





"""simple docstring"""






import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

lowerCAmelCase_			=							logging.get_logger(__name__)


lowerCAmelCase_			=							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_			=							{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCAmelCase_			=							{'''facebook/blenderbot-3B''': 128}



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






       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
       # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
       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.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]






       def 							__A (   self    ,  a__    ,  a__ = None     ):
              return token_ids_a + [self.eos_token_id]






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     []
              for is_user, text in conversation.iter_texts():
                     if is_user:
                            # We need to space prefix as it's being done within blenderbot
                            inputs.append(' ' + text     )
                     else:
                            # Generated responses should contain them already.
                            inputs.append(a__     )

              _UpperCAmelCase     =     '  '.join(a__     )
              _UpperCAmelCase     =     self.encode(a__     )
              if len(a__     ) > self.model_max_length:
                     _UpperCAmelCase     =     input_ids[-self.model_max_length :]
                     logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""     )
              return input_ids













"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)



@dataclass
class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  42


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker






