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

from utils import logger






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Any													=	params

											lowercase_     :					Union[str, Any]													=	np.array(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	np.array([len(__SCREAMING_SNAKE_CASE		) 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    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return (self.token_ids[index], self.lengths[index])






					def __len__(       self		):


											"""simple docstring"""





											return len(self.lengths		)






					def      _snake_case   (       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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	self.params.max_model_input_size
											lowercase_     :					Optional[int]													=	self.lengths > max_len
											logger.info(F'''Splitting {sum(__SCREAMING_SNAKE_CASE		)} too long sequences.'''		)

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

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

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

											lowercase_     :					Any													=	np.array(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	np.array(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	[t[0] for t in batch]
											lowercase_     :					Dict													=	[t[1] for t in batch]
											assert len(__SCREAMING_SNAKE_CASE		) == len(__SCREAMING_SNAKE_CASE		)

											# Max for paddings
											lowercase_     :					Any													=	max(__SCREAMING_SNAKE_CASE		)

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

											lowercase_     :					Any													=	torch.tensor(tk_		)  # (bs, max_seq_len_)
											lowercase_     :					int													=	torch.tensor(__SCREAMING_SNAKE_CASE		)  # (bs)
											return tk_t, lg_t





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					int													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''`custom_timesteps` must be in descending order.'''		):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
																	scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __SCREAMING_SNAKE_CASE    ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,							):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)



