






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 SchedulerMixin


a 			=							logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class 							__snake_case(       __UpperCAmelCase   ):
 lowercase			:							List[Any]		   =		42
class 							__snake_case(       __UpperCAmelCase  ,							__UpperCAmelCase   ):
 lowercase			:							Tuple		   =		1



 @register_to_config
 def __init__(						self : Any			,		UpperCAmelCase__ : Optional[int] = 40			,		UpperCAmelCase__ : str = 0.002			,		UpperCAmelCase__ : List[str] = 80.0			,		UpperCAmelCase__ : Any = 0.5			,		UpperCAmelCase__ : List[Any] = 1.0			,		UpperCAmelCase__ : Union[str, Any] = 7.0			,		UpperCAmelCase__ : Union[str, Any] = True			,		):

    """simple docstring"""






    lowercase__    :						Any       =			sigma_max

    lowercase__    :						Tuple       =			np.linspace(0			,		1			,		UpperCAmelCase__						)
    lowercase__    :						Any       =			self._convert_to_karras(UpperCAmelCase__						)
    lowercase__    :						Optional[int]       =			self.sigma_to_t(UpperCAmelCase__						)

    # setable values
    lowercase__    :						Optional[int]       =			None
    lowercase__    :						int       =			torch.from_numpy(UpperCAmelCase__						)
    lowercase__    :						Union[str, Any]       =			torch.from_numpy(UpperCAmelCase__						)
    lowercase__    :						Union[str, Any]       =			False
    lowercase__    :						Optional[int]       =			False



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : Optional[Any]			,		UpperCAmelCase__ : Dict=None						):

    """simple docstring"""






    if schedule_timesteps is None:
       lowercase__    :						Optional[int]       =			self.timesteps

    lowercase__    :						int       =			(schedule_timesteps == timestep).nonzero()
    return indices.item()



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : List[Any]			,		UpperCAmelCase__ : Optional[int]						):

    """simple docstring"""






    if isinstance(UpperCAmelCase__			,		torch.Tensor						):
       lowercase__    :						Union[str, Any]       =			timestep.to(self.timesteps.device						)
    lowercase__    :						Union[str, Any]       =			self.index_for_timestep(UpperCAmelCase__						)
    lowercase__    :						str       =			self.sigmas[step_idx]

    lowercase__    :						int       =			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

    lowercase__    :						Any       =			True
    return sample



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

    """simple docstring"""






    if not isinstance(UpperCAmelCase__			,		np.ndarray						):
       lowercase__    :						Union[str, Any]       =			np.array(UpperCAmelCase__			,		dtype=np.floataa						)

    lowercase__    :						Optional[Any]       =			1_000 * 0.25 * np.log(sigmas + 1e-44						)

    return timesteps



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : int = None			,		UpperCAmelCase__ : List[str] = None			,		UpperCAmelCase__ : Tuple = None			,		):

    """simple docstring"""






    if num_inference_steps is None and timesteps is None:
       raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""						)

    if num_inference_steps is not None and timesteps is not None:
       raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""						)

    # Follow DDPMScheduler custom timesteps logic
    if timesteps is not None:
       for i in range(1			,		len(UpperCAmelCase__						)						):
          if timesteps[i] >= timesteps[i - 1]:
             raise ValueError("""`timesteps` must be in descending order."""						)

       if timesteps[0] >= self.config.num_train_timesteps:
          raise ValueError(
              f'''`timesteps` must start before `self.config.train_timesteps`:'''
              f''' {self.config.num_train_timesteps}.'''						)

       lowercase__    :						Optional[Any]       =			np.array(UpperCAmelCase__			,		dtype=np.intaa						)
       lowercase__    :						int       =			True
    else:
       if num_inference_steps > self.config.num_train_timesteps:
          raise ValueError(
              f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
              f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
              f''' maximal {self.config.num_train_timesteps} timesteps.'''						)

       lowercase__    :						List[str]       =			num_inference_steps

       lowercase__    :						List[Any]       =			self.config.num_train_timesteps // self.num_inference_steps
       lowercase__    :						Optional[int]       =			(np.arange(0			,		UpperCAmelCase__						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
       lowercase__    :						Union[str, Any]       =			False

    # Map timesteps to Karras sigmas directly for multistep sampling
    # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
    lowercase__    :						Optional[int]       =			self.config.num_train_timesteps
    lowercase__    :						List[Any]       =			timesteps[::-1].copy()
    lowercase__    :						Optional[int]       =			ramp / (num_train_timesteps - 1)
    lowercase__    :						List[Any]       =			self._convert_to_karras(UpperCAmelCase__						)
    lowercase__    :						Optional[Any]       =			self.sigma_to_t(UpperCAmelCase__						)

    lowercase__    :						int       =			np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
    lowercase__    :						List[Any]       =			torch.from_numpy(UpperCAmelCase__						).to(device=UpperCAmelCase__						)

    if str(UpperCAmelCase__						).startswith("""mps"""						):
       # mps does not support float64
       lowercase__    :						Tuple       =			torch.from_numpy(UpperCAmelCase__						).to(UpperCAmelCase__			,		dtype=torch.floataa						)
    else:
       lowercase__    :						Any       =			torch.from_numpy(UpperCAmelCase__						).to(device=UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			self.config.sigma_min
    lowercase__    :						Dict       =			self.config.sigma_max

    lowercase__    :						Any       =			self.config.rho
    lowercase__    :						Tuple       =			sigma_min ** (1 / rho)
    lowercase__    :						Optional[int]       =			sigma_max ** (1 / rho)
    lowercase__    :						Tuple       =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas



 def     UpperCAmelCase     (						self : Any			,		UpperCAmelCase__ : Tuple						):

    """simple docstring"""






    lowercase__    :						Tuple       =			self.config.sigma_data

    lowercase__    :						Dict       =			sigma_data**2 / (sigma**2 + sigma_data**2)
    lowercase__    :						int       =			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out



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

    """simple docstring"""






    lowercase__    :						Dict       =			self.config.sigma_min
    lowercase__    :						int       =			self.config.sigma_data

    lowercase__    :						int       =			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
    lowercase__    :						Dict       =			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out



 def     UpperCAmelCase     (						self : int			,		UpperCAmelCase__ : Optional[int]			,		UpperCAmelCase__ : Tuple			,		UpperCAmelCase__ : Optional[Any]			,		UpperCAmelCase__ : int = None			,		UpperCAmelCase__ : Dict = True			,		):

    """simple docstring"""






    if (
        isinstance(UpperCAmelCase__			,		UpperCAmelCase__						)
        or isinstance(UpperCAmelCase__			,		torch.IntTensor						)
        or isinstance(UpperCAmelCase__			,		torch.LongTensor						)
    ):
       raise ValueError(
           (
               """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
               f''' `{self.__class__}.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(UpperCAmelCase__			,		torch.Tensor						):
       lowercase__    :						Optional[int]       =			timestep.to(self.timesteps.device						)

    lowercase__    :						str       =			self.config.sigma_min
    lowercase__    :						Dict       =			self.config.sigma_max

    lowercase__    :						List[Any]       =			self.index_for_timestep(UpperCAmelCase__						)

    # sigma_next corresponds to next_t in original implementation
    lowercase__    :						Any       =			self.sigmas[step_index]
    if step_index + 1 < self.config.num_train_timesteps:
       lowercase__    :						Tuple       =			self.sigmas[step_index + 1]
    else:
       # Set sigma_next to sigma_min
       lowercase__    :						List[str]       =			self.sigmas[-1]

    # Get scalings for boundary conditions
    lowercase__  ,       lowercase__    :						List[Any]       =			self.get_scalings_for_boundary_condition(UpperCAmelCase__						)

    # 1. Denoise model output using boundary conditions
    lowercase__    :						Dict       =			c_out * model_output + c_skip * sample
    if self.config.clip_denoised:
       lowercase__    :						Union[str, Any]       =			denoised.clamp(-1			,		1						)

    # 2. Sample z ~ N(0, s_noise^2 * I)
    # Noise is not used for onestep sampling.
    if len(self.timesteps						) > 1:
       lowercase__    :						Tuple       =			randn_tensor(
           model_output.shape			,		dtype=model_output.dtype			,		device=model_output.device			,		generator=UpperCAmelCase__						)
    else:
       lowercase__    :						Tuple       =			torch.zeros_like(UpperCAmelCase__						)
    lowercase__    :						List[Any]       =			noise * self.config.s_noise

    lowercase__    :						Union[str, Any]       =			sigma_next.clamp(min=UpperCAmelCase__			,		max=UpperCAmelCase__						)

    # 3. Return noisy sample
    # tau = sigma_hat, eps = sigma_min
    lowercase__    :						Dict       =			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

    if not return_dict:
       return (prev_sample,)

    return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : Optional[Any]			,		UpperCAmelCase__ : Optional[Any]			,		UpperCAmelCase__ : List[Any]			,		):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.sigmas.to(device=original_samples.device			,		dtype=original_samples.dtype						)
    if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase__						):
       # mps does not support float64
       lowercase__    :						Any       =			self.timesteps.to(original_samples.device			,		dtype=torch.floataa						)
       lowercase__    :						List[Any]       =			timesteps.to(original_samples.device			,		dtype=torch.floataa						)
    else:
       lowercase__    :						Any       =			self.timesteps.to(original_samples.device						)
       lowercase__    :						List[Any]       =			timesteps.to(original_samples.device						)

    lowercase__    :						Tuple       =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    lowercase__    :						int       =			sigmas[step_indices].flatten()
    while len(sigma.shape						) < len(original_samples.shape						):
       lowercase__    :						Union[str, Any]       =			sigma.unsqueeze(-1						)

    lowercase__    :						Any       =			original_samples + noise * sigma
    return noisy_samples







 def __len__(						self : List[str]						):

    """simple docstring"""






    return self.config.num_train_timesteps









import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=None       ):
       _UpperCamelCase														=    None
       # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
       if str(weight.dtype       ) == "torch.bfloat16":
              # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
              _UpperCamelCase														=    weight.view(torch.intaa       )
              _UpperCamelCase														=    '''bfloat16'''
       _UpperCamelCase														=    weight.cpu().numpy()
       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							f'''{weight_name}.dat'''       )
       if index is not None:
              if dtype is None:
                     _UpperCamelCase														=    str(array.dtype       )
              _UpperCamelCase														=    {'''dtype''': dtype, '''shape''': list(array.shape       )}
       if array.ndim == 0:
              _UpperCamelCase														=    array[None]
       _UpperCamelCase														=    np.memmap(lowerCAmelCase     ,							dtype=array.dtype     ,							mode='''w+'''     ,							shape=array.shape       )
       _UpperCamelCase														=    array[:]
       file_array.flush()
       return index


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tuple(weight_info['''shape''']       )
       if shape == ():
              # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
              _UpperCamelCase														=    (1,)

       _UpperCamelCase														=    weight_info['''dtype''']
       if dtype == "bfloat16":
              # NumPy does not support bfloat16 so this was saved as a int16
              _UpperCamelCase														=    '''int16'''

       _UpperCamelCase														=    np.memmap(lowerCAmelCase     ,							dtype=lowerCAmelCase     ,							shape=lowerCAmelCase     ,							mode='''r'''       )

       if len(weight_info['''shape''']       ) == 0:
              _UpperCamelCase														=    weight[0]
       _UpperCamelCase														=    torch.tensor(lowerCAmelCase       )
       if weight_info["dtype"] == "bfloat16":
              _UpperCamelCase														=    weight.view(torch.bfloataa       )

       return weight


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       if index is None or len(lowerCAmelCase       ) == 0:
              # Nothing to save
              return

       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							'''index.json'''       )
       if os.path.isfile(lowerCAmelCase       ):
              with open(lowerCAmelCase     ,							'''r'''     ,							encoding='''utf-8'''       ) as f:
                     _UpperCamelCase														=    json.load(lowerCAmelCase       )
       else:
              _UpperCamelCase														=    {}
       current_index.update(lowerCAmelCase       )

       with open(lowerCAmelCase     ,							'''w'''     ,							encoding='''utf-8'''       ) as f:
              json.dump(lowerCAmelCase     ,							lowerCAmelCase     ,							indent=2       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       os.makedirs(lowerCAmelCase     ,							exist_ok=lowerCAmelCase       )
       _UpperCamelCase														=    {}
       for name, parameter in state_dict.items():
              _UpperCamelCase														=    offload_weight(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							index=lowerCAmelCase       )

       # Update index
       save_offload_index(lowerCAmelCase     ,							lowerCAmelCase       )

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    dataset
            _UpperCamelCase														=    prefix

     def __getitem__(   self,							A   ):





            """simple docstring"""

            return self.dataset[F'''{self.prefix}{key}''']

     def __iter__(   self   ):





            """simple docstring"""

            return iter([key for key in self.dataset if key.startswith(self.prefix   )]   )



     def __len__(   self   ):





            """simple docstring"""

            return len(self.dataset   )






class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A = None,							A = None,							A = None,							A=None,							):





            """simple docstring"""

            if state_dict is None and save_folder is None:
                   raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''   )

            _UpperCamelCase														=    {} if state_dict is None else state_dict
            _UpperCamelCase														=    save_folder
            if index is None and save_folder is not None:
                   with open(os.path.join(A,							'''index.json'''   )   ) as f:
                          _UpperCamelCase														=    json.load(A   )
            _UpperCamelCase														=    {} if index is None else index
            _UpperCamelCase														=    list(self.state_dict.keys()   )
            self.all_keys.extend([key for key in self.index if key not in self.all_keys]   )
            _UpperCamelCase														=    device

     def __getitem__(   self,							A   ):





            """simple docstring"""

            if key in self.state_dict:
                   return self.state_dict[key]
            _UpperCamelCase														=    self.index[key]
            if weight_info.get('''safetensors_file'''   ) is not None:
                   if not is_safetensors_available():
                          raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''   )

                   from safetensors import safe_open

                   _UpperCamelCase														=    '''cpu''' if self.device is None else self.device
                   with safe_open(weight_info['''safetensors_file'''],							framework='''pt''',							device=A   ) as f:
                          _UpperCamelCase														=    f.get_tensor(weight_info.get('''weight_name''',							A   )   )

                   if "dtype" in weight_info:
                          return tensor.to(getattr(A,							weight_info['''dtype''']   )   )
                   else:
                          return tensor

            _UpperCamelCase														=    os.path.join(self.save_folder,							F'''{key}.dat'''   )
            return load_offloaded_weight(A,							A   )

     def __iter__(   self   ):





            """simple docstring"""

            return iter(self.all_keys   )



     def __len__(   self   ):





            """simple docstring"""

            return len(self.all_keys   )






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    {}
       for module_name in submodule_names:
              # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
              # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
              result.update(
                  {
                      key: param
                      for key, param in state_dict.items()
                      if key == module_name or key.startswith(module_name + '''.'''       )
                  }       )
       return result






