

'''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

_UpperCamelCase						:  str							       =				logging.get_logger(__name__)


_UpperCamelCase						:  Optional[int]							       =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCamelCase						:  Any							       =				{
    "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"
    },
}

_UpperCamelCase						:  Tuple							       =				{"facebook/blenderbot-3B": 128}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       int				=     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE			:       Dict				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE			:       List[str]				=     ['''input_ids''', '''attention_mask''']
  SCREAMING_SNAKE_CASE			:       str				=     BlenderbotTokenizer

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





      '''simple docstring'''

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

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

      lowerCAmelCase 						=							add_prefix_space

      lowerCAmelCase 						=							'post_processor'
      lowerCAmelCase 						=							getattr(self.backend_tokenizer      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
      if tokenizer_component_instance:
          lowerCAmelCase 						=							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 						=							tuple(state['sep']	)
          if "cls" in state:
              lowerCAmelCase 						=							tuple(state['cls']	)

          lowerCAmelCase 						=							False

          if state.get('add_prefix_space'      , _SCREAMING_SNAKE_CASE	) != add_prefix_space:
              lowerCAmelCase 						=							add_prefix_space
              lowerCAmelCase 						=							True

          if state.get('trim_offsets'      , _SCREAMING_SNAKE_CASE	) != trim_offsets:
              lowerCAmelCase 						=							trim_offsets
              lowerCAmelCase 						=							True

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

  @property
  # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
  def   _SCREAMING_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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							AddedToken(_SCREAMING_SNAKE_CASE      , lstrip=_SCREAMING_SNAKE_CASE      , rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) else value
      lowerCAmelCase 						=							value

  def   _SCREAMING_SNAKE_CASE				(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							kwargs.get('is_split_into_words'      , _SCREAMING_SNAKE_CASE	)
      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(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							kwargs.get('is_split_into_words'      , _SCREAMING_SNAKE_CASE	)

      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(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      lowerCAmelCase 						=							self._tokenizer.model.save(_SCREAMING_SNAKE_CASE      , name=_SCREAMING_SNAKE_CASE	)
      return tuple(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      lowerCAmelCase 						=							[self.sep_token_id]
      lowerCAmelCase 						=							[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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      return token_ids_a + [self.eos_token_id]


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							[]
      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(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							'  '.join(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.encode(_SCREAMING_SNAKE_CASE	)
      if len(_SCREAMING_SNAKE_CASE	) > self.model_max_length:
          lowerCAmelCase 						=							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 torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available



@dataclass
class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[List[np.ndarray], torch.FloatTensor]


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_text_to_video_synth import TextToVideoSDPipeline
     from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
     from .pipeline_text_to_video_zero import TextToVideoZeroPipeline





