





"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor
       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       __magic_name__	:str						    =   1






       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 2_0_0_0       ,					__UpperCAmelCase = 0.15       ,					__UpperCAmelCase = 0.01       ,					__UpperCAmelCase = 13_48.0       ,					__UpperCAmelCase = 1E-5       ,					__UpperCAmelCase = 1       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				sigma_max

         # setable values
         lowerCAmelCase__     :str											=				None

         self.set_sigmas(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         return sample






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

         lowerCAmelCase__     :str											=				torch.linspace(1       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					device=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				sigma_min if sigma_min is not None else self.config.sigma_min
         lowerCAmelCase__     :Any											=				sigma_max if sigma_max is not None else self.config.sigma_max
         lowerCAmelCase__     :Dict											=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
           self.set_timesteps(__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         lowerCAmelCase__     :str											=				torch.exp(torch.linspace(math.log(__UpperCAmelCase )       ,					math.log(__UpperCAmelCase )       ,					__UpperCAmelCase ) )
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return torch.where(
             timesteps == 0       ,					torch.zeros_like(t.to(timesteps.device ) )       ,					self.discrete_sigmas[timesteps - 1].to(timesteps.device )       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         if self.timesteps is None:
           raise ValueError(
               '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

         lowerCAmelCase__     :Dict											=				timestep * torch.ones(
             sample.shape[0]       ,					device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
         lowerCAmelCase__     :Tuple											=				(timestep * (len(self.timesteps ) - 1)).long()

         # mps requires indices to be in the same device, so we use cpu as is the default with cuda
         lowerCAmelCase__     :List[str]											=				timesteps.to(self.discrete_sigmas.device )

         lowerCAmelCase__     :Union[str, Any]											=				self.discrete_sigmas[timesteps].to(sample.device )
         lowerCAmelCase__     :Optional[Any]											=				self.get_adjacent_sigma(__UpperCAmelCase       ,					__UpperCAmelCase ).to(sample.device )
         lowerCAmelCase__     :Optional[Any]											=				torch.zeros_like(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				(sigma**2 - adjacent_sigma**2) ** 0.5

         # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
         # also equation 47 shows the analog from SDE models to ancestral sampling methods
         lowerCAmelCase__     :int											=				diffusion.flatten()
         while len(diffusion.shape ) < len(sample.shape ):
           lowerCAmelCase__     :List[Any]											=				diffusion.unsqueeze(-1 )
         lowerCAmelCase__     :Any											=				drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         lowerCAmelCase__     :Optional[int]											=				randn_tensor(
             sample.shape       ,					layout=sample.layout       ,					generator=__UpperCAmelCase       ,					device=sample.device       ,					dtype=sample.dtype )
         lowerCAmelCase__     :Union[str, Any]											=				sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         lowerCAmelCase__     :List[str]											=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

         if not return_dict:
           return (prev_sample, prev_sample_mean)

         return SdeVeOutput(prev_sample=__UpperCAmelCase       ,					prev_sample_mean=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         if self.timesteps is None:
           raise ValueError(
               '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         lowerCAmelCase__     :str											=				randn_tensor(sample.shape       ,					layout=sample.layout       ,					generator=__UpperCAmelCase ).to(sample.device )

         # compute step size from the model_output, the noise, and the snr
         lowerCAmelCase__     :Optional[Any]											=				torch.norm(model_output.reshape(model_output.shape[0]       ,					-1 )       ,					dim=-1 ).mean()
         lowerCAmelCase__     :Any											=				torch.norm(noise.reshape(noise.shape[0]       ,					-1 )       ,					dim=-1 ).mean()
         lowerCAmelCase__     :Tuple											=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
         lowerCAmelCase__     :Dict											=				step_size * torch.ones(sample.shape[0] ).to(sample.device )
         # self.repeat_scalar(step_size, sample.shape[0])

         # compute corrected sample: model_output term and noise term
         lowerCAmelCase__     :Tuple											=				step_size.flatten()
         while len(step_size.shape ) < len(sample.shape ):
           lowerCAmelCase__     :Optional[int]											=				step_size.unsqueeze(-1 )
         lowerCAmelCase__     :List[str]											=				sample + step_size * model_output
         lowerCAmelCase__     :List[str]											=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				timesteps.to(original_samples.device )
         lowerCAmelCase__     :Optional[Any]											=				self.discrete_sigmas.to(original_samples.device )[timesteps]
         lowerCAmelCase__     :Dict											=				(
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(__UpperCAmelCase ) * sigmas[:, None, None, None]
         )
         lowerCAmelCase__     :List[Any]											=				noise + original_samples
         return noisy_samples






       def __len__(       self ):



         '''simple docstring'''

         return self.config.num_train_timesteps







"""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_roberta import RobertaTokenizer


__A											=						logging.get_logger(__name__)

__A											=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__A											=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

__A											=						{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:str						    =   VOCAB_FILES_NAMES
       __magic_name__	:List[Any]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Optional[Any]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__	:str						    =   ["""input_ids""", """attention_mask"""]
       __magic_name__	:Any						    =   RobertaTokenizer






       def __init__(       self       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase="replace"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="<mask>"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					tokenizer_file=__UpperCAmelCase       ,					errors=__UpperCAmelCase       ,					bos_token=__UpperCAmelCase       ,					eos_token=__UpperCAmelCase       ,					sep_token=__UpperCAmelCase       ,					cls_token=__UpperCAmelCase       ,					unk_token=__UpperCAmelCase       ,					pad_token=__UpperCAmelCase       ,					mask_token=__UpperCAmelCase       ,					add_prefix_space=__UpperCAmelCase       ,					trim_offsets=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Dict											=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get('add_prefix_space'       ,					__UpperCAmelCase ) != add_prefix_space:
           lowerCAmelCase__     :Optional[int]											=				getattr(__UpperCAmelCase       ,					pre_tok_state.pop('type' ) )
           lowerCAmelCase__     :List[Any]											=				add_prefix_space
           lowerCAmelCase__     :str											=				pre_tok_class(**__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				add_prefix_space

         lowerCAmelCase__     :str											=				'post_processor'
         lowerCAmelCase__     :Optional[Any]											=				getattr(self.backend_tokenizer       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         if tokenizer_component_instance:
           lowerCAmelCase__     :Optional[Any]											=				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:
             lowerCAmelCase__     :Any											=				tuple(state['sep'] )
           if "cls" in state:
             lowerCAmelCase__     :int											=				tuple(state['cls'] )

           lowerCAmelCase__     :List[Any]											=				False

           if state.get('add_prefix_space'       ,					__UpperCAmelCase ) != add_prefix_space:
             lowerCAmelCase__     :Union[str, Any]											=				add_prefix_space
             lowerCAmelCase__     :Any											=				True

           if state.get('trim_offsets'       ,					__UpperCAmelCase ) != trim_offsets:
             lowerCAmelCase__     :Union[str, Any]											=				trim_offsets
             lowerCAmelCase__     :Optional[int]											=				True

           if changes_to_apply:
             lowerCAmelCase__     :str											=				getattr(__UpperCAmelCase       ,					state.pop('type' ) )
             lowerCAmelCase__     :Any											=				component_class(**__UpperCAmelCase )
             setattr(self.backend_tokenizer       ,					__UpperCAmelCase       ,					__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         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 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else value
         lowerCAmelCase__     :List[str]											=				value






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('is_split_into_words'       ,					__UpperCAmelCase )
         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(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				kwargs.get('is_split_into_words'       ,					__UpperCAmelCase )

         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(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self._tokenizer.model.save(__UpperCAmelCase       ,					name=__UpperCAmelCase )
         return tuple(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				[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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[self.sep_token_id]
         lowerCAmelCase__     :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]
