
'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class  _UpperCAmelCase ( lowerCAmelCase__    ):

					"""simple docstring"""

					a_    	=					(CMStochasticIterativeScheduler,)
					a_    	=					10


					def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	List[str]      =							{
							    """num_train_timesteps""": 2_01,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**lowerCAmelCase_ )
							return config


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Any      =							10

							a_      :	int      =							self.get_scheduler_config()
							a_      :	Optional[Any]      =							self.scheduler_classes[0](**lowerCAmelCase_ )

							scheduler.set_timesteps(lowerCAmelCase_ )

							a_      :	int      =							scheduler.timesteps[0]
							a_      :	Any      =							scheduler.timesteps[1]

							a_      :	Optional[int]      =							self.dummy_sample
							a_      :	str      =							0.1 * sample

							a_      :	Any      =							scheduler.step(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ).prev_sample
							a_      :	Optional[Any]      =							scheduler.step(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ).prev_sample

							self.assertEqual(output_a.shape							,		sample.shape )
							self.assertEqual(output_a.shape							,		output_a.shape )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	str      =							self.scheduler_classes[0]
							a_      :	Dict      =							self.get_scheduler_config()
							a_      :	Optional[Any]      =							scheduler_class(**lowerCAmelCase_ )

							a_      :	Any      =							1
							scheduler.set_timesteps(lowerCAmelCase_ )
							a_      :	Optional[int]      =							scheduler.timesteps

							a_      :	str      =							torch.manual_seed(0 )

							a_      :	Tuple      =							self.dummy_model()
							a_      :	Optional[Any]      =							self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(lowerCAmelCase_ ):
									# 1. scale model input
									a_      :	Tuple      =							scheduler.scale_model_input(lowerCAmelCase_							,		lowerCAmelCase_ )

									# 2. predict noise residual
									a_      :	List[Any]      =							model(lowerCAmelCase_							,		lowerCAmelCase_ )

									# 3. predict previous sample x_t-1
									a_      :	Union[str, Any]      =							scheduler.step(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		generator=lowerCAmelCase_ ).prev_sample

									a_      :	int      =							pred_prev_sample

							a_      :	str      =							torch.sum(torch.abs(lowerCAmelCase_ ) )
							a_      :	str      =							torch.mean(torch.abs(lowerCAmelCase_ ) )

							assert abs(result_sum.item() - 192.7614 ) < 1E-2
							assert abs(result_mean.item() - 0.2510 ) < 1E-3


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Union[str, Any]      =							self.scheduler_classes[0]
							a_      :	int      =							self.get_scheduler_config()
							a_      :	str      =							scheduler_class(**lowerCAmelCase_ )

							a_      :	int      =							[1_06, 0]
							scheduler.set_timesteps(timesteps=lowerCAmelCase_ )
							a_      :	int      =							scheduler.timesteps

							a_      :	List[str]      =							torch.manual_seed(0 )

							a_      :	List[str]      =							self.dummy_model()
							a_      :	Optional[Any]      =							self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
									# 1. scale model input
									a_      :	Tuple      =							scheduler.scale_model_input(lowerCAmelCase_							,		lowerCAmelCase_ )

									# 2. predict noise residual
									a_      :	Optional[int]      =							model(lowerCAmelCase_							,		lowerCAmelCase_ )

									# 3. predict previous sample x_t-1
									a_      :	List[Any]      =							scheduler.step(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		generator=lowerCAmelCase_ ).prev_sample

									a_      :	Optional[int]      =							pred_prev_sample

							a_      :	Optional[Any]      =							torch.sum(torch.abs(lowerCAmelCase_ ) )
							a_      :	Optional[Any]      =							torch.mean(torch.abs(lowerCAmelCase_ ) )

							assert abs(result_sum.item() - 347.6357 ) < 1E-2
							assert abs(result_mean.item() - 0.4527 ) < 1E-3


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	List[Any]      =							self.scheduler_classes[0]
							a_      :	List[str]      =							self.get_scheduler_config()
							a_      :	Any      =							scheduler_class(**lowerCAmelCase_ )

							a_      :	Any      =							[39, 30, 12, 15, 0]

							with self.assertRaises(lowerCAmelCase_							,		msg="""`timesteps` must be in descending order.""" ):
									scheduler.set_timesteps(timesteps=lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	int      =							self.scheduler_classes[0]
							a_      :	Union[str, Any]      =							self.get_scheduler_config()
							a_      :	Tuple      =							scheduler_class(**lowerCAmelCase_ )

							a_      :	str      =							[39, 30, 12, 1, 0]
							a_      :	Tuple      =							len(lowerCAmelCase_ )

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






					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

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

							a_      :	str      =							[scheduler.config.num_train_timesteps]

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




'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case:  Optional[Any]								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{"vocab_file": "sentencepiece.model"}

__snake_case:  int								=			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

__snake_case:  Union[str, Any]								=			{
    "google/rembert": 2_56,
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					VOCAB_FILES_NAMES
     a_    	=					PRETRAINED_VOCAB_FILES_MAP
     a_    	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[UNK]"							,		lowerCAmelCase_="[SEP]"							,		lowerCAmelCase_="[PAD]"							,		lowerCAmelCase_="[CLS]"							,		lowerCAmelCase_="[MASK]"							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(
           do_lower_case=lowerCAmelCase_							,		remove_space=lowerCAmelCase_							,		keep_accents=lowerCAmelCase_							,		bos_token=lowerCAmelCase_							,		eos_token=lowerCAmelCase_							,		unk_token=lowerCAmelCase_							,		sep_token=lowerCAmelCase_							,		pad_token=lowerCAmelCase_							,		cls_token=lowerCAmelCase_							,		mask_token=lowerCAmelCase_							,		**lowerCAmelCase_							,		)

       a_      :	int      =							do_lower_case
       a_      :	int      =							remove_space
       a_      :	Dict      =							keep_accents
       a_      :	Union[str, Any]      =							vocab_file

       a_      :	List[str]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(lowerCAmelCase_ )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return len(self.sp_model )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							{self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab


     def __getstate__(      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.__dict__.copy()
       a_      :	Optional[Any]      =							None
       return state


     def __setstate__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							d
       a_      :	Optional[int]      =							spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=False ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.EncodeAsPieces(lowerCAmelCase_ )
       return pieces


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.PieceToId(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.IdToPiece(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							self.sp_model.decode_pieces(lowerCAmelCase_ )
       return out_string


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[Any]      =							[self.sep_token_id]
       a_      :	Tuple      =							[self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = False ):

       '''simple docstring'''

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               """You should not supply a second sequence if the provided sequence of """
               """ids is already formatted with special tokens for the model.""" )
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(lowerCAmelCase_ )) + [1] + ([0] * len(lowerCAmelCase_ )) + [1]
       return [1] + ([0] * len(lowerCAmelCase_ )) + [1]


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	Optional[int]      =							[self.sep_token_id]
       a_      :	Optional[Any]      =							[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if not os.path.isdir(lowerCAmelCase_ ):
         logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCAmelCase_ ) )
         return
       a_      :	List[str]      =							os.path.join(
           lowerCAmelCase_							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ):
         copyfile(self.vocab_file							,		lowerCAmelCase_ )

       return (out_vocab_file,)


