


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       (DDPMParallelScheduler,)




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       a						:List[Any] 							=       {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''variance_type''': '''fixed_small''',
           '''clip_sample''': True,
       }

       config.update(**_lowerCamelCase    )
       return config




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for timesteps in [1, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,  [0.002, 0.02, 0.2, 2]    ):
             self.check_over_configs(beta_start=_lowerCamelCase					,  beta_end=_lowerCamelCase    )




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




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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_lowerCamelCase    )




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




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




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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       self.scheduler_classes[0]
       a						:Optional[Any] 							=       self.get_scheduler_config()
       a						:Any 							=       scheduler_class(**_lowerCamelCase    )

       assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_0979    )    ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1e-5




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.scheduler_classes[0]
       a						:Any 							=       self.get_scheduler_config()
       a						:Union[str, Any] 							=       scheduler_class(**_lowerCamelCase    )

       a						:Dict 							=       len(_lowerCamelCase    )

       a						:Tuple 							=       self.dummy_model()
       a						:Optional[Any] 							=       self.dummy_sample_deter
       a						:str 							=       self.dummy_sample_deter + 0.1
       a						:Optional[Any] 							=       self.dummy_sample_deter - 0.1

       a						:int 							=       samplea.shape[0]
       a						:List[str] 							=       torch.stack([samplea, samplea, samplea]					,  dim=0    )
       a						:List[str] 							=       torch.arange(_lowerCamelCase    )[0:3, None].repeat(1					,  _lowerCamelCase    )

       a						:str 							=       model(samples.flatten(0					,  1    )					,  timesteps.flatten(0					,  1    )    )
       a						:List[Any] 							=       scheduler.batch_step_no_noise(_lowerCamelCase					,  timesteps.flatten(0					,  1    )					,  samples.flatten(0					,  1    )    )

       a						:str 							=       torch.sum(torch.abs(_lowerCamelCase    )    )
       a						:str 							=       torch.mean(torch.abs(_lowerCamelCase    )    )

       assert abs(result_sum.item() - 1153.1833    ) < 1e-2
       assert abs(result_mean.item() - 0.5005    ) < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.scheduler_classes[0]
       a						:Any 							=       self.get_scheduler_config()
       a						:Dict 							=       scheduler_class(**_lowerCamelCase    )

       a						:Tuple 							=       len(_lowerCamelCase    )

       a						:Optional[int] 							=       self.dummy_model()
       a						:Optional[Any] 							=       self.dummy_sample_deter
       a						:Optional[int] 							=       torch.manual_seed(0    )

       for t in reversed(range(_lowerCamelCase    )    ):
             # 1. predict noise residual
             a						:Dict 							=       model(_lowerCamelCase					,  _lowerCamelCase    )

             # 2. predict previous mean of sample x_t-1
             a						:int 							=       scheduler.step(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  generator=_lowerCamelCase    ).prev_sample

             a						:Any 							=       pred_prev_sample

       a						:int 							=       torch.sum(torch.abs(_lowerCamelCase    )    )
       a						:Dict 							=       torch.mean(torch.abs(_lowerCamelCase    )    )

       assert abs(result_sum.item() - 258.9606    ) < 1e-2
       assert abs(result_mean.item() - 0.3372    ) < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       self.scheduler_classes[0]
       a						:List[Any] 							=       self.get_scheduler_config(prediction_type='''v_prediction'''    )
       a						:Any 							=       scheduler_class(**_lowerCamelCase    )

       a						:Tuple 							=       len(_lowerCamelCase    )

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

       for t in reversed(range(_lowerCamelCase    )    ):
             # 1. predict noise residual
             a						:Union[str, Any] 							=       model(_lowerCamelCase					,  _lowerCamelCase    )

             # 2. predict previous mean of sample x_t-1
             a						:str 							=       scheduler.step(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  generator=_lowerCamelCase    ).prev_sample

             a						:List[str] 							=       pred_prev_sample

       a						:Optional[int] 							=       torch.sum(torch.abs(_lowerCamelCase    )    )
       a						:Any 							=       torch.mean(torch.abs(_lowerCamelCase    )    )

       assert abs(result_sum.item() - 202.0296    ) < 1e-2
       assert abs(result_mean.item() - 0.2631    ) < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       self.scheduler_classes[0]
       a						:str 							=       self.get_scheduler_config()
       a						:Tuple 							=       scheduler_class(**_lowerCamelCase    )

       a						:int 							=       [100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=_lowerCamelCase    )

       a						:List[str] 							=       scheduler.timesteps

       for i, timestep in enumerate(_lowerCamelCase    ):
             if i == len(_lowerCamelCase    ) - 1:
                   a						:str 							=       -1
             else:
                   a						:List[str] 							=       timesteps[i + 1]

             a						:Union[str, Any] 							=       scheduler.previous_timestep(_lowerCamelCase    )
             a						:List[str] 							=       prev_t.item()

             self.assertEqual(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.scheduler_classes[0]
       a						:List[Any] 							=       self.get_scheduler_config()
       a						:Dict 							=       scheduler_class(**_lowerCamelCase    )

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

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.scheduler_classes[0]
       a						:Any 							=       self.get_scheduler_config()
       a						:str 							=       scheduler_class(**_lowerCamelCase    )

       a						:Tuple 							=       [100, 87, 50, 1, 0]
       a						:Optional[Any] 							=       len(_lowerCamelCase    )

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.scheduler_classes[0]
       a						:Optional[Any] 							=       self.get_scheduler_config()
       a						:Dict 							=       scheduler_class(**_lowerCamelCase    )

       a						:int 							=       [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    )









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

import sentencepiece as spm

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


snake_case				:    Dict       				=     logging.get_logger(__name__)

snake_case				:    Tuple       				=     '''▁'''

snake_case				:    Any       				=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

snake_case				:    Tuple       				=     {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

snake_case				:    int       				=     {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__      =       ['input_ids', 'attention_mask']




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase="<s>"					,  _lowerCamelCase="</s>"					,  _lowerCamelCase="</s>"					,  _lowerCamelCase="<s>"					,  _lowerCamelCase="<unk>"					,  _lowerCamelCase="<pad>"					,  _lowerCamelCase="<mask>"					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       # Mask token behave like a normal word, i.e. include the space before it
       a						:Optional[int] 							=       AddedToken(_lowerCamelCase					,  lstrip=_lowerCamelCase					,  rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase					,  _lowerCamelCase    ) else mask_token

       a						:int 							=       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_lowerCamelCase					,  eos_token=_lowerCamelCase					,  unk_token=_lowerCamelCase					,  sep_token=_lowerCamelCase					,  cls_token=_lowerCamelCase					,  pad_token=_lowerCamelCase					,  mask_token=_lowerCamelCase					,  sp_model_kwargs=self.sp_model_kwargs					,  **_lowerCamelCase					,  )

       a						:Optional[int] 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(str(_lowerCamelCase    )    )
       a						:str 							=       vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       a						:Tuple 							=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       a						:List[str] 							=       1

       a						:Dict 							=       len(self.sp_model    ) + self.fairseq_offset
       a						:List[Any] 							=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




 def __getstate__(		self    ):
       a						:List[str] 							=       self.__dict__.copy()
       a						:Optional[int] 							=       None
       a						:int 							=       self.sp_model.serialized_model_proto()
       return state




 def __setstate__(		self					,  _lowerCamelCase    ):
       a						:Union[str, Any] 							=       d

       # for backward compatibility
       if not hasattr(self					,  '''sp_model_kwargs'''    ):
             a						:Union[str, Any] 							=       {}

       a						:Optional[int] 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = 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    )

       if token_ids_a is None:
             return [1] + ([0] * len(_lowerCamelCase    )) + [1]
       return [1] + ([0] * len(_lowerCamelCase    )) + [1, 1] + ([0] * len(_lowerCamelCase    )) + [1]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       a						:int 							=       [self.sep_token_id]
       a						:int 							=       [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 + sep + token_ids_a + sep    ) * [0]




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return len(self.sp_model    ) + self.fairseq_offset + 1  # Add the <mask> token




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       {self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.sp_model.encode(_lowerCamelCase					,  out_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       a						:Optional[Any] 							=       self.sp_model.PieceToId(_lowerCamelCase    )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Tuple 							=       ''''''.join(_lowerCamelCase    ).replace(_lowerCamelCase					,  ''' '''    ).strip()
       return out_string




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       if not os.path.isdir(_lowerCamelCase    ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
             return
       a						:int 							=       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    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file					,  _lowerCamelCase    )
       elif not os.path.isfile(self.vocab_file    ):
             with open(_lowerCamelCase					,  '''wb'''    ) as fi:
                   a						:List[Any] 							=       self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase    )

       return (out_vocab_file,)





