


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Any=0.999 ,  UpperCAmelCase_						:		Dict="cosine" ,  ):




      """simple docstring"""





      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(UpperCAmelCase_						:		Optional[int]						):
                  return math.exp(t * -12.0						)

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

      a						:Dict 							=       []
      for i in range(UpperCAmelCase_						):
            a						:Dict 							=       i / num_diffusion_timesteps
            a						:List[str] 							=       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_						) / alpha_bar_fn(UpperCAmelCase_						) ,  UpperCAmelCase_						)						)
      return torch.tensor(UpperCAmelCase_ ,  dtype=torch.floataa						)







class 		_snake_case  (      _snake_case						,    _snake_case							):
 SCREAMING_SNAKE_CASE__      =       [e.name for e in KarrasDiffusionSchedulers]
 SCREAMING_SNAKE_CASE__      =       2




 @register_to_config
 def __init__(		self					,  _lowerCamelCase = 1000					,  _lowerCamelCase = 0.0_0085					,  _lowerCamelCase = 0.012					,  _lowerCamelCase = "linear"					,  _lowerCamelCase = None					,  _lowerCamelCase = "epsilon"					,  _lowerCamelCase = "linspace"					,  _lowerCamelCase = 0					,  ):
       if trained_betas is not None:
             a						:Dict 							=       torch.tensor(_lowerCamelCase					,  dtype=torch.floataa    )
       elif beta_schedule == "linear":
             a						:List[str] 							=       torch.linspace(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  dtype=torch.floataa    )
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             a						:List[Any] 							=       (
                 torch.linspace(beta_start**0.5					,  beta_end**0.5					,  _lowerCamelCase					,  dtype=torch.floataa    ) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             a						:List[str] 							=       betas_for_alpha_bar(_lowerCamelCase    )
       else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

       a						:str 							=       1.0 - self.betas
       a						:Union[str, Any] 							=       torch.cumprod(self.alphas					,  dim=0    )

       #  set all values
       self.set_timesteps(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=None    ):
       if schedule_timesteps is None:
             a						:Union[str, Any] 							=       self.timesteps

       a						:Tuple 							=       (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter    ) == 0:
             a						:Any 							=       1 if len(_lowerCamelCase    ) > 1 else 0
       else:
             a						:int 							=       timestep.cpu().item() if torch.is_tensor(_lowerCamelCase    ) else timestep
             a						:List[Any] 							=       self._index_counter[timestep_int]

       return indices[pos].item()




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # 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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       a						:List[Any] 							=       self.index_for_timestep(_lowerCamelCase    )

       if self.state_in_first_order:
             a						:Optional[int] 							=       self.sigmas[step_index]
       else:
             a						:List[Any] 							=       self.sigmas_interpol[step_index]

       a						:Union[str, Any] 							=       sample / ((sigma**2 + 1) ** 0.5)
       return sample




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       num_inference_steps

       a						:Union[str, Any] 							=       num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
             a						:int 							=       np.linspace(0					,  num_train_timesteps - 1					,  _lowerCamelCase					,  dtype=_lowerCamelCase    )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             a						:Any 							=       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
             a						:Optional[Any] 							=       (np.arange(0					,  _lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase    )
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             a						:List[Any] 							=       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
             a						:str 							=       (np.arange(_lowerCamelCase					,  0					,  -step_ratio    )).round().copy().astype(_lowerCamelCase    )
             timesteps -= 1
       else:
             raise ValueError(
                 F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

       a						:str 							=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
       a						:List[str] 							=       torch.from_numpy(np.log(_lowerCamelCase    )    ).to(_lowerCamelCase    )

       a						:Optional[int] 							=       np.interp(_lowerCamelCase					,  np.arange(0					,  len(_lowerCamelCase    )    )					,  _lowerCamelCase    )
       a						:Optional[int] 							=       np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
       a						:Any 							=       torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )

       # interpolate sigmas
       a						:int 							=       sigmas.log().lerp(sigmas.roll(1    ).log()					,  0.5    ).exp()

       a						:Optional[Any] 							=       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
       a						:Any 							=       torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             # mps does not support float64
             a						:Union[str, Any] 							=       torch.from_numpy(_lowerCamelCase    ).to(_lowerCamelCase					,  dtype=torch.floataa    )
       else:
             a						:Union[str, Any] 							=       torch.from_numpy(_lowerCamelCase    ).to(_lowerCamelCase    )

       # interpolate timesteps
       a						:Any 							=       self.sigma_to_t(_lowerCamelCase    ).to(_lowerCamelCase					,  dtype=timesteps.dtype    )
       a						:Union[str, Any] 							=       torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])					,  dim=-1    ).flatten()

       a						:int 							=       torch.cat([timesteps[:1], interleaved_timesteps]    )

       a						:Dict 							=       None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       a						:str 							=       defaultdict(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # get log sigma
       a						:Any 							=       sigma.log()

       # get distribution
       a						:Optional[Any] 							=       log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       a						:int 							=       dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
       a						:int 							=       low_idx + 1

       a						:Optional[Any] 							=       self.log_sigmas[low_idx]
       a						:str 							=       self.log_sigmas[high_idx]

       # interpolate sigmas
       a						:Any 							=       (low - log_sigma) / (low - high)
       a						:Tuple 							=       w.clamp(0					,  1    )

       # transform interpolation to time range
       a						:Any 							=       (1 - w) * low_idx + w * high_idx
       a						:List[str] 							=       t.view(sigma.shape    )
       return t




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return self.sample is None




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = True					,  ):
       a						:Union[str, Any] 							=       self.index_for_timestep(_lowerCamelCase    )

       # advance index counter by 1
       a						:Tuple 							=       timestep.cpu().item() if torch.is_tensor(_lowerCamelCase    ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
             a						:Tuple 							=       self.sigmas[step_index]
             a						:int 							=       self.sigmas_interpol[step_index + 1]
             a						:List[Any] 							=       self.sigmas[step_index + 1]
       else:
             # 2nd order / KDPM2's method
             a						:List[Any] 							=       self.sigmas[step_index - 1]
             a						:List[str] 							=       self.sigmas_interpol[step_index]
             a						:Tuple 							=       self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       a						:int 							=       0
       a						:List[Any] 							=       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             a						:Union[str, Any] 							=       sigma_hat if self.state_in_first_order else sigma_interpol
             a						:Any 							=       sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             a						:str 							=       sigma_hat if self.state_in_first_order else sigma_interpol
             a						:Any 							=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**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`'''    )

       if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             a						:int 							=       (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             a						:Dict 							=       sigma_interpol - sigma_hat

             # store for 2nd order step
             a						:Dict 							=       sample
       else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             a						:Any 							=       (sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             a						:List[str] 							=       sigma_next - sigma_hat

             a						:str 							=       self.sample
             a						:str 							=       None

       a						:List[str] 							=       sample + derivative * dt

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_lowerCamelCase    )




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

       a						:Dict 							=       [self.index_for_timestep(_lowerCamelCase					,  _lowerCamelCase    ) for t in timesteps]

       a						:int 							=       sigmas[step_indices].flatten()
       while len(sigma.shape    ) < len(original_samples.shape    ):
             a						:List[Any] 							=       sigma.unsqueeze(-1    )

       a						:List[str] 							=       original_samples + noise * sigma
       return noisy_samples




 def __len__(		self    ):
       return self.config.num_train_timesteps









from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ''
 SCREAMING_SNAKE_CASE__      =       'hf-legacy'  # "hf://"" is reserved for hffs




 def __init__(		self					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(self					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       repo_info
       a						:int 							=       token
       a						:int 							=       None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.dir_cache is None:
             a						:Dict 							=       {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   a						:List[Any] 							=       {
                       '''name''': hf_file.rfilename,
                       '''size''': None,
                       '''type''': '''file''',
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCamelCase    ): {'''name''': str(_lowerCamelCase    ), '''size''': None, '''type''': '''directory'''}
                           for d in list(PurePosixPath(hf_file.rfilename    ).parents    )[:-1]
                       }    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = "rb"					,  **_lowerCamelCase					,  ):
       if not isinstance(self.repo_info					,  _lowerCamelCase    ):
             raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''    )
       a						:Optional[int] 							=       hf_hub_url(self.repo_info.id					,  _lowerCamelCase					,  revision=self.repo_info.sha    )
       return fsspec.open(
           _lowerCamelCase					,  mode=_lowerCamelCase					,  headers=get_authentication_headers_for_url(_lowerCamelCase					,  use_auth_token=self.token    )					,  client_kwargs={'''trust_env''': True}					,  ).open()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:Union[str, Any] 							=       self._strip_protocol(_lowerCamelCase    )
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=False					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:str 							=       PurePosixPath(path.strip('''/'''    )    )
       a						:Tuple 							=       {}
       for p, f in self.dir_cache.items():
             a						:Optional[int] 							=       PurePosixPath(p.strip('''/'''    )    )
             a						:str 							=       p.parent
             if root == path:
                   a						:List[str] 							=       f
       a						:Any 							=       list(paths.values()    )
       if detail:
             return out
       else:
             return sorted(f['''name'''] for f in out    )





