# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			42
  _SCREAMING_SNAKE_CASE    				=			None



def   __A		(			_A							,				_A=0.999							,				_A="cosine"							,				):



 """simple docstring"""


 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_A					):
   return math.exp(t * -12.0					)

 else:
  raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 __a       =  []
 for i in range(_A					):
  __a       =  i / num_diffusion_timesteps
  __a       =  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_A					) / alpha_bar_fn(_A					)							,				_A					)					)
 return torch.tensor(_A							,				dtype=torch.floataa					)






class    A_ (       a_			,       a_ ):
  _SCREAMING_SNAKE_CASE    				=			1

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int = 10_00     ,							__SCREAMING_SNAKE_CASE :      float = 0.00_01     ,							__SCREAMING_SNAKE_CASE :      float = 0.02     ,							__SCREAMING_SNAKE_CASE :      str = "linear"     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[np.ndarray, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      int = 0     ,							__SCREAMING_SNAKE_CASE :      str = "epsilon"     ,							__SCREAMING_SNAKE_CASE :      float = 1.0     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   if kwargs.get("set_alpha_to_one"     ,							__SCREAMING_SNAKE_CASE       ) is not None:
    __a       =  (
        "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
    )
    deprecate("set_alpha_to_one"     ,							"1.0.0"     ,							__SCREAMING_SNAKE_CASE     ,							standard_warn=__SCREAMING_SNAKE_CASE       )
    __a       =  kwargs["set_alpha_to_one"]
   if trained_betas is not None:
    __a       =  torch.tensor(__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   elif beta_schedule == "linear":
    __a       =  torch.linspace(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __a       =  (
        torch.linspace(beta_start**0.5     ,							beta_end**0.5     ,							__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       ) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __a       =  betas_for_alpha_bar(__SCREAMING_SNAKE_CASE       )
   else:
    raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""       )

   __a       =  1.0 - self.betas
   __a       =  torch.cumprod(self.alphas     ,							dim=0       )

   # At every step in inverted ddim, we are looking into the next alphas_cumprod
   # For the final step, there is no next alphas_cumprod, and the index is out of bounds
   # `set_alpha_to_zero` decides whether we set this parameter simply to zero
   # in this case, self.step() just output the predicted noise
   # or whether we use the final alpha of the "non-previous" one.
   __a       =  torch.tensor(0.0       ) if set_alpha_to_zero else self.alphas_cumprod[-1]

   # standard deviation of the initial noise distribution
   __a       =  1.0

   # setable values
   __a       =  None
   __a       =  torch.from_numpy(np.arange(0     ,							__SCREAMING_SNAKE_CASE       ).copy().astype(np.intaa       )       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None       ):
   return sample

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Union[str, torch.device] = None       ):

   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       =  num_inference_steps
   __a       =  self.config.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       =  (np.arange(0     ,							__SCREAMING_SNAKE_CASE       ) * step_ratio).round().copy().astype(np.intaa       )
   __a       =  torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )
   self.timesteps += self.config.steps_offset

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      float = 0.0     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      Optional[torch.FloatTensor] = None     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							):
   # 1. get previous step value (=t+1)
   __a       =  timestep + self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   # change original implementation to exactly match noise levels for analogous forward process
   __a       =  self.alphas_cumprod[timestep]
   __a       =  (
       self.alphas_cumprod[prev_timestep]
       if prev_timestep < self.config.num_train_timesteps
       else self.final_alpha_cumprod
   )

   __a       =  1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   if self.config.prediction_type == "epsilon":
    __a       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    __a       =  model_output
   elif self.config.prediction_type == "sample":
    __a       =  model_output
    __a       =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
    __a       =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    __a       =  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
   else:
    raise ValueError(
        f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
        " `v_prediction`"       )

   # 4. Clip or threshold "predicted x_0"
   if self.config.clip_sample:
    __a       =  pred_original_sample.clamp(
        -self.config.clip_sample_range     ,							self.config.clip_sample_range       )

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __a       =  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

   # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __a       =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if not return_dict:
    return (prev_sample, pred_original_sample)
   return DDIMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE     ,							pred_original_sample=__SCREAMING_SNAKE_CASE       )

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



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

   __a       =  encoder_input_tokens.shape[1]
   __a       =  torch.arange(__SCREAMING_SNAKE_CASE     ,							device=encoder_input_tokens.device       )
   x += self.position_encoding(__SCREAMING_SNAKE_CASE       )

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


