





"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_      =				{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_      =				{
    '''allenai/longformer-base-4096''': 4_096,
    '''allenai/longformer-large-4096''': 4_096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_096,
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	VOCAB_FILES_NAMES
       lowerCAmelCase__    			=	PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__    			=	["""input_ids""", """attention_mask"""]
       lowerCAmelCase__    			=	LongformerTokenizer






       def __init__( self     ,	a__=None     ,	a__=None     ,	a__=None     ,	a__="replace"     ,	a__="<s>"     ,	a__="</s>"     ,	a__="</s>"     ,	a__="<s>"     ,	a__="<unk>"     ,	a__="<pad>"     ,	a__="<mask>"     ,	a__=False     ,	a__=True     ,	**a__     ,	):
              super().__init__(
                  a__     ,	a__     ,	tokenizer_file=a__     ,	errors=a__     ,	bos_token=a__     ,	eos_token=a__     ,	sep_token=a__     ,	cls_token=a__     ,	unk_token=a__     ,	pad_token=a__     ,	mask_token=a__     ,	add_prefix_space=a__     ,	trim_offsets=a__     ,	**a__     ,	)

              _UpperCAmelCase			     =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
              if pre_tok_state.get('add_prefix_space'     ,	a__  ) != add_prefix_space:
                     _UpperCAmelCase			     =  getattr(a__     ,	pre_tok_state.pop('type'  )  )
                     _UpperCAmelCase			     =  add_prefix_space
                     _UpperCAmelCase			     =  pre_tok_class(**a__  )

              _UpperCAmelCase			     =  add_prefix_space

              _UpperCAmelCase			     =  'post_processor'
              _UpperCAmelCase			     =  getattr(self.backend_tokenizer     ,	a__     ,	a__  )
              if tokenizer_component_instance:
                     _UpperCAmelCase			     =  json.loads(tokenizer_component_instance.__getstate__()  )

                     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                     if "sep" in state:
                            _UpperCAmelCase			     =  tuple(state['sep']  )
                     if "cls" in state:
                            _UpperCAmelCase			     =  tuple(state['cls']  )

                     _UpperCAmelCase			     =  False

                     if state.get('add_prefix_space'     ,	a__  ) != add_prefix_space:
                            _UpperCAmelCase			     =  add_prefix_space
                            _UpperCAmelCase			     =  True

                     if state.get('trim_offsets'     ,	a__  ) != trim_offsets:
                            _UpperCAmelCase			     =  trim_offsets
                            _UpperCAmelCase			     =  True

                     if changes_to_apply:
                            _UpperCAmelCase			     =  getattr(a__     ,	state.pop('type'  )  )
                            _UpperCAmelCase			     =  component_class(**a__  )
                            setattr(self.backend_tokenizer     ,	a__     ,	a__  )






       @property
       def 				__A			( self  ):
              if self._mask_token is None:
                     if self.verbose:
                            logger.error('Using mask_token, but it is not set yet.'  )
                     return None
              return str(self._mask_token  )






       @mask_token.setter
       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else value
              _UpperCAmelCase			     =  value






       def 				__A			( self     ,	*a__     ,	**a__  ):
              _UpperCAmelCase			     =  kwargs.get('is_split_into_words'     ,	a__  )
              assert self.add_prefix_space or not is_split_into_words, (
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  "to use it with pretokenized inputs."
              )

              return super()._batch_encode_plus(*a__     ,	**a__  )






       def 				__A			( self     ,	*a__     ,	**a__  ):
              _UpperCAmelCase			     =  kwargs.get('is_split_into_words'     ,	a__  )

              assert self.add_prefix_space or not is_split_into_words, (
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  "to use it with pretokenized inputs."
              )

              return super()._encode_plus(*a__     ,	**a__  )






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              _UpperCAmelCase			     =  self._tokenizer.model.save(a__     ,	name=a__  )
              return tuple(a__  )






       def 				__A			( self     ,	a__     ,	a__=None  ):
              _UpperCAmelCase			     =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              _UpperCAmelCase			     =  [self.sep_token_id]
              _UpperCAmelCase			     =  [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]













"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






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






       def 				__A			( self  ):
              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=a__     ,	beta_end=a__  )






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






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






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






