# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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





@dataclass
class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			torch.FloatTensor
   __UpperCAmelCase			:			torch.FloatTensor





class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_					):
   __UpperCAmelCase			:			str              =      1


   @register_to_config
   def __init__(self					:						Optional[Any]	,  __UpperCAmelCase					:						int = 2_0_0_0	,  __UpperCAmelCase					:						float = 0.15	,  __UpperCAmelCase					:						float = 0.01	,  __UpperCAmelCase					:						float = 1348.0	,  __UpperCAmelCase					:						float = 1E-5	,  __UpperCAmelCase					:						int = 1	,  )						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  sigma_max

         # setable values
         UpperCAmelCase__								=  None

         self.set_sigmas(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						Optional[int] = None		)						->    torch.FloatTensor:
         """simple docstring"""



         return sample


   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						Union[str, torch.device] = None		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

         UpperCAmelCase__								=  torch.linspace(1	,  __UpperCAmelCase	,  __UpperCAmelCase	,  device=__UpperCAmelCase		)


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						float = None		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  sigma_min if sigma_min is not None else self.config.sigma_min
         UpperCAmelCase__								=  sigma_max if sigma_max is not None else self.config.sigma_max
         UpperCAmelCase__								=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
               self.set_timesteps(__UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         UpperCAmelCase__								=  torch.exp(torch.linspace(math.log(__UpperCAmelCase		)	,  math.log(__UpperCAmelCase		)	,  __UpperCAmelCase		)		)
         UpperCAmelCase__								=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Optional[int]		)						->    Dict:
         """simple docstring"""



         return torch.where(
             timesteps == 0	,  torch.zeros_like(t.to(timesteps.device		)		)	,  self.discrete_sigmas[timesteps - 1].to(timesteps.device		)	,  )


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						bool = True	,  )						->    Union[SdeVeOutput, Tuple]:
         """simple docstring"""



         if self.timesteps is None:
               raise ValueError(
                   "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

         UpperCAmelCase__								=  timestep * torch.ones(
             sample.shape[0]	,  device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
         UpperCAmelCase__								=  (timestep * (len(self.timesteps		) - 1)).long()

         # mps requires indices to be in the same device, so we use cpu as is the default with cuda
         UpperCAmelCase__								=  timesteps.to(self.discrete_sigmas.device		)

         UpperCAmelCase__								=  self.discrete_sigmas[timesteps].to(sample.device		)
         UpperCAmelCase__								=  self.get_adjacent_sigma(__UpperCAmelCase	,  __UpperCAmelCase		).to(sample.device		)
         UpperCAmelCase__								=  torch.zeros_like(__UpperCAmelCase		)
         UpperCAmelCase__								=  (sigma**2 - adjacent_sigma**2) ** 0.5

         # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
         # also equation 47 shows the analog from SDE models to ancestral sampling methods
         UpperCAmelCase__								=  diffusion.flatten()
         while len(diffusion.shape		) < len(sample.shape		):
               UpperCAmelCase__								=  diffusion.unsqueeze(-1		)
         UpperCAmelCase__								=  drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         UpperCAmelCase__								=  randn_tensor(
             sample.shape	,  layout=sample.layout	,  generator=__UpperCAmelCase	,  device=sample.device	,  dtype=sample.dtype		)
         UpperCAmelCase__								=  sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         UpperCAmelCase__								=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

         if not return_dict:
               return (prev_sample, prev_sample_mean)

         return SdeVeOutput(prev_sample=__UpperCAmelCase	,  prev_sample_mean=__UpperCAmelCase		)


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						bool = True	,  )						->    Union[SchedulerOutput, Tuple]:
         """simple docstring"""



         if self.timesteps is None:
               raise ValueError(
                   "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         UpperCAmelCase__								=  randn_tensor(sample.shape	,  layout=sample.layout	,  generator=__UpperCAmelCase		).to(sample.device		)

         # compute step size from the model_output, the noise, and the snr
         UpperCAmelCase__								=  torch.norm(model_output.reshape(model_output.shape[0]	,  -1		)	,  dim=-1		).mean()
         UpperCAmelCase__								=  torch.norm(noise.reshape(noise.shape[0]	,  -1		)	,  dim=-1		).mean()
         UpperCAmelCase__								=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
         UpperCAmelCase__								=  step_size * torch.ones(sample.shape[0]		).to(sample.device		)
         # self.repeat_scalar(step_size, sample.shape[0])

         # compute corrected sample: model_output term and noise term
         UpperCAmelCase__								=  step_size.flatten()
         while len(step_size.shape		) < len(sample.shape		):
               UpperCAmelCase__								=  step_size.unsqueeze(-1		)
         UpperCAmelCase__								=  sample + step_size * model_output
         UpperCAmelCase__								=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
               return (prev_sample,)

         return SchedulerOutput(prev_sample=__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						torch.FloatTensor	,  __UpperCAmelCase					:						torch.FloatTensor	,  )						->    torch.FloatTensor:
         """simple docstring"""



         UpperCAmelCase__								=  timesteps.to(original_samples.device		)
         UpperCAmelCase__								=  self.discrete_sigmas.to(original_samples.device		)[timesteps]
         UpperCAmelCase__								=  (
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(__UpperCAmelCase		) * sigmas[:, None, None, None]
         )
         UpperCAmelCase__								=  noise + original_samples
         return noisy_samples





   def __len__(self					:						List[Any]		)						->    List[Any]:
         """simple docstring"""



         return self.config.num_train_timesteps




from typing import Callable, Optional, Union

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


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[int]              =      'xlm-prophetnet'
   __UpperCAmelCase			:			int              =      ['past_key_values']
   __UpperCAmelCase			:			Optional[int]              =      {
       'num_attention_heads': 'num_encoder_attention_heads',
   }


   def __init__(self					:						str	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[Union[str, Callable]] = "gelu"	,  __UpperCAmelCase					:						Optional[int] = 3_0_5_2_2	,  __UpperCAmelCase					:						Optional[int] = 1_0_2_4	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[int] = 5_1_2	,  __UpperCAmelCase					:						Optional[float] = 0.02	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 2	,  __UpperCAmelCase					:						Optional[int] = 3_2	,  __UpperCAmelCase					:						Optional[int] = 1_2_8	,  __UpperCAmelCase					:						Optional[bool] = False	,  __UpperCAmelCase					:						Optional[float] = 0.0	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						Optional[int] = 2	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  num_encoder_layers
         UpperCAmelCase__								=  num_encoder_attention_heads
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  num_decoder_layers
         UpperCAmelCase__								=  num_decoder_attention_heads
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  init_std  # Normal(0, this parameter)
         UpperCAmelCase__								=  activation_function

         # parameters for xlmprophetnet
         UpperCAmelCase__								=  ngram
         UpperCAmelCase__								=  num_buckets
         UpperCAmelCase__								=  relative_max_distance
         UpperCAmelCase__								=  disable_ngram_loss
         UpperCAmelCase__								=  eps

         # 3 Types of Dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  dropout

         UpperCAmelCase__								=  use_cache

         super().__init__(
             pad_token_id=__UpperCAmelCase	,  bos_token_id=__UpperCAmelCase	,  eos_token_id=__UpperCAmelCase	,  is_encoder_decoder=__UpperCAmelCase	,  add_cross_attention=__UpperCAmelCase	,  decoder_start_token_id=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @property
   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         raise NotImplementedError(
             "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
             " `num_decoder_layers`."		)



