






# 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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	42
				a__       							=	None







def   __lowercase       (			snake_case,	snake_case=0.999,	snake_case="cosine",	):







				"""simple docstring"""






				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				__magic_name__		:Optional[int]   =   []
				for i in range(snake_case			):
								__magic_name__		:Union[str, Any]   =   i / num_diffusion_timesteps
								__magic_name__		:str   =   (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(snake_case			) / alpha_bar_fn(snake_case			),	snake_case			)			)
				return torch.tensor(snake_case,	dtype=torch.floataa			)

class     lowerCamelCase_					(   lowerCamelCase      ,					lowerCamelCase			):
				a__       							=	1







				@register_to_config
				def __init__(    self							,       __lowerCAmelCase = 1_0_0_0							,       __lowerCAmelCase = 0.0001							,       __lowerCAmelCase = 0.02							,       __lowerCAmelCase = "linear"							,       __lowerCAmelCase = None							,       __lowerCAmelCase = True							,       __lowerCAmelCase = True							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = "epsilon"							,       __lowerCAmelCase = 1.0							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								if kwargs.get('''set_alpha_to_one'''							,       __lowerCAmelCase  ) is not None:
												__magic_name__		:int   =   (
												    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
												)
												deprecate('''set_alpha_to_one'''							,       '''1.0.0'''							,       __lowerCAmelCase							,       standard_warn=__lowerCAmelCase  )
												__magic_name__		:List[str]   =   kwargs['''set_alpha_to_one''']
								if trained_betas is not None:
												__magic_name__		:Union[str, Any]   =   torch.tensor(__lowerCAmelCase							,       dtype=torch.floataa  )
								elif beta_schedule == "linear":
												__magic_name__		:str   =   torch.linspace(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       dtype=torch.floataa  )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												__magic_name__		:Union[str, Any]   =   (
												    torch.linspace(beta_start**0.5							,       beta_end**0.5							,       __lowerCAmelCase							,       dtype=torch.floataa  ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												__magic_name__		:Optional[Any]   =   betas_for_alpha_bar(__lowerCAmelCase  )
								else:
												raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

								__magic_name__		:Tuple   =   1.0 - self.betas
								__magic_name__		:List[str]   =   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.
								__magic_name__		:Dict   =   torch.tensor(0.0  ) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								__magic_name__		:Tuple   =   1.0

								# setable values
								__magic_name__		:Dict   =   None
								__magic_name__		:Optional[int]   =   torch.from_numpy(np.arange(0							,       __lowerCAmelCase  ).copy().astype(np.intaa  )  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None  ):
								"""simple docstring"""
								return sample







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None  ):
								"""simple docstring"""

								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.'''  )

								__magic_name__		:Optional[Any]   =   num_inference_steps
								__magic_name__		:Dict   =   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
								__magic_name__		:Union[str, Any]   =   (np.arange(0							,       __lowerCAmelCase  ) * step_ratio).round().copy().astype(np.intaa  )
								__magic_name__		:Dict   =   torch.from_numpy(__lowerCAmelCase  ).to(__lowerCAmelCase  )
								self.timesteps += self.config.steps_offset







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 0.0							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = True							,       ):
								"""simple docstring"""
								# 1. get previous step value (=t+1)
								__magic_name__		:Union[str, Any]   =   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
								__magic_name__		:Any   =   self.alphas_cumprod[timestep]
								__magic_name__		:List[str]   =   (
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

								__magic_name__		:int   =   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":
												__magic_name__		:List[Any]   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												__magic_name__		:str   =   model_output
								elif self.config.prediction_type == "sample":
												__magic_name__		:int   =   model_output
												__magic_name__		:List[Any]   =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
												__magic_name__		:Optional[Any]   =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
												__magic_name__		:int   =   (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:
												__magic_name__		:Tuple   =   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
								__magic_name__		:List[Any]   =   (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
								__magic_name__		:Any   =   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=__lowerCAmelCase							,       pred_original_sample=__lowerCAmelCase  )







				def __len__(    self  ):
								"""simple docstring"""
								return self.config.num_train_timesteps














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

from utils import logger
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   params

								__magic_name__		:Any   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.array([len(__lowerCAmelCase  ) 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							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return (self.token_ids[index], self.lengths[index])







				def __len__(    self  ):
								"""simple docstring"""
								return len(self.lengths  )







				def       A						(    self  ):
								"""simple docstring"""
								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       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.params.max_model_input_size
								__magic_name__		:int   =   self.lengths > max_len
								logger.info(F'''Splitting {sum(__lowerCAmelCase  )} too long sequences.'''  )

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

								__magic_name__		:Optional[int]   =   []
								__magic_name__		:List[Any]   =   []
								if self.params.mlm:
												__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
												__magic_name__  ,					__magic_name__		:Tuple   =   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:
																__magic_name__		:int   =   []
																for sub_s in divide_chunks(seq_							,       max_len - 2  ):
																				if sub_s[0] != cls_id:
																								__magic_name__		:List[Any]   =   np.insert(__lowerCAmelCase							,       0							,       __lowerCAmelCase  )
																				if sub_s[-1] != sep_id:
																								__magic_name__		:Union[str, Any]   =   np.insert(__lowerCAmelCase							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )
																				assert len(__lowerCAmelCase  ) <= max_len
																				assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																				sub_seqs.append(__lowerCAmelCase  )

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

								__magic_name__		:Tuple   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   np.array(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   len(self  )
								__magic_name__		:int   =   self.lengths > 1_1
								__magic_name__		:List[str]   =   self.token_ids[indices]
								__magic_name__		:Union[str, Any]   =   self.lengths[indices]
								__magic_name__		:List[str]   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )







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







				def       A						(    self  ):
								"""simple docstring"""
								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       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   [t[0] for t in batch]
								__magic_name__		:List[Any]   =   [t[1] for t in batch]
								assert len(__lowerCAmelCase  ) == len(__lowerCAmelCase  )

								# Max for paddings
								__magic_name__		:Tuple   =   max(__lowerCAmelCase  )

								# Pad token ids
								if self.params.mlm:
												__magic_name__		:Any   =   self.params.special_tok_ids['''pad_token''']
								else:
												__magic_name__		:str   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Any   =   [list(t.astype(__lowerCAmelCase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase  )) for t in token_ids]
								assert len(tk_  ) == len(__lowerCAmelCase  )
								assert all(len(__lowerCAmelCase  ) == max_seq_len_ for t in tk_  )

								__magic_name__		:Optional[int]   =   torch.tensor(tk_  )  # (bs, max_seq_len_)
								__magic_name__		:Optional[int]   =   torch.tensor(__lowerCAmelCase  )  # (bs)
								return tk_t, lg_t






