
'''simple docstring'''





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

from utils import logger


class 						__lowerCAmelCase	(	__lowercase      ):



      """simple docstring"""







      def __init__(      self		:       Tuple				,       _snake_case		:       Any				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			Any	    =       params

          __lowercase		:			List[str]	    =       np.array(__a				)
          __lowercase		:			List[Any]	    =       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		:       Any				,       _snake_case		:       int				):
          return (self.token_ids[index], self.lengths[index])




      def __len__(      self		:       List[Any]				):
          return len(self.lengths				)




      def 		snake_case_			(      self		:       str				):
          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		:       int				):
          __lowercase		:			Tuple	    =       self.params.max_model_input_size
          __lowercase		:			Union[str, Any]	    =       self.lengths > max_len
          logger.info(F'Splitting {sum(__a				)} too long sequences.'				)

          def divide_chunks(_snake_case		:       str				,       _snake_case		:       Optional[Any]				):
              return [l[i : i + n] for i in range(0				,       len(__a				)				,       __a				)]

          __lowercase		:			int	    =       []
          __lowercase		:			Any	    =       []
          if self.params.mlm:
              __lowercase		:			Union[str, Any]	    =       self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
          else:
              __lowercase		:			Union[str, 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		:			int	    =       []
                  for sub_s in divide_chunks(seq_				,       max_len - 2				):
                      if sub_s[0] != cls_id:
                          __lowercase		:			Tuple	    =       np.insert(__a				,       0				,       __a				)
                      if sub_s[-1] != sep_id:
                          __lowercase		:			int	    =       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]				)

          __lowercase		:			Dict	    =       np.array(__a				)
          __lowercase		:			str	    =       np.array(__a				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Optional[int]	    =       len(self				)
          __lowercase		:			Union[str, Any]	    =       self.lengths > 11
          __lowercase		:			Optional[int]	    =       self.token_ids[indices]
          __lowercase		:			Tuple	    =       self.lengths[indices]
          __lowercase		:			Any	    =       len(self				)
          logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'				)




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




      def 		snake_case_			(      self		:       List[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 		snake_case_			(      self		:       List[str]				,       _snake_case		:       Optional[int]				):
          __lowercase		:			Union[str, Any]	    =       [t[0] for t in batch]
          __lowercase		:			Dict	    =       [t[1] for t in batch]
          assert len(__a				) == len(__a				)

          # Max for paddings
          __lowercase		:			Any	    =       max(__a				)

          # Pad token ids
          if self.params.mlm:
              __lowercase		:			Tuple	    =       self.params.special_tok_ids["""pad_token"""]
          else:
              __lowercase		:			Optional[Any]	    =       self.params.special_tok_ids["""unk_token"""]
          __lowercase		:			Optional[Any]	    =       [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_				)

          __lowercase		:			str	    =       torch.tensor(tk_				)  # (bs, max_seq_len_)
          __lowercase		:			List[str]	    =       torch.tensor(__a				)  # (bs)
          return tk_t, lg_t




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,       [0.0_02, 0.02, 0.2, 2]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




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




      def 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

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

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_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		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_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		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

          for i, timestep in enumerate(_snake_case				):
              if i == len(_snake_case				) - 1:
                  __lowercase		:			Optional[Any]	    =       -1
              else:
                  __lowercase		:			List[str]	    =       timesteps[i + 1]

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       [100, 87, 50, 51, 0]

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




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			Union[str, Any]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       len(_snake_case				)

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




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [scheduler.config.num_train_timesteps]

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


