


'''simple docstring'''
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




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






      def lowercase_				(   self							:       Optional[int]		,		**_A							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							{
              '''num_train_timesteps''': 1_000,
              '''beta_start''': 0.0_0_0_1,
              '''beta_end''': 0.0_2,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_A					)
          return config






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=_A					)






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]		,		[0.0_0_2, 0.0_2, 0.2, 2]					):
              self.check_over_configs(beta_start=_A		,		beta_end=_A					)






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_A					)






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						int						=							self.get_scheduler_config()
          UpperCAmelCase__     :						Dict						=							scheduler_class(**_A					)

          assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_0_9_7_9					)					) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.0_2					)					) < 1e-5






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						str						=							self.get_scheduler_config()
          UpperCAmelCase__     :						Any						=							scheduler_class(**_A					)

          UpperCAmelCase__     :						List[str]						=							len(_A					)

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

          for t in reversed(range(_A					)					):
              # 1. predict noise residual
              UpperCAmelCase__     :						int						=							model(_A		,		_A					)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase__     :						List[Any]						=							scheduler.step(_A		,		_A		,		_A		,		generator=_A					).prev_sample

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

          UpperCAmelCase__     :						List[Any]						=							torch.sum(torch.abs(_A					)					)
          UpperCAmelCase__     :						List[Any]						=							torch.mean(torch.abs(_A					)					)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6					) < 1e-2
          assert abs(result_mean.item() - 0.3_3_7_2					) < 1e-3






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						List[str]						=							self.get_scheduler_config(prediction_type='''v_prediction'''					)
          UpperCAmelCase__     :						Tuple						=							scheduler_class(**_A					)

          UpperCAmelCase__     :						Optional[Any]						=							len(_A					)

          UpperCAmelCase__     :						Any						=							self.dummy_model()
          UpperCAmelCase__     :						Union[str, Any]						=							self.dummy_sample_deter
          UpperCAmelCase__     :						Tuple						=							torch.manual_seed(0					)

          for t in reversed(range(_A					)					):
              # 1. predict noise residual
              UpperCAmelCase__     :						int						=							model(_A		,		_A					)

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

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

          UpperCAmelCase__     :						Optional[Any]						=							torch.sum(torch.abs(_A					)					)
          UpperCAmelCase__     :						List[str]						=							torch.mean(torch.abs(_A					)					)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6					) < 1e-2
          assert abs(result_mean.item() - 0.2_6_3_1					) < 1e-3






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						Dict						=							self.get_scheduler_config()
          UpperCAmelCase__     :						Dict						=							scheduler_class(**_A					)

          UpperCAmelCase__     :						List[str]						=							[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_A					)

          UpperCAmelCase__     :						Tuple						=							scheduler.timesteps

          for i, timestep in enumerate(_A					):
              if i == len(_A					) - 1:
                  UpperCAmelCase__     :						Optional[int]						=							-1
              else:
                  UpperCAmelCase__     :						Union[str, Any]						=							timesteps[i + 1]

              UpperCAmelCase__     :						str						=							scheduler.previous_timestep(_A					)
              UpperCAmelCase__     :						List[Any]						=							prev_t.item()

              self.assertEqual(_A		,		_A					)






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						Any						=							self.get_scheduler_config()
          UpperCAmelCase__     :						Dict						=							scheduler_class(**_A					)

          UpperCAmelCase__     :						int						=							[100, 87, 50, 51, 0]

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






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						Optional[Any]						=							self.get_scheduler_config()
          UpperCAmelCase__     :						Union[str, Any]						=							scheduler_class(**_A					)

          UpperCAmelCase__     :						Dict						=							[100, 87, 50, 1, 0]
          UpperCAmelCase__     :						List[Any]						=							len(_A					)

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






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							self.scheduler_classes[0]
          UpperCAmelCase__     :						List[str]						=							self.get_scheduler_config()
          UpperCAmelCase__     :						List[str]						=							scheduler_class(**_A					)

          UpperCAmelCase__     :						Dict						=							[scheduler.config.num_train_timesteps]

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





"""simple docstring"""

from typing import Dict, List, Optional

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


A_						:    Tuple				=      logging.get_logger(__name__)


A_						:    int				=      {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
A_						:    List[Any]				=      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A_						:    Optional[Any]				=      0

A_						:    int				=      0xE000
A_						:    Any				=      0xE001
A_						:    List[Any]				=      0xE002
A_						:    Optional[Any]				=      0xE003
A_						:    int				=      0xE004

# Maps special codepoints to human-readable names.
A_						:    Dict[int, str]				=      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
A_						:    Dict[str, int]				=      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Tuple=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=2_048     ,       **_SCREAMING_SNAKE_CASE				:       Tuple     ,       )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else bos_token
					SCREAMING_SNAKE_CASE						:							Tuple							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else eos_token
					SCREAMING_SNAKE_CASE						:							Any							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else sep_token
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else cls_token
					SCREAMING_SNAKE_CASE						:							Dict							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else mask_token

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

					# Creates a mapping for looking up the IDs of special symbols.
					SCREAMING_SNAKE_CASE						:							Dict[str, int]							=     {}
					for codepoint, name in SPECIAL_CODEPOINTS.items():
									SCREAMING_SNAKE_CASE						:							str							=     codepoint

					# Creates a mapping for looking up the string forms of special symbol IDs.
					SCREAMING_SNAKE_CASE						:							Dict[int, str]							=     {
					    codepoint: name for name, codepoint in self._special_codepoints.items()
					}

					SCREAMING_SNAKE_CASE						:							Dict							=     UNICODE_VOCAB_SIZE
					SCREAMING_SNAKE_CASE						:							List[str]							=     len(self._special_codepoints    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	int:





					"""simple docstring"""


					return self._unicode_vocab_size





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	List[str]:





					"""simple docstring"""


					return list(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	int:





					"""simple docstring"""


					try:
									return ord(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid token: '{token}'"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	str:





					"""simple docstring"""


					try:
									if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
									return chr(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid id: {index}"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	List[str]:





					"""simple docstring"""


					return "".join(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Tuple							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Any							=     cls + token_ids_a + sep
					if token_ids_a is not None:
									result += token_ids_a + sep
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[int]:





					"""simple docstring"""


					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_SCREAMING_SNAKE_CASE     ,       token_ids_a=_SCREAMING_SNAKE_CASE     ,       already_has_special_tokens=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							int							=     [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					if token_ids_a is not None:
									result += ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Dict							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     len(cls + token_ids_a + sep    ) * [0]
					if token_ids_a is not None:
									result += len(token_ids_a + sep    ) * [1]
					return result







	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Any:





					"""simple docstring"""


					return ()





