






'''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

__A							:							Tuple  							=		logging.get_logger(__name__)


__A							:							Union[str, Any]  							=		{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__A							:							str  							=		{
    """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"""
    },
}

__A							:							str  							=		{"""facebook/blenderbot-3B""": 1_28}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							VOCAB_FILES_NAMES
    lowerCAmelCase__      				=							PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase__      				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase__      				=							["input_ids", "attention_mask"]
    lowerCAmelCase__      				=							BlenderbotTokenizer

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Dict=None				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     List[str]=None				,     __lowerCamelCase     :     List[str]="replace"				,     __lowerCamelCase     :     Union[str, Any]="<s>"				,     __lowerCamelCase     :     Dict="</s>"				,     __lowerCamelCase     :     Optional[Any]="</s>"				,     __lowerCamelCase     :     List[Any]="<s>"				,     __lowerCamelCase     :     Union[str, Any]="<unk>"				,     __lowerCamelCase     :     Optional[int]="<pad>"				,     __lowerCamelCase     :     List[str]="<mask>"				,     __lowerCamelCase     :     List[Any]=False				,     __lowerCamelCase     :     int=True				,     **__lowerCamelCase     :     Optional[Any]				,     )							->							int:

     '''simple docstring'''




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

     lowerCamelCase__  =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
     if pre_tok_state.get("add_prefix_space"				,     __lowerCamelCase     ) != add_prefix_space:
      lowerCamelCase__  =     getattr(__lowerCamelCase				,     pre_tok_state.pop("type"     )     )
      lowerCamelCase__  =     add_prefix_space
      lowerCamelCase__  =     pre_tok_class(**__lowerCamelCase     )

     lowerCamelCase__  =     add_prefix_space

     lowerCamelCase__  =     "post_processor"
     lowerCamelCase__  =     getattr(self.backend_tokenizer				,     __lowerCamelCase				,     __lowerCamelCase     )
     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"				,     __lowerCamelCase     ) != add_prefix_space:
       lowerCamelCase__  =     add_prefix_space
       lowerCamelCase__  =     True

      if state.get("trim_offsets"				,     __lowerCamelCase     ) != trim_offsets:
       lowerCamelCase__  =     trim_offsets
       lowerCamelCase__  =     True

      if changes_to_apply:
       lowerCamelCase__  =     getattr(__lowerCamelCase				,     state.pop("type"     )     )
       lowerCamelCase__  =     component_class(**__lowerCamelCase     )
       setattr(self.backend_tokenizer				,     __lowerCamelCase				,     __lowerCamelCase     )

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

     '''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    a__				(					self     :     str				,     __lowerCamelCase     :     Optional[int]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     AddedToken(__lowerCamelCase				,     lstrip=__lowerCamelCase				,     rstrip=__lowerCamelCase     ) if isinstance(__lowerCamelCase				,     __lowerCamelCase     ) else value
     lowerCamelCase__  =     value

    def    a__				(					self     :     str				,     *__lowerCamelCase     :     Tuple				,     **__lowerCamelCase     :     List[str]     )							->							BatchEncoding:

     '''simple docstring'''




     lowerCamelCase__  =     kwargs.get("is_split_into_words"				,     __lowerCamelCase     )
     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(*__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     List[Any]				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     Optional[Any]     )							->							BatchEncoding:

     '''simple docstring'''




     lowerCamelCase__  =     kwargs.get("is_split_into_words"				,     __lowerCamelCase     )

     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(*__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[str] = None     )							->							Tuple[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self._tokenizer.model.save(__lowerCamelCase				,     name=__lowerCamelCase     )
     return tuple(__lowerCamelCase     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     Optional[List[int]] = None     )							->							List[int]:

     '''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    a__				(					self     :     Dict				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     Optional[List[int]] = None     )							->							List[Any]:

     '''simple docstring'''




     return token_ids_a + [self.eos_token_id]




    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     "Conversation"     )							->							List[int]:

     '''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(__lowerCamelCase     )

     lowerCamelCase__  =     "  ".join(__lowerCamelCase     )
     lowerCamelCase__  =     self.encode(__lowerCamelCase     )
     if len(__lowerCamelCase     ) > 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 collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__A							:							Union[str, Any]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"gpt_neo"
    lowerCAmelCase__      				=							["past_key_values"]
    lowerCAmelCase__      				=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[int]=50257				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Any=24				,     __lowerCamelCase     :     List[Any]=[[["global", "local"], 12]]				,     __lowerCamelCase     :     Any=16				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Optional[Any]=256				,     __lowerCamelCase     :     List[Any]="gelu_new"				,     __lowerCamelCase     :     Any=0.0				,     __lowerCamelCase     :     List[Any]=0.0				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     __lowerCamelCase     :     Any=0.1				,     __lowerCamelCase     :     Dict=1E-5				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     List[Any]=True				,     __lowerCamelCase     :     Optional[int]=50256				,     __lowerCamelCase     :     Tuple=50256				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_layers
     lowerCamelCase__  =     num_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     window_size
     lowerCamelCase__  =     activation_function
     lowerCamelCase__  =     resid_dropout
     lowerCamelCase__  =     embed_dropout
     lowerCamelCase__  =     attention_dropout
     lowerCamelCase__  =     classifier_dropout
     lowerCamelCase__  =     layer_norm_epsilon
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     use_cache

     lowerCamelCase__  =     bos_token_id
     lowerCamelCase__  =     eos_token_id

     lowerCamelCase__  =     attention_types
     lowerCamelCase__  =     self.expand_attention_types_params(__lowerCamelCase     )

     if len(self.attention_layers     ) != self.num_layers:
      raise ValueError(
          "Configuration for convolutional module is incorrect. "
          "It is required that `len(config.attention_layers)` == `config.num_layers` "
          f'''but is `len(config.attention_layers) = {len(self.attention_layers     )}`, '''
          f'''`config.num_layers = {self.num_layers}`. '''
          "`config.attention_layers` is prepared using `config.attention_types`. "
          "Please verify the value of `config.attention_types` argument."     )

     super().__init__(bos_token_id=__lowerCamelCase				,     eos_token_id=__lowerCamelCase				,     **__lowerCamelCase     )




    @staticmethod
    def    a__				(					__lowerCamelCase     :     int     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     []
     for item in attention_types:
      for _ in range(item[1]     ):
       attentions.extend(item[0]     )
     return attentions






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     input.size()
 lowerCamelCase__  =     len(lowercase__)
 lowerCamelCase__  =     shape[dimension]

 lowerCamelCase__  =     torch.arange(0				,     lowercase__				,     lowercase__)
 lowerCamelCase__  =     torch.div(sizedim - size				,     lowercase__				,     rounding_mode="floor") + 1
 lowerCamelCase__  =     torch.arange(lowercase__) + low_indices[:min_length][:, None]

 lowerCamelCase__  =     [slice(lowercase__)] * rank
 lowerCamelCase__  =     indices
 lowerCamelCase__  =     input[s]

 lowerCamelCase__  =     list(range(0				,     rank + 1))
 perm.append(perm.pop(dimension + 1))

 return sliced.permute(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     torch.arange(1				,     lowercase__)
 lowerCamelCase__  =     torch.remainder(lowercase__				,     lowercase__)
 lowerCamelCase__  =     remainders == 0
 lowerCamelCase__  =     candidates[divisor_indices]
 lowerCamelCase__  =     torch.max(lowercase__)
 return largest_divisor, torch.div(lowercase__				,     lowercase__				,     rounding_mode="floor")
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    @property
    def    a__				(					self     :     Any     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     lowerCamelCase__  =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
     if self.use_past:
      self.fill_with_past_key_values_(__lowerCamelCase				,     direction="inputs"     )
      lowerCamelCase__  =     {0: "batch", 1: "past_sequence + sequence"}
     else:
      lowerCamelCase__  =     {0: "batch", 1: "sequence"}

     return common_inputs

    @property
    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     return self._config.num_heads

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     PreTrainedTokenizer				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     Optional[TensorType] = None				,     )							->							Mapping[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     super(__lowerCamelCase				,     self     ).generate_dummy_inputs(
         __lowerCamelCase				,     batch_size=__lowerCamelCase				,     seq_length=__lowerCamelCase				,     is_pair=__lowerCamelCase				,     framework=__lowerCamelCase     )

     # We need to order the input in the way they appears in the forward()
     lowerCamelCase__  =     OrderedDict({"input_ids": common_inputs["input_ids"]}     )

     # Need to add the past_keys
     if self.use_past:
      if not is_torch_available():
       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
      else:
       import torch

       lowerCamelCase__  ,	lowerCamelCase__  =     common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       lowerCamelCase__  =     seqlen + 2
       lowerCamelCase__  =     (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       lowerCamelCase__  =     [
           (torch.zeros(__lowerCamelCase     ), torch.zeros(__lowerCamelCase     )) for _ in range(self.num_layers     )
       ]

     lowerCamelCase__  =     common_inputs["attention_mask"]
     if self.use_past:
      lowerCamelCase__  =     ordered_inputs["attention_mask"].dtype
      lowerCamelCase__  =     torch.cat(
          [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase				,     __lowerCamelCase				,     dtype=__lowerCamelCase     )]				,     dim=1     )

     return ordered_inputs




    @property
    def    a__				(					self     :     Optional[Any]     )							->							int:

     '''simple docstring'''




     return 13

