
'''simple docstring'''



# 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				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  42
       SCREAMING_SNAKE_CASE      =  None






def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=0.999    ,      SCREAMING_SNAKE_CASE_="cosine"    ,      )     ->     List[Any]:




   """simple docstring"""
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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







class    _a				(_lowerCamelCase  ,					_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  1






       @register_to_config
       def __init__(							self       ,							A__ = 10_00       ,							A__ = 0.0001       ,							A__ = 0.02       ,							A__ = "linear"       ,							A__ = None       ,							A__ = True       ,							A__ = True       ,							A__ = 0       ,							A__ = "epsilon"       ,							A__ = 1.0       ,							**A__       ,							)    ->			Any:
          if kwargs.get("""set_alpha_to_one"""       ,							A__      ) is not None:
             _SCREAMING_SNAKE_CASE      =				(
                 """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
             )
             deprecate("""set_alpha_to_one"""       ,							"""1.0.0"""       ,							A__       ,							standard_warn=A__      )
             _SCREAMING_SNAKE_CASE      =				kwargs["""set_alpha_to_one"""]
          if trained_betas is not None:
             _SCREAMING_SNAKE_CASE      =				torch.tensor(A__       ,							dtype=torch.floataa      )
          elif beta_schedule == "linear":
             _SCREAMING_SNAKE_CASE      =				torch.linspace(A__       ,							A__       ,							A__       ,							dtype=torch.floataa      )
          elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             _SCREAMING_SNAKE_CASE      =				(
                 torch.linspace(beta_start**0.5       ,							beta_end**0.5       ,							A__       ,							dtype=torch.floataa      ) ** 2
             )
          elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             _SCREAMING_SNAKE_CASE      =				betas_for_alpha_bar(A__      )
          else:
             raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

          _SCREAMING_SNAKE_CASE      =				1.0 - self.betas
          _SCREAMING_SNAKE_CASE      =				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.
          _SCREAMING_SNAKE_CASE      =				torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          _SCREAMING_SNAKE_CASE      =				1.0

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






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			torch.FloatTensor:
          return sample






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			List[Any]:

          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."      )

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






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = 0.0       ,							A__ = False       ,							A__ = None       ,							A__ = True       ,							)    ->			Union[DDIMSchedulerOutput, Tuple]:
          # 1. get previous step value (=t+1)
          _SCREAMING_SNAKE_CASE      =				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
          _SCREAMING_SNAKE_CASE      =				self.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE      =				(
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          _SCREAMING_SNAKE_CASE      =				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":
             _SCREAMING_SNAKE_CASE      =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             _SCREAMING_SNAKE_CASE      =				model_output
          elif self.config.prediction_type == "sample":
             _SCREAMING_SNAKE_CASE      =				model_output
             _SCREAMING_SNAKE_CASE      =				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
             _SCREAMING_SNAKE_CASE      =				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             _SCREAMING_SNAKE_CASE      =				(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:
             _SCREAMING_SNAKE_CASE      =				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
          _SCREAMING_SNAKE_CASE      =				(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
          _SCREAMING_SNAKE_CASE      =				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=A__       ,							pred_original_sample=A__      )






       def __len__(							self      )    ->			List[Any]:
          return self.config.num_train_timesteps








'''simple docstring'''



import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				params

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array([len(A__      ) for t in data]      )

          self.check()
          self.remove_long_sequences()
          self.remove_empty_sequences()
          self.remove_unknown_sequences()
          self.check()
          self.print_statistics()






       def __getitem__(							self       ,							A__      )    ->			Dict:
          return (self.token_ids[index], self.lengths[index])






       def __len__(							self      )    ->			Tuple:
          return len(self.lengths      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          assert len(self.token_ids      ) == len(self.lengths      )
          assert all(self.lengths[i] == len(self.token_ids[i]      ) for i in range(len(self.lengths      )      )      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.params.max_model_input_size
          _SCREAMING_SNAKE_CASE      =				self.lengths > max_len
          logger.info(F"Splitting {sum(A__      )} too long sequences."      )

          def divide_chunks(A__       ,							A__      ):
             return [l[i : i + n] for i in range(0       ,							len(A__      )       ,							A__      )]

          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				[]
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
          else:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

          for seq_, len_ in zip(self.token_ids       ,							self.lengths      ):
             assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
             if len_ <= max_len:
                new_tok_ids.append(seq_      )
                new_lengths.append(len_      )
             else:
                _SCREAMING_SNAKE_CASE      =				[]
                for sub_s in divide_chunks(seq_       ,							max_len - 2      ):
                   if sub_s[0] != cls_id:
                      _SCREAMING_SNAKE_CASE      =				np.insert(A__       ,							0       ,							A__      )
                   if sub_s[-1] != sep_id:
                      _SCREAMING_SNAKE_CASE      =				np.insert(A__       ,							len(A__      )       ,							A__      )
                   assert len(A__      ) <= max_len
                   assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                   sub_seqs.append(A__      )

                new_tok_ids.extend(A__      )
                new_lengths.extend([len(A__      ) for l in sub_seqs]      )

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array(A__      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				len(self      )
          _SCREAMING_SNAKE_CASE      =				self.lengths > 11
          _SCREAMING_SNAKE_CASE      =				self.token_ids[indices]
          _SCREAMING_SNAKE_CASE      =				self.lengths[indices]
          _SCREAMING_SNAKE_CASE      =				len(self      )
          logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences."      )






       def        UpperCamelCase				(							self      )    ->			int:
          if "unk_token" not in self.params.special_tok_ids:
             return
          else:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""unk_token"""]
          _SCREAMING_SNAKE_CASE      =				len(self      )
          _SCREAMING_SNAKE_CASE      =				np.array([np.count_nonzero(a == unk_token_id      ) for a in self.token_ids]      )
          _SCREAMING_SNAKE_CASE      =				(unk_occs / self.lengths) < 0.5
          _SCREAMING_SNAKE_CASE      =				self.token_ids[indices]
          _SCREAMING_SNAKE_CASE      =				self.lengths[indices]
          _SCREAMING_SNAKE_CASE      =				len(self      )
          logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."      )






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          if not self.params.is_master:
             return
          logger.info(F"{len(self      )} sequences"      )
          # data_len = sum(self.lengths)
          # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
          # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

          # unk_idx = self.params.special_tok_ids['unk_token']
          # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
          # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






       def        UpperCamelCase				(							self       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				[t[0] for t in batch]
          _SCREAMING_SNAKE_CASE      =				[t[1] for t in batch]
          assert len(A__      ) == len(A__      )

          # Max for paddings
          _SCREAMING_SNAKE_CASE      =				max(A__      )

          # Pad token ids
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""pad_token"""]
          else:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""unk_token"""]
          _SCREAMING_SNAKE_CASE      =				[list(t.astype(A__      )      ) + [pad_idx] * (max_seq_len_ - len(A__      )) for t in token_ids]
          assert len(tk_      ) == len(A__      )
          assert all(len(A__      ) == max_seq_len_ for t in tk_      )

          _SCREAMING_SNAKE_CASE      =				torch.tensor(tk_      )  # (bs, max_seq_len_)
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )  # (bs)
          return tk_t, lg_t






