
'''simple docstring'''
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_snake_case         =  8







def  _A		(     snake_case		,							snake_case=BITS					)      ->     int:
   _lowercase      :				List[str]      					=       x.device

   _lowercase      :				Tuple      					=       (x * 2_55).int().clamp(0		,							2_55					)

   _lowercase      :				int      					=       2 ** torch.arange(bits - 1		,							-1		,							-1		,							device=snake_case					)
   _lowercase      :				Dict      					=       rearrange(snake_case		,							"d -> d 1 1"					)
   _lowercase      :				List[str]      					=       rearrange(snake_case		,							"b c h w -> b c 1 h w"					)

   _lowercase      :				List[Any]      					=       ((x & mask) != 0).float()
   _lowercase      :				str      					=       rearrange(snake_case		,							"b c d h w -> b (c d) h w"					)
   _lowercase      :				List[str]      					=       bits * 2 - 1
   return bits







def  _A		(     snake_case		,							snake_case=BITS					)      ->     List[str]:
   _lowercase      :				List[Any]      					=       x.device

   _lowercase      :				int      					=       (x > 0).int()
   _lowercase      :				Optional[int]      					=       2 ** torch.arange(bits - 1		,							-1		,							-1		,							device=snake_case		,							dtype=torch.intaa					)

   _lowercase      :				int      					=       rearrange(snake_case		,							"d -> d 1 1"					)
   _lowercase      :				int      					=       rearrange(snake_case		,							"b (c d) h w -> b c d h w"		,							d=8					)
   _lowercase      :				Optional[int]      					=       reduce(x * mask		,							"b c d h w -> b c h w"		,							"sum"					)
   return (dec / 2_55).clamp(0.0		,							1.0					)







def  _A		(     self		,							snake_case		,							snake_case		,							snake_case		,							snake_case = 0.0		,							snake_case = True		,							snake_case=None		,							snake_case = True		,							)      ->     Any:
   if self.num_inference_steps is None:
      raise ValueError(
          "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"					)

   # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
   # Ideally, read DDIM paper in-detail understanding

   # Notation (<variable name> -> <name in paper>
   # - pred_noise_t -> e_theta(x_t, t)
   # - pred_original_sample -> f_theta(x_t, t) or x_0
   # - std_dev_t -> sigma_t
   # - eta -> η
   # - pred_sample_direction -> "direction pointing to x_t"
   # - pred_prev_sample -> "x_t-1"

   # 1. get previous step value (=t-1)
   _lowercase      :				Optional[int]      					=       timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   _lowercase      :				int      					=       self.alphas_cumprod[timestep]
   _lowercase      :				Tuple      					=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

   _lowercase      :				List[str]      					=       1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   _lowercase      :				Optional[int]      					=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   _lowercase      :				Tuple      					=       self.bit_scale
   if self.config.clip_sample:
      _lowercase      :				Any      					=       torch.clamp(snake_case		,							-scale		,							snake_case					)

   # 5. compute variance: "sigma_t(η)" -> see formula (16)
   # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
   _lowercase      :				str      					=       self._get_variance(snake_case		,							snake_case					)
   _lowercase      :				int      					=       eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      _lowercase      :				int      					=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

   # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   _lowercase      :				Optional[int]      					=       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

   # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   _lowercase      :				int      					=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if eta > 0:
      # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
      _lowercase      :				Optional[int]      					=       model_output.device if torch.is_tensor(snake_case					) else "cpu"
      _lowercase      :				int      					=       torch.randn(model_output.shape		,							dtype=model_output.dtype		,							generator=snake_case					).to(snake_case					)
      _lowercase      :				int      					=       self._get_variance(snake_case		,							snake_case					) ** 0.5 * eta * noise

      _lowercase      :				List[Any]      					=       prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=snake_case		,							pred_original_sample=snake_case					)







def  _A		(     self		,							snake_case		,							snake_case		,							snake_case		,							snake_case="epsilon"		,							snake_case=None		,							snake_case = True		,							)      ->     int:
   _lowercase      :				Optional[int]      					=       timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
      _lowercase      :				List[str]      					=       torch.split(snake_case		,							sample.shape[1]		,							dim=1					)
   else:
      _lowercase      :				List[Any]      					=       None

   # 1. compute alphas, betas
   _lowercase      :				Any      					=       self.alphas_cumprod[t]
   _lowercase      :				Optional[Any]      					=       self.alphas_cumprod[t - 1] if t > 0 else self.one
   _lowercase      :				int      					=       1 - alpha_prod_t
   _lowercase      :				str      					=       1 - alpha_prod_t_prev

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if prediction_type == "epsilon":
      _lowercase      :				int      					=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif prediction_type == "sample":
      _lowercase      :				Dict      					=       model_output
   else:
      raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''					)

   # 3. Clip "predicted x_0"
   _lowercase      :				Any      					=       self.bit_scale
   if self.config.clip_sample:
      _lowercase      :				List[str]      					=       torch.clamp(snake_case		,							-scale		,							snake_case					)

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   _lowercase      :				Union[str, Any]      					=       (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
   _lowercase      :				Any      					=       self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   _lowercase      :				List[Any]      					=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   _lowercase      :				Any      					=       0
   if t > 0:
      _lowercase      :				Optional[Any]      					=       torch.randn(
          model_output.size()		,							dtype=model_output.dtype		,							layout=model_output.layout		,							generator=snake_case					).to(model_output.device					)
      _lowercase      :				List[str]      					=       (self._get_variance(snake_case		,							predicted_variance=snake_case					) ** 0.5) * noise

   _lowercase      :				List[str]      					=       pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=snake_case		,							pred_original_sample=snake_case					)


class        a__  (							_a  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = 1.0      ,    ):





      """simple docstring"""





      super().__init__()
      _lowercase      :				Tuple      					=       bit_scale
      _lowercase      :				str      					=       (
          ddim_bit_scheduler_step if isinstance(_a      ,    _a  ) else ddpm_bit_scheduler_step
      )

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






   @torch.no_grad()
   def __call__(			self      ,    _UpperCamelCase = 256      ,    _UpperCamelCase = 256      ,    _UpperCamelCase = 50      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 1      ,    _UpperCamelCase = "pil"      ,    _UpperCamelCase = True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       torch.randn(
          (batch_size, self.unet.config.in_channels, height, width)      ,    generator=_a      ,    )
      _lowercase      :				Union[str, Any]      					=       decimal_to_bits(_a  ) * self.bit_scale
      _lowercase      :				int      					=       latents.to(self.device  )

      self.scheduler.set_timesteps(_a  )

      for t in self.progress_bar(self.scheduler.timesteps  ):
         # predict the noise residual
         _lowercase      :				List[str]      					=       self.unet(_a      ,    _a  ).sample

         # compute the previous noisy sample x_t -> x_t-1
         _lowercase      :				Union[str, Any]      					=       self.scheduler.step(_a      ,    _a      ,    _a  ).prev_sample

      _lowercase      :				Tuple      					=       bits_to_decimal(_a  )

      if output_type == "pil":
         _lowercase      :				Optional[Any]      					=       self.numpy_to_pil(_a  )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=_a  )









'''simple docstring'''
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,
)






