




"""simple docstring"""

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


_lowerCamelCase					:     List[str]				       =    logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class     lowercase (      __UpperCAmelCase):
    __lowerCAmelCase      :      torch.FloatTensor

class     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase):
    __lowerCAmelCase      :      Tuple					    =				1







    @register_to_config
    def __init__(							self				:   Optional[int] ,       _lowerCamelCase				:   int = 40 ,       _lowerCamelCase				:   float = 0.002 ,       _lowerCamelCase				:   float = 80.0 ,       _lowerCamelCase				:   float = 0.5 ,       _lowerCamelCase				:   float = 1.0 ,       _lowerCamelCase				:   float = 7.0 ,       _lowerCamelCase				:   bool = True ,       ):



     """simple docstring"""

     A_					:			Optional[int]						=						sigma_max

     A_					:			Union[str, Any]						=						np.linspace(0 ,       1 ,       _lowerCamelCase    )
     A_					:			Any						=						self._convert_to_karras(_lowerCamelCase    )
     A_					:			str						=						self.sigma_to_t(_lowerCamelCase    )

     # setable values
     A_					:			List[str]						=						None
     A_					:			str						=						torch.from_numpy(_lowerCamelCase    )
     A_					:			int						=						torch.from_numpy(_lowerCamelCase    )
     A_					:			Any						=						False
     A_					:			Any						=						False







    def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   Optional[int] ,       _lowerCamelCase				:   int=None    ):



     """simple docstring"""

     if schedule_timesteps is None:
      A_					:			Tuple						=						self.timesteps

     A_					:			Any						=						(schedule_timesteps == timestep).nonzero()
     return indices.item()







    def    a_					(							self				:   Optional[int] ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   Union[float, torch.FloatTensor]    ):



     """simple docstring"""

     if isinstance(_lowerCamelCase ,       torch.Tensor    ):
      A_					:			Tuple						=						timestep.to(self.timesteps.device    )
     A_					:			List[str]						=						self.index_for_timestep(_lowerCamelCase    )
     A_					:			Dict						=						self.sigmas[step_idx]

     A_					:			List[Any]						=						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

     A_					:			List[str]						=						True
     return sample







    def    a_					(							self				:   Optional[int] ,       _lowerCamelCase				:   Union[float, np.ndarray]    ):



     """simple docstring"""

     if not isinstance(_lowerCamelCase ,       np.ndarray    ):
      A_					:			Optional[int]						=						np.array(_lowerCamelCase ,       dtype=np.floataa    )

     A_					:			List[str]						=						10_00 * 0.25 * np.log(sigmas + 1E-44    )

     return timesteps







    def    a_					(							self				:   Union[str, Any] ,       _lowerCamelCase				:   Optional[int] = None ,       _lowerCamelCase				:   Union[str, torch.device] = None ,       _lowerCamelCase				:   Optional[List[int]] = 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(_lowerCamelCase    )    ):
       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}."""    )

      A_					:			int						=						np.array(_lowerCamelCase ,       dtype=np.intaa    )
      A_					:			str						=						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."""    )

      A_					:			Union[str, Any]						=						num_inference_steps

      A_					:			Any						=						self.config.num_train_timesteps // self.num_inference_steps
      A_					:			List[Any]						=						(np.arange(0 ,       _lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
      A_					:			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
     A_					:			List[Any]						=						self.config.num_train_timesteps
     A_					:			Any						=						timesteps[::-1].copy()
     A_					:			Optional[int]						=						ramp / (num_train_timesteps - 1)
     A_					:			int						=						self._convert_to_karras(_lowerCamelCase    )
     A_					:			Optional[int]						=						self.sigma_to_t(_lowerCamelCase    )

     A_					:			Optional[int]						=						np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
     A_					:			Optional[Any]						=						torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )

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







    def    a_					(							self				:   Optional[Any] ,       _lowerCamelCase				:   Optional[Any]    ):



     """simple docstring"""

     A_					:			float						=						self.config.sigma_min
     A_					:			float						=						self.config.sigma_max

     A_					:			List[str]						=						self.config.rho
     A_					:			Union[str, Any]						=						sigma_min ** (1 / rho)
     A_					:			Any						=						sigma_max ** (1 / rho)
     A_					:			Union[str, Any]						=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas







    def    a_					(							self				:   Dict ,       _lowerCamelCase				:   Optional[Any]    ):



     """simple docstring"""

     A_					:			List[str]						=						self.config.sigma_data

     A_					:			Optional[int]						=						sigma_data**2 / (sigma**2 + sigma_data**2)
     A_					:			str						=						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out







    def    a_					(							self				:   Union[str, Any] ,       _lowerCamelCase				:   Optional[Any]    ):



     """simple docstring"""

     A_					:			Any						=						self.config.sigma_min
     A_					:			Any						=						self.config.sigma_data

     A_					:			int						=						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     A_					:			Optional[Any]						=						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out







    def    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   Union[float, torch.FloatTensor] ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   Optional[torch.Generator] = None ,       _lowerCamelCase				:   bool = True ,       ):



     """simple docstring"""

     if (
         isinstance(_lowerCamelCase ,       _lowerCamelCase    )
         or isinstance(_lowerCamelCase ,       torch.IntTensor    )
         or isinstance(_lowerCamelCase ,       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(_lowerCamelCase ,       torch.Tensor    ):
      A_					:			Tuple						=						timestep.to(self.timesteps.device    )

     A_					:			List[str]						=						self.config.sigma_min
     A_					:			Optional[int]						=						self.config.sigma_max

     A_					:			Dict						=						self.index_for_timestep(_lowerCamelCase    )

     # sigma_next corresponds to next_t in original implementation
     A_					:			Dict						=						self.sigmas[step_index]
     if step_index + 1 < self.config.num_train_timesteps:
      A_					:			Any						=						self.sigmas[step_index + 1]
     else:
      # Set sigma_next to sigma_min
      A_					:			int						=						self.sigmas[-1]

     # Get scalings for boundary conditions
     A_					:			Optional[int]						=						self.get_scalings_for_boundary_condition(_lowerCamelCase    )

     # 1. Denoise model output using boundary conditions
     A_					:			Optional[Any]						=						c_out * model_output + c_skip * sample
     if self.config.clip_denoised:
      A_					:			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:
      A_					:			Union[str, Any]						=						randn_tensor(
          model_output.shape ,       dtype=model_output.dtype ,       device=model_output.device ,       generator=_lowerCamelCase    )
     else:
      A_					:			Union[str, Any]						=						torch.zeros_like(_lowerCamelCase    )
     A_					:			Any						=						noise * self.config.s_noise

     A_					:			List[Any]						=						sigma_next.clamp(min=_lowerCamelCase ,       max=_lowerCamelCase    )

     # 3. Return noisy sample
     # tau = sigma_hat, eps = sigma_min
     A_					:			List[str]						=						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

     if not return_dict:
      return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase    )







    def    a_					(							self				:   Optional[int] ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   torch.FloatTensor ,       ):



     """simple docstring"""

     A_					:			Optional[int]						=						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_					:			Tuple						=						self.timesteps.to(original_samples.device ,       dtype=torch.floataa    )
      A_					:			int						=						timesteps.to(original_samples.device ,       dtype=torch.floataa    )
     else:
      A_					:			Optional[int]						=						self.timesteps.to(original_samples.device    )
      A_					:			List[Any]						=						timesteps.to(original_samples.device    )

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

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

     A_					:			Union[str, Any]						=						original_samples + noise * sigma
     return noisy_samples







    def __len__(							self				:   List[str]    ):



     """simple docstring"""

     return self.config.num_train_timesteps











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					:     List[Any]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





