inputs_dict



     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()      ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,		)
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE     :					Any			=						self.model_class(**_A				)

       model.enable_xformers_memory_efficient_attention()

       assert (
           model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
           == "XFormersAttnProcessor"
       ), "xformers is not enabled"



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[Any]			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Any			=						32

       __SCREAMING_SNAKE_CASE     :					str			=						self.model_class(**_A				)
       model.to(_A				)
       model.eval()

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(**_A				)

         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						output.sample

       self.assertIsNotNone(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						inputs_dict['''sample'''].shape
       self.assertEqual(output.shape      ,		_A      ,		'''Input and output shapes do not match'''				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE     :					Any			=						self.model_class(**_A				)
       model.to(_A				)
       model.eval()

       with torch.no_grad():
         # Warmup pass when using mps (see #372)
         if torch_device == "mps" and isinstance(_A      ,		_A				):
           model(**self.dummy_input				)

         __SCREAMING_SNAKE_CASE     :					int			=						model(**_A				)
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					int			=						first.sample

         __SCREAMING_SNAKE_CASE     :					int			=						model(**_A				)
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						second.sample

       __SCREAMING_SNAKE_CASE     :					Dict			=						first.cpu().numpy()
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						second.cpu().numpy()
       __SCREAMING_SNAKE_CASE     :					int			=						out_a[~np.isnan(_A				)]
       __SCREAMING_SNAKE_CASE     :					int			=						out_a[~np.isnan(_A				)]
       __SCREAMING_SNAKE_CASE     :					Any			=						np.amax(np.abs(out_a - out_a				)				)
       self.assertLessEqual(_A      ,		1e-5				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						8

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.model_class(**_A				)
       model.to(_A				)
       model.eval()

       model.set_attention_slice('''auto'''				)
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						model(**_A				)
       assert output is not None

       model.set_attention_slice('''max'''				)
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model(**_A				)
       assert output is not None

       model.set_attention_slice(2				)
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(**_A				)
       assert output is not None



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					int			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						8

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.model_class(**_A				)
       model.to(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					str			=						model(**_A				).sample

       __SCREAMING_SNAKE_CASE     :					Tuple			=						create_lora_layers(_A				)

       # make sure we can set a list of attention processors
       model.set_attn_processor(_A				)
       model.to(_A				)

       # test that attn processors can be set to itself
       model.set_attn_processor(model.attn_processors				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					str			=						model(**_A      ,		cross_attention_kwargs={'''scale''': 0.0}				).sample
         __SCREAMING_SNAKE_CASE     :					Any			=						model(**_A      ,		cross_attention_kwargs={'''scale''': 0.5}				).sample
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						model(**_A      ,		cross_attention_kwargs={'''scale''': 0.5}				).sample

       assert (samplea - samplea).abs().max() < 3e-3
       assert (samplea - samplea).abs().max() < 3e-3

       # sample 2 and sample 3 should be different
       assert (samplea - samplea).abs().max() > 3e-3



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					int			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						8

       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.model_class(**_A				)
       model.to(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(**_A				).sample

       __SCREAMING_SNAKE_CASE     :					Tuple			=						create_lora_layers(_A				)
       model.set_attn_processor(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						model(**_A      ,		cross_attention_kwargs={'''scale''': 0.5}				).sample

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_attn_procs(_A				)
         self.assertTrue(os.path.isfile(os.path.join(_A      ,		'''pytorch_lora_weights.bin'''				)				)				)
         torch.manual_seed(0				)
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.model_class(**_A				)
         new_model.to(_A				)
         new_model.load_attn_procs(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						new_model(**_A      ,		cross_attention_kwargs={'''scale''': 0.5}				).sample

       assert (sample - new_sample).abs().max() < 1e-3

       # LoRA and no LoRA should NOT be the same
       assert (sample - old_sample).abs().max() > 1e-4



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Any			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Tuple			=						8

       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.model_class(**_A				)
       model.to(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Tuple			=						model(**_A				).sample

       __SCREAMING_SNAKE_CASE     :					Dict			=						create_lora_layers(_A				)
       model.set_attn_processor(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					str			=						model(**_A      ,		cross_attention_kwargs={'''scale''': 0.5}				).sample

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_attn_procs(_A      ,		safe_serialization=_A				)
         self.assertTrue(os.path.isfile(os.path.join(_A      ,		'''pytorch_lora_weights.safetensors'''				)				)				)
         torch.manual_seed(0				)
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.model_class(**_A				)
         new_model.to(_A				)
         new_model.load_attn_procs(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						new_model(**_A      ,		cross_attention_kwargs={'''scale''': 0.5}				).sample

       assert (sample - new_sample).abs().max() < 3e-3

       # LoRA and no LoRA should NOT be the same
       assert (sample - old_sample).abs().max() > 1e-4



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						8

       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_class(**_A				)
       model.to(_A				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						create_lora_layers(_A      ,		mock_weights=_A				)
       model.set_attn_processor(_A				)
       # Saving as torch, properly reloads with directly filename
       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_attn_procs(_A				)
         self.assertTrue(os.path.isfile(os.path.join(_A      ,		'''pytorch_lora_weights.bin'''				)				)				)
         torch.manual_seed(0				)
         __SCREAMING_SNAKE_CASE     :					Any			=						self.model_class(**_A				)
         new_model.to(_A				)
         new_model.load_attn_procs(_A      ,		weight_name='''pytorch_lora_weights.bin'''				)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						8

       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.model_class(**_A				)
       model.to(_A				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						create_lora_layers(_A      ,		mock_weights=_A				)
       model.set_attn_processor(_A				)
       # Saving as torch, properly reloads with directly filename
       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_attn_procs(_A				)
         self.assertTrue(os.path.isfile(os.path.join(_A      ,		'''pytorch_lora_weights.bin'''				)				)				)
         torch.manual_seed(0				)
         __SCREAMING_SNAKE_CASE     :					str			=						self.model_class(**_A				)
         new_model.to(_A				)
         with self.assertRaises(_A				) as e:
           new_model.load_attn_procs(_A      ,		use_safetensors=_A				)
         self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''      ,		str(e.exception				)				)



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Any			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						8

       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					int			=						self.model_class(**_A				)
       model.to(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(**_A				).sample

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						create_lora_layers(_A				)
       model.set_attn_processor(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						model(**_A      ,		cross_attention_kwargs={'''scale''': 0.0}				).sample

       model.set_attn_processor(AttnProcessor()				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(**_A				).sample

       assert (sample - new_sample).abs().max() < 1e-4
       assert (sample - old_sample).abs().max() < 3e-3



     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()      ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,		)
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Any			=						self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE     :					Any			=						4

       torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_class(**_A				)
       model.to(_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						create_lora_layers(_A				)
       model.set_attn_processor(_A				)

       # default
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(**_A				).sample

         model.enable_xformers_memory_efficient_attention()
         __SCREAMING_SNAKE_CASE     :					str			=						model(**_A				).sample

         model.disable_xformers_memory_efficient_attention()
         __SCREAMING_SNAKE_CASE     :					Tuple			=						model(**_A				).sample

       assert (sample - on_sample).abs().max() < 1e-4
       assert (sample - off_sample).abs().max() < 1e-4




     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE     :					Tuple			=						32

       __SCREAMING_SNAKE_CASE     :					str			=						self.model_class(**_A				)
       model.to(_A				)
       model.eval()

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						model(**_A				)[0]

       model.enable_forward_chunking()
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model(**_A				)[0]

       self.assertEqual(output.shape      ,		output_a.shape      ,		'''Shape doesn\'t match'''				)
       assert np.abs(output.cpu() - output_a.cpu()				).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    AutoencoderKL      ,		_A    :    CLIPTextModel      ,		_A    :    CLIPTokenizer      ,		_A    :    UNetaDConditionModel      ,		_A    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		):







       """simple docstring"""




       super().__init__()

       self.register_modules(vae=_A      ,		text_encoder=_A      ,		tokenizer=_A      ,		unet=_A      ,		scheduler=_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Dict				):







       """simple docstring"""




       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.vae.encode(tfms.ToTensor()(_A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						0.1_82_15 * latent.latent_dist.sample()
       return latent



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						self.vae.decode(_A				).sample
       __SCREAMING_SNAKE_CASE     :					Any			=						(img / 2 + 0.5).clamp(0      ,		1				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						img.detach().cpu().permute(0      ,		2      ,		3      ,		1				).numpy()
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(img * 255).round().astype('''uint8'''				)
       return Image.fromarray(img[0]				)



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tokenizer(
           _A      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.tokenizer(
           ''''''      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					str			=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

       return torch.cat([uncond_embedding, text_embedding]				)




     def __call__(							self    :    Union[str, Any]      ,		_A    :    Image.Image      ,		_A    :    str      ,		_A    :    float = 0.3      ,		_A    :    float = 0.6      ,		_A    :    float = 0.5      ,		_A    :    int = 42      ,		_A    :    int = 50      ,		_A    :    float = 7.5      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						steps - int(kmin * steps				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						steps - int(kmax * steps				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.prep_text(_A				)

       self.scheduler.set_timesteps(_A				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						img.size
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.encode(_A				)

       torch.manual_seed(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)      ,		).to(self.device				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.add_noise(
           _A      ,		_A      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([latents] * 2				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.scheduler.scale_model_input(_A      ,		self.scheduler.timesteps[tmax]				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.unet(
             _A      ,		self.scheduler.timesteps[tmax]      ,		encoder_hidden_states=_A      ,		).sample

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						pred.chunk(2				)
       __SCREAMING_SNAKE_CASE     :					str			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

       __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		self.scheduler.timesteps[tmax]      ,		_A				).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps				)				):
         if i > tmax:
           if i < tmin:  # layout generation phase
             __SCREAMING_SNAKE_CASE     :					List[str]			=						self.scheduler.add_noise(
                 _A      ,		_A      ,		timesteps=_A      ,		)

             __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						(mix_factor * latents) + (
                 1 - mix_factor
             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
             __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.cat([input] * 2				)

           else:  # content generation phase
             __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([latents] * 2				)

           __SCREAMING_SNAKE_CASE     :					str			=						self.scheduler.scale_model_input(_A      ,		_A				)

           with torch.no_grad():
             __SCREAMING_SNAKE_CASE     :					int			=						self.unet(
                 _A      ,		_A      ,		encoder_hidden_states=_A      ,		).sample

           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred.chunk(2				)
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

           __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		_A      ,		_A				).prev_sample

       return self.decode(_A				)
