
"""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


UpperCamelCase__          =	logging.get_logger(__name__)

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

UpperCamelCase__          =	{
    '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'
        ),
    },
}

UpperCamelCase__          =	{
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   Optional[int]           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   List[Any]           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Dict           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Tuple           =     ["""input_ids""", """attention_mask"""]
      UpperCamelCase							:   Dict           =     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_     ,	):
          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_     ,	)

          UpperCAmelCase__	:      Tuple     							=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
          if pre_tok_state.get('add_prefix_space'     ,	UpperCamelCase_      ) != add_prefix_space:
              UpperCAmelCase__	:      Optional[Any]     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      List[Any]     							=					add_prefix_space
              UpperCAmelCase__	:      Tuple     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					add_prefix_space

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

              UpperCAmelCase__	:      Tuple     							=					False

              if state.get('add_prefix_space'     ,	UpperCamelCase_      ) != add_prefix_space:
                  UpperCAmelCase__	:      Optional[int]     							=					add_prefix_space
                  UpperCAmelCase__	:      int     							=					True

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

              if changes_to_apply:
                  UpperCAmelCase__	:      int     							=					getattr(UpperCamelCase_     ,	state.pop('type'      )      )
                  UpperCAmelCase__	:      Dict     							=					component_class(**UpperCamelCase_      )
                  setattr(self.backend_tokenizer     ,	UpperCamelCase_     ,	UpperCamelCase_      )



      @property
      def  __snake_case							(   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  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					AddedToken(UpperCamelCase_     ,	lstrip=UpperCamelCase_     ,	rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ) else value
          UpperCAmelCase__	:      Tuple     							=					value



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      Optional[int]     							=					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_      ):
          UpperCAmelCase__	:      Union[str, 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      ):
          UpperCAmelCase__	:      List[str]     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      List[Any]     							=					[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      ):
          UpperCAmelCase__	:      str     							=					[self.sep_token_id]
          UpperCAmelCase__	:      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 + sep + token_ids_a + sep      ) * [0]



"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

