




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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=0.999       ,  lowercase_="cosine"       ,  )							->						Union[str, Any]:

    """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]     =       2







      @register_to_config
      def __init__(  self       :			List[Any]			,							snake_case       :			int = 1000			,							snake_case       :			float = 0.0_00_85			,							snake_case       :			float = 0.0_12			,							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					)

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







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			Union[str, Any]			,							snake_case       :			Tuple=None					):
          if schedule_timesteps is None:
              __UpperCamelCase			=    self.timesteps

          __UpperCamelCase			=    (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:
              __UpperCamelCase			=    1 if len(snake_case					) > 1 else 0
          else:
              __UpperCamelCase			=    timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
              __UpperCamelCase			=    self._index_counter[timestep_int]

          return indices[pos].item()







      @property
      def    snake_case						(  self       :			int					):
          # 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       :			str			,							snake_case       :			torch.FloatTensor			,							snake_case       :			Union[float, torch.FloatTensor]			,							):
          __UpperCamelCase			=    self.index_for_timestep(snake_case					)

          if self.state_in_first_order:
              __UpperCamelCase			=    self.sigmas[step_index]
          else:
              __UpperCamelCase			=    self.sigmas_interpol[step_index - 1]

          __UpperCamelCase			=    sample / ((sigma**2 + 1) ** 0.5)
          return sample







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

          __UpperCamelCase			=    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":
              __UpperCamelCase			=    np.linspace(0			,							num_train_timesteps - 1			,							snake_case			,							dtype=snake_case					)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
              __UpperCamelCase			=    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			=    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(snake_case			,							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			=    torch.from_numpy(np.log(snake_case					)					).to(snake_case					)

          __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					)

          # compute up and down sigmas
          __UpperCamelCase			=    sigmas.roll(-1					)
          __UpperCamelCase			=    0.0
          __UpperCamelCase			=    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          __UpperCamelCase			=    (sigmas_next**2 - sigmas_up**2) ** 0.5
          __UpperCamelCase			=    0.0

          # compute interpolated sigmas
          __UpperCamelCase			=    sigmas.log().lerp(sigmas_down.log()			,							0.5					).exp()
          __UpperCamelCase			=    0.0

          # set sigmas
          __UpperCamelCase			=    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
          __UpperCamelCase			=    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
          __UpperCamelCase			=    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
          __UpperCamelCase			=    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

          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(snake_case					)

          __UpperCamelCase			=    self.sigma_to_t(snake_case					).to(snake_case			,							dtype=timesteps.dtype					)
          __UpperCamelCase			=    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])			,							dim=-1					).flatten()

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

          __UpperCamelCase			=    None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          __UpperCamelCase			=    defaultdict(snake_case					)







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			List[Any]					):
          # get log sigma
          __UpperCamelCase			=    sigma.log()

          # get distribution
          __UpperCamelCase			=    log_sigma - self.log_sigmas[:, None]

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

          __UpperCamelCase			=    self.log_sigmas[low_idx]
          __UpperCamelCase			=    self.log_sigmas[high_idx]

          # interpolate sigmas
          __UpperCamelCase			=    (low - log_sigma) / (low - high)
          __UpperCamelCase			=    w.clamp(0			,							1					)

          # transform interpolation to time range
          __UpperCamelCase			=    (1 - w) * low_idx + w * high_idx
          __UpperCamelCase			=    t.view(sigma.shape					)
          return t







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







      def    snake_case						(  self       :			str			,							snake_case       :			Union[torch.FloatTensor, np.ndarray]			,							snake_case       :			Union[float, torch.FloatTensor]			,							snake_case       :			Union[torch.FloatTensor, np.ndarray]			,							snake_case       :			Optional[torch.Generator] = None			,							snake_case       :			bool = True			,							):
          __UpperCamelCase			=    self.index_for_timestep(snake_case					)

          # advance index counter by 1
          __UpperCamelCase			=    timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
              __UpperCamelCase			=    self.sigmas[step_index]
              __UpperCamelCase			=    self.sigmas_interpol[step_index]
              __UpperCamelCase			=    self.sigmas_up[step_index]
              __UpperCamelCase			=    self.sigmas_down[step_index - 1]
          else:
              # 2nd order / KPDM2's method
              __UpperCamelCase			=    self.sigmas[step_index - 1]
              __UpperCamelCase			=    self.sigmas_interpol[step_index - 1]
              __UpperCamelCase			=    self.sigmas_up[step_index - 1]
              __UpperCamelCase			=    self.sigmas_down[step_index - 1]

          # 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
          __UpperCamelCase			=    0
          __UpperCamelCase			=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase			=    sigma_hat if self.state_in_first_order else sigma_interpol
              __UpperCamelCase			=    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
              __UpperCamelCase			=    sigma_hat if self.state_in_first_order else sigma_interpol
              __UpperCamelCase			=    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
              __UpperCamelCase			=    (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              __UpperCamelCase			=    sigma_interpol - sigma_hat

              # store for 2nd order step
              __UpperCamelCase			=    sample
              __UpperCamelCase			=    dt
              __UpperCamelCase			=    sample + derivative * dt
          else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              __UpperCamelCase			=    (sample - pred_original_sample) / sigma_interpol
              # 3. delta timestep
              __UpperCamelCase			=    sigma_down - sigma_hat

              __UpperCamelCase			=    self.sample
              __UpperCamelCase			=    None

              __UpperCamelCase			=    sample + derivative * dt
              __UpperCamelCase			=    prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=snake_case					)







      def    snake_case						(  self       :			Optional[int]			,							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			=    [self.index_for_timestep(snake_case			,							snake_case					) 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[int]					):
          return self.config.num_train_timesteps







'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Optional[int]			=    {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"cpmant"





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						int = 3_0_7_2_0							,							UpperCAmelCase_     :						int = 4_0_9_6							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 1_2_8							,							UpperCAmelCase_     :						int = 1_0_2_4_0							,							UpperCAmelCase_     :						int = 4_8							,							UpperCAmelCase_     :						int = 0.0							,							UpperCAmelCase_     :						int = 5_1_2							,							UpperCAmelCase_     :						int = 2_0_4_8							,							UpperCAmelCase_     :						int = 1e-6							,							UpperCAmelCase_     :						float = 1.0							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						bool = True							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							Union[str, Any]							= prompt_types
         a					:							List[str]							= prompt_length
         a					:							List[Any]							= segment_types
         a					:							Any							= hidden_size
         a					:							Optional[int]							= num_attention_heads
         a					:							Union[str, Any]							= dim_head
         a					:							int							= dim_ff
         a					:							Union[str, Any]							= num_hidden_layers
         a					:							List[Any]							= position_bias_num_buckets
         a					:							Optional[int]							= position_bias_max_distance
         a					:							str							= dropout_p
         a					:							List[str]							= eps
         a					:							Dict							= use_cache
         a					:							Tuple							= vocab_size
         a					:							Optional[Any]							= init_std

