




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


snake_case_ : Tuple    		=       logging.get_logger(__name__)

snake_case_ : str    		=       {"tokenizer_file": "tokenizer.json"}

snake_case_ : Tuple    		=       {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    List[Any]          =  VOCAB_FILES_NAMES
   UpperCAmelCase__  :    int          =  PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__  :    Any          =  ['''input_ids''', '''attention_mask''']
   UpperCAmelCase__  :    List[Any]          =  None







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

        """simple docstring"""


        super().__init__(
            _snake_case					,						_snake_case					,						tokenizer_file=_snake_case					,						unk_token=_snake_case					,						bos_token=_snake_case					,						eos_token=_snake_case					,						pad_token=_snake_case					,						add_prefix_space=_snake_case					,						clean_up_tokenization_spaces=_snake_case					,						**_snake_case					,						)
        UpperCAmelCase_          =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space'''					,						_snake_case) != add_prefix_space:
             UpperCAmelCase_          =							getattr(_snake_case					,						pre_tok_state.pop('''type'''))
             UpperCAmelCase_          =							add_prefix_space
             UpperCAmelCase_          =							pre_tok_class(**_snake_case)

        UpperCAmelCase_          =							add_prefix_space







   def lowerCamelCase						(      self		:						Optional[Any]					,						*_snake_case		:						str					,						**_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_snake_case)
        if not (self.add_prefix_space or not is_split_into_words):
             raise Exception(
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                 ''' pretokenized inputs.''')

        return super()._batch_encode_plus(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]					,						*_snake_case		:						List[str]					,						**_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_snake_case)

        if not (self.add_prefix_space or not is_split_into_words):
             raise Exception(
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                 ''' pretokenized inputs.''')

        return super()._encode_plus(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						int					,						_snake_case		:						Any = None):

        """simple docstring"""


        UpperCAmelCase_          =							self._tokenizer.model.save(_snake_case					,						name=_snake_case)
        return tuple(_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(_snake_case					,						add_special_tokens=_snake_case) + [self.eos_token_id])

        if len(_snake_case) > self.model_max_length:
             UpperCAmelCase_          =							input_ids[-self.model_max_length :]
        return input_ids










from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

