'''simple docstring'''




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__	(							_snake_case      ):






   """simple docstring"""
   def __init__(self							,					_a							,					_a							,					_a							,					_a							,					_a							,					)     ->  Any:
        super().__init__()

        self.register_modules(vae=_a							,					text_encoder=_a							,					tokenizer=_a							,					unet=_a							,					scheduler=_a					)
   def 		_lowerCamelCase			(self							,					_a					)     ->  Optional[Any]:
        with torch.no_grad():
             lowercase_       :							Tuple  					=       self.vae.encode(tfms.ToTensor()(_a					).unsqueeze(0					).to(self.device					) * 2 - 1					)
             lowercase_       :							str  					=       0.1_82_15 * latent.latent_dist.sample()
        return latent
   def 		_lowerCamelCase			(self							,					_a					)     ->  Dict:
        lowercase_       :							Tuple  					=       (1 / 0.1_82_15) * latent
        with torch.no_grad():
             lowercase_       :							List[Any]  					=       self.vae.decode(_a					).sample
        lowercase_       :							Any  					=       (img / 2 + 0.5).clamp(0							,					1					)
        lowercase_       :							Any  					=       img.detach().cpu().permute(0							,					2							,					3							,					1					).numpy()
        lowercase_       :							Dict  					=       (img * 255).round().astype('uint8'					)
        return Image.fromarray(img[0]					)
   def 		_lowerCamelCase			(self							,					_a					)     ->  Tuple:
        lowercase_       :							int  					=       self.tokenizer(
            _a							,					padding='max_length'							,					max_length=self.tokenizer.model_max_length							,					truncation=_a							,					return_tensors='pt'							,					)

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

        lowercase_       :							Optional[Any]  					=       self.tokenizer(
            ''							,					padding='max_length'							,					max_length=self.tokenizer.model_max_length							,					truncation=_a							,					return_tensors='pt'							,					)

        lowercase_       :							Any  					=       self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

        return torch.cat([uncond_embedding, text_embedding]					)





   def __call__(self							,					_a							,					_a							,					_a = 0.3							,					_a = 0.6							,					_a = 0.5							,					_a = 42							,					_a = 50							,					_a = 7.5							,					)     ->  Image.Image:
        lowercase_       :							Dict  					=       steps - int(kmin * steps					)
        lowercase_       :							Dict  					=       steps - int(kmax * steps					)

        lowercase_       :							Any  					=       self.prep_text(_a					)

        self.scheduler.set_timesteps(_a					)

        lowercase_					,lowercase_       :							Any  					=       img.size
        lowercase_       :							List[Any]  					=       self.encode(_a					)

        torch.manual_seed(_a					)
        lowercase_       :							List[str]  					=       torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)							,					).to(self.device					)

        lowercase_       :							Optional[Any]  					=       self.scheduler.add_noise(
            _a							,					_a							,					timesteps=self.scheduler.timesteps[tmax]							,					)

        lowercase_       :							Optional[Any]  					=       torch.cat([latents] * 2					)

        lowercase_       :							List[str]  					=       self.scheduler.scale_model_input(_a							,					self.scheduler.timesteps[tmax]					)

        with torch.no_grad():
             lowercase_       :							List[Any]  					=       self.unet(
                 _a							,					self.scheduler.timesteps[tmax]							,					encoder_hidden_states=_a							,					).sample

        lowercase_					,lowercase_       :							Tuple  					=       pred.chunk(2					)
        lowercase_       :							Dict  					=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowercase_       :							List[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
                       lowercase_       :							Union[str, Any]  					=       self.scheduler.add_noise(
                           _a							,					_a							,					timesteps=_a							,					)

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

                  else:  # content generation phase
                       lowercase_       :							Union[str, Any]  					=       torch.cat([latents] * 2					)

                  lowercase_       :							Any  					=       self.scheduler.scale_model_input(_a							,					_a					)

                  with torch.no_grad():
                       lowercase_       :							Optional[int]  					=       self.unet(
                           _a							,					_a							,					encoder_hidden_states=_a							,					).sample

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

                  lowercase_       :							Dict  					=       self.scheduler.step(_a							,					_a							,					_a					).prev_sample

        return self.decode(_a					)





'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_A										= get_logger(__name__)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if version.parse(SCREAMING_SNAKE_CASE_				) < version.parse(SCREAMING_SNAKE_CASE_				):
          if "dev" in min_version:
               lowercase_       :							Tuple  					=       (
                   'This example requires a source install from HuggingFace diffusers (see '
                   '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
               )
          else:
               lowercase_       :							List[Any]  					=       f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(SCREAMING_SNAKE_CASE_				)




