




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     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		List[str]:

    """simple docstring"""


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












import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a_      						=   logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       torch.FloatTensor
      lowerCAmelCase__	:       Optional[torch.FloatTensor]			=    None







def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=0.999    ,   lowercase_="cosine"    ,   )				->		Dict:

    """simple docstring"""


    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowercase_					):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowercase_					):
            return math.exp(t * -12.0					)

    else:
        raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

    __UpperCamelCase     						=  []
    for i in range(lowercase_					):
        __UpperCamelCase     						=  i / num_diffusion_timesteps
        __UpperCamelCase     						=  (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowercase_					) / alpha_bar_fn(lowercase_					)    ,   lowercase_					)					)
    return torch.tensor(lowercase_    ,   dtype=torch.floataa					)


class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Tuple			=    [e.name for e in KarrasDiffusionSchedulers]
      lowerCAmelCase__	:       List[Any]			=    1







      @register_to_config
      def __init__(   self    :       Tuple       ,				snake_case    :       int = 1000       ,				snake_case    :       float = 0.00_01       ,				snake_case    :       float = 0.02       ,				snake_case    :       str = "linear"       ,				snake_case    :       Optional[Union[np.ndarray, List[float]]] = None       ,				snake_case    :       str = "epsilon"       ,				snake_case    :       str = "linspace"       ,				snake_case    :       int = 0       ,				):
          if trained_betas is not None:
              __UpperCamelCase     						=  torch.tensor(snake_case       ,				dtype=torch.floataa )
          elif beta_schedule == "linear":
              __UpperCamelCase     						=  torch.linspace(snake_case       ,				snake_case       ,				snake_case       ,				dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __UpperCamelCase     						=  (
                  torch.linspace(beta_start**0.5       ,				beta_end**0.5       ,				snake_case       ,				dtype=torch.floataa ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __UpperCamelCase     						=  betas_for_alpha_bar(snake_case )
          else:
              raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

          __UpperCamelCase     						=  1.0 - self.betas
          __UpperCamelCase     						=  torch.cumprod(self.alphas       ,				dim=0 )

          __UpperCamelCase     						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          __UpperCamelCase     						=  np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
          __UpperCamelCase     						=  torch.from_numpy(snake_case )

          # setable values
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  np.linspace(0       ,				num_train_timesteps - 1       ,				snake_case       ,				dtype=snake_case )[::-1].copy()
          __UpperCamelCase     						=  torch.from_numpy(snake_case )
          __UpperCamelCase     						=  False







      @property
      def 					snake_case		(   self    :       Tuple ):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5







      def 					snake_case		(   self    :       int       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Union[float, torch.FloatTensor] ):
          if isinstance(snake_case       ,				torch.Tensor ):
              __UpperCamelCase     						=  timestep.to(self.timesteps.device )
          __UpperCamelCase     						=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase     						=  self.sigmas[step_index]
          __UpperCamelCase     						=  sample / ((sigma**2 + 1) ** 0.5)
          __UpperCamelCase     						=  True
          return sample







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int       ,				snake_case    :       Union[str, torch.device] = None ):
          __UpperCamelCase     						=  num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              __UpperCamelCase     						=  np.linspace(0       ,				self.config.num_train_timesteps - 1       ,				snake_case       ,				dtype=snake_case )[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              __UpperCamelCase     						=  self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __UpperCamelCase     						=  (np.arange(0       ,				snake_case ) * step_ratio).round()[::-1].copy().astype(snake_case )
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              __UpperCamelCase     						=  self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __UpperCamelCase     						=  (np.arange(self.config.num_train_timesteps       ,				0       ,				-step_ratio )).round().copy().astype(snake_case )
              timesteps -= 1
          else:
              raise ValueError(
                  F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

          __UpperCamelCase     						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          __UpperCamelCase     						=  np.interp(snake_case       ,				np.arange(0       ,				len(snake_case ) )       ,				snake_case )
          __UpperCamelCase     						=  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
          __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(device=snake_case )
          if str(snake_case ).startswith('''mps''' ):
              # mps does not support float64
              __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(snake_case       ,				dtype=torch.floataa )
          else:
              __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(device=snake_case )







      def 					snake_case		(   self    :       int       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Union[float, torch.FloatTensor]       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Optional[torch.Generator] = None       ,				snake_case    :       bool = True       ,				):

          if (
              isinstance(snake_case       ,				snake_case )
              or isinstance(snake_case       ,				torch.IntTensor )
              or isinstance(snake_case       ,				torch.LongTensor )
          ):
              raise ValueError(
                  (
                      '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                      ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                      ''' one of the `scheduler.timesteps` as a timestep.'''
                  )       ,				)

          if not self.is_scale_input_called:
              logger.warning(
                  '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                  '''See `StableDiffusionPipeline` for a usage example.''' )

          if isinstance(snake_case       ,				torch.Tensor ):
              __UpperCamelCase     						=  timestep.to(self.timesteps.device )

          __UpperCamelCase     						=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase     						=  self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase     						=  sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase     						=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError('''prediction_type not implemented yet: sample''' )
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

          __UpperCamelCase     						=  self.sigmas[step_index]
          __UpperCamelCase     						=  self.sigmas[step_index + 1]
          __UpperCamelCase     						=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          __UpperCamelCase     						=  (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          __UpperCamelCase     						=  (sample - pred_original_sample) / sigma

          __UpperCamelCase     						=  sigma_down - sigma

          __UpperCamelCase     						=  sample + derivative * dt

          __UpperCamelCase     						=  model_output.device
          __UpperCamelCase     						=  randn_tensor(model_output.shape       ,				dtype=model_output.dtype       ,				device=snake_case       ,				generator=snake_case )

          __UpperCamelCase     						=  prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=snake_case       ,				pred_original_sample=snake_case )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       torch.FloatTensor       ,				):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          __UpperCamelCase     						=  self.sigmas.to(device=original_samples.device       ,				dtype=original_samples.dtype )
          if original_samples.device.type == "mps" and torch.is_floating_point(snake_case ):
              # mps does not support float64
              __UpperCamelCase     						=  self.timesteps.to(original_samples.device       ,				dtype=torch.floataa )
              __UpperCamelCase     						=  timesteps.to(original_samples.device       ,				dtype=torch.floataa )
          else:
              __UpperCamelCase     						=  self.timesteps.to(original_samples.device )
              __UpperCamelCase     						=  timesteps.to(original_samples.device )

          __UpperCamelCase     						=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          __UpperCamelCase     						=  sigmas[step_indices].flatten()
          while len(sigma.shape ) < len(original_samples.shape ):
              __UpperCamelCase     						=  sigma.unsqueeze(-1 )

          __UpperCamelCase     						=  original_samples + noise * sigma
          return noisy_samples







      def __len__(   self    :       Optional[Any] ):
          return self.config.num_train_timesteps






