
'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					(UnCLIPScheduler,)


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							{
           """num_train_timesteps""": 10_00,
           """variance_type""": """fixed_small_log""",
           """clip_sample""": True,
           """clip_sample_range""": 1.0,
           """prediction_type""": """epsilon""",
       }

       config.update(**lowerCAmelCase_ )
       return config


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for variance in ["fixed_small_log", "learned_range"]:
         self.check_over_configs(variance_type=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for clip_sample_range in [1, 5, 10, 20]:
         self.check_over_configs(clip_sample_range=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for time_step in [0, 5_00, 9_99]:
         for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
           if prev_timestep is not None and prev_timestep >= time_step:
             continue

           self.check_over_forward(time_step=lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_0_0_0E-1_0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0549625 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.9994987 ) ) < 1E-5


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.scheduler_classes[0]
       a_      :	Optional[int]      =							self.get_scheduler_config(variance_type="""learned_range""" )
       a_      :	str      =							scheduler_class(**lowerCAmelCase_ )

       a_      :	Any      =							0.5

       assert scheduler._get_variance(1							,		predicted_variance=lowerCAmelCase_ ) - -10.1712790 < 1E-5
       assert scheduler._get_variance(4_87							,		predicted_variance=lowerCAmelCase_ ) - -5.7998052 < 1E-5
       assert scheduler._get_variance(9_99							,		predicted_variance=lowerCAmelCase_ ) - -0.0010011 < 1E-5


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       a_      :	Union[str, Any]      =							scheduler.timesteps

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

       for i, t in enumerate(lowerCAmelCase_ ):
         # 1. predict noise residual
         a_      :	List[str]      =							model(lowerCAmelCase_							,		lowerCAmelCase_ )

         # 2. predict previous mean of sample x_t-1
         a_      :	Any      =							scheduler.step(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		generator=lowerCAmelCase_ ).prev_sample

         a_      :	List[Any]      =							pred_prev_sample

       a_      :	Union[str, Any]      =							torch.sum(torch.abs(lowerCAmelCase_ ) )
       a_      :	Tuple      =							torch.mean(torch.abs(lowerCAmelCase_ ) )

       assert abs(result_sum.item() - 252.2682495 ) < 1E-2
       assert abs(result_mean.item() - 0.3284743 ) < 1E-3


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       scheduler.set_timesteps(25 )

       a_      :	Optional[int]      =							scheduler.timesteps

       a_      :	Dict      =							self.dummy_model()
       a_      :	Any      =							self.dummy_sample_deter
       a_      :	str      =							torch.manual_seed(0 )

       for i, t in enumerate(lowerCAmelCase_ ):
         # 1. predict noise residual
         a_      :	List[Any]      =							model(lowerCAmelCase_							,		lowerCAmelCase_ )

         if i + 1 == timesteps.shape[0]:
           a_      :	Tuple      =							None
         else:
           a_      :	Optional[int]      =							timesteps[i + 1]

         # 2. predict previous mean of sample x_t-1
         a_      :	Any      =							scheduler.step(
             lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_							,		generator=lowerCAmelCase_ ).prev_sample

         a_      :	List[str]      =							pred_prev_sample

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

       assert abs(result_sum.item() - 258.2044983 ) < 1E-2
       assert abs(result_mean.item() - 0.3362038 ) < 1E-3


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass




'''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,)


