





'''simple docstring'''


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 							__UpperCAmelCase  (       _UpperCAmelCase						):







  '''simple docstring'''







  __lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
  __lowerCAmelCase 				=    10







  def        A     (self						:      Tuple      ,      **_lowerCAmelCase						:      List[Any]      ):
   A							  =      {
       """num_train_timesteps""": 1100,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
   }

   config.update(**_lowerCAmelCase      )
   return config







  def        A     (self						:      int      ):






   for timesteps in [10, 50, 100, 1000]:
    self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )







  def        A     (self						:      int      ):






   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]      ):
    self.check_over_configs(beta_start=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )







  def        A     (self						:      str      ):






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







  def        A     (self						:      List[Any]      ):






   if torch_device == "mps":
    return
   A							  =      self.scheduler_classes[0]
   A							  =      self.get_scheduler_config()
   A							  =      scheduler_class(**_lowerCAmelCase      )

   scheduler.set_timesteps(self.num_inference_steps      )

   A							  =      torch.manual_seed(0      )

   A							  =      self.dummy_model()
   A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
   A							  =      sample.to(_lowerCAmelCase      )

   for i, t in enumerate(scheduler.timesteps      ):
    A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

    A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

    A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
    A							  =      output.prev_sample

   A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
   A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

   assert abs(result_sum.item() - 1_3849.3877      ) < 1e-2
   assert abs(result_mean.item() - 18.0_331      ) < 5e-3







  def        A     (self						:      int      ):






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







  def        A     (self						:      List[str]      ):






   if torch_device == "mps":
    return
   A							  =      self.scheduler_classes[0]
   A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
   A							  =      scheduler_class(**_lowerCAmelCase      )

   scheduler.set_timesteps(self.num_inference_steps      )

   A							  =      self.dummy_model()
   A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
   A							  =      sample.to(_lowerCAmelCase      )

   A							  =      torch.manual_seed(0      )

   for i, t in enumerate(scheduler.timesteps      ):
    A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

    A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

    A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
    A							  =      output.prev_sample

   A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
   A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

   assert abs(result_sum.item() - 328.9_970      ) < 1e-2
   assert abs(result_mean.item() - 0.4_284      ) < 1e-3







  def        A     (self						:      Optional[Any]      ):






   if torch_device == "mps":
    return
   A							  =      self.scheduler_classes[0]
   A							  =      self.get_scheduler_config()
   A							  =      scheduler_class(**_lowerCAmelCase      )

   scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
   A							  =      torch.manual_seed(0      )

   A							  =      self.dummy_model()
   A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma

   for t in scheduler.timesteps:
    A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

    A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

    A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
    A							  =      output.prev_sample

   A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
   A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

   assert abs(result_sum.item() - 1_3849.3818      ) < 1e-1
   assert abs(result_mean.item() - 18.0_331      ) < 1e-3








'''simple docstring'''


from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase				:	int				    =       logging.get_logger(__name__)



class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		__lowerCAmelCase 				=    ['''input_ids''', '''attention_mask''']







		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      str="[PAD]"      ,      _lowerCAmelCase						:      Dict="[BOS]"      ,      _lowerCAmelCase						:      Any="[EOS]"      ,      _lowerCAmelCase						:      List[Any]="[MASK]"      ,      _lowerCAmelCase						:      int="[CLS]"      ,      _lowerCAmelCase						:      Optional[Any]="[SEP]"      ,      _lowerCAmelCase						:      List[str]=2048      ,      **_lowerCAmelCase						:      Dict      ,      ):
			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else pad_token
			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else bos_token
			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else eos_token
			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else mask_token
			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else cls_token
			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else sep_token

			super().__init__(
			    pad_token=_lowerCAmelCase      ,      bos_token=_lowerCAmelCase      ,      eos_token=_lowerCAmelCase      ,      mask_token=_lowerCAmelCase      ,      cls_token=_lowerCAmelCase      ,      sep_token=_lowerCAmelCase      ,      model_max_length=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )

			A							  =      2**8  # utf is 8 bits

			# define special tokens dict
			A							  =      {
			    self.pad_token: 0,
			    self.bos_token: 1,
			    self.eos_token: 2,
			    self.mask_token: 3,
			    self.cls_token: 4,
			    self.sep_token: 5,
			}
			A							  =      len(self.special_tokens_encoder      )
			A							  =      {v: k for k, v in self.special_tokens_encoder.items()}







		def        A     (self						:      Tuple      ):
			A							  =      self.special_tokens_encoder.copy()
			vocab.update(self.added_tokens_encoder      )
			for i in range(self._utf_vocab_size      ):
				A							  =      chr(_lowerCAmelCase      )
				A							  =      i + len(self.special_tokens_encoder      )
			return vocab







		@property
		def        A     (self						:      Tuple      ):
			return self._utf_vocab_size + self._num_special_tokens







		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ,      _lowerCAmelCase						:      bool = False      ):
			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_lowerCAmelCase      ,      token_ids_a=_lowerCAmelCase      ,      already_has_special_tokens=_lowerCAmelCase      )

			# normal case: some special tokens
			if token_ids_a is None:
				return [1] + [0] * len(_lowerCAmelCase      ) + [1]
			return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]







		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			else:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]







		def        A     (self						:      str      ,      _lowerCAmelCase						:      str      ):
			A							  =      [chr(_lowerCAmelCase      ) for i in text.encode("""utf-8"""      )]
			return tokens







		def        A     (self						:      int      ,      _lowerCAmelCase						:      List[str]      ):
			if token in self.special_tokens_encoder:
				A							  =      self.special_tokens_encoder[token]
			elif token in self.added_tokens_encoder:
				A							  =      self.added_tokens_encoder[token]
			elif len(_lowerCAmelCase      ) != 1:
				A							  =      self.unk_token_id
			else:
				A							  =      ord(_lowerCAmelCase      ) + self._num_special_tokens
			return token_id







		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Any      ):
			if index in self.special_tokens_decoder:
				A							  =      self.special_tokens_decoder[index]
			elif index in self.added_tokens_decoder:
				A							  =      self.added_tokens_decoder[index]
			else:
				A							  =      chr(index - self._num_special_tokens      )
			return token







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ):
			A							  =      b""""""
			for token in tokens:
				if token in self.special_tokens_decoder:
					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
				elif token in self.added_tokens_decoder:
					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
				elif token in self.special_tokens_encoder:
					A							  =      token.encode("""utf-8"""      )
				elif token in self.added_tokens_encoder:
					A							  =      token.encode("""utf-8"""      )
				else:
					A							  =      bytes([ord(_lowerCAmelCase      )]      )
				bstring += tok_string
			A							  =      bstring.decode("""utf-8"""      ,      errors="""replace"""      )
			return string







		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Optional[str] = None      ):
			return ()

