


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	42
 SCREAMING_SNAKE_CASE__				=	42




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       super().__init__()
       self.register_modules(unet=_lowerCamelCase ,  scheduler=_lowerCamelCase   )




 @torch.no_grad()
 def __call__(		self ,  _lowerCamelCase = 1 ,  _lowerCamelCase = 50 ,  _lowerCamelCase = None ,  _lowerCamelCase = "pil" ,  _lowerCamelCase = True ,  **_lowerCamelCase ,  ):
       a						:Optional[Any]										=	self.unet.config.sample_size
       a						:List[str]										=	(batch_size, 3, img_size, img_size)

       a						:int										=	self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       a						:int										=	randn_tensor(_lowerCamelCase ,  generator=_lowerCamelCase ,  device=self.device   ) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_lowerCamelCase   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
             # here sigma_t == t_i from the paper
             a						:Dict										=	self.scheduler.schedule[t]
             a						:List[Any]										=	self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             a							,    a						:Tuple										=	self.scheduler.add_noise_to_input(_lowerCamelCase ,  _lowerCamelCase ,  generator=_lowerCamelCase   )

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             a						:Tuple										=	(sigma_hat / 2) * model((sample_hat + 1) / 2 ,  sigma_hat / 2   ).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             a						:str										=	self.scheduler.step(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   a						:Optional[Any]										=	(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 ,  sigma_prev / 2   ).sample
                   a						:Dict										=	self.scheduler.step_correct(
                       _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  step_output.prev_sample ,  step_output['''derivative'''] ,  )
             a						:Union[str, Any]										=	step_output.prev_sample

       a						:Tuple										=	(sample / 2 + 0.5).clamp(0 ,  1   )
       a						:Tuple										=	sample.cpu().permute(0 ,  2 ,  3 ,  1   ).numpy()
       if output_type == "pil":
             a						:Optional[Any]										=	self.numpy_to_pil(_lowerCamelCase   )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase   )









from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





