




"""simple docstring"""





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


A				=							logging.get_logger(__name__)

A				=							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

A				=							{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

A				=							{
    """gpt-neox-20b""": 2_048,
}



class   a__					(						__magic_name__ ):
			lowercase_		=						VOCAB_FILES_NAMES
			lowercase_		=						PRETRAINED_VOCAB_FILES_MAP
			lowercase_		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase_		=						["input_ids", "attention_mask"]


			def __init__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     Any=None   ,	UpperCamelCase_   :     Optional[Any]=None   ,	UpperCamelCase_   :     str=None   ,	UpperCamelCase_   :     int="<|endoftext|>"   ,	UpperCamelCase_   :     Union[str, Any]="<|endoftext|>"   ,	UpperCamelCase_   :     Tuple="<|endoftext|>"   ,	UpperCamelCase_   :     Optional[int]=False   ,	**UpperCamelCase_   :     Optional[int]   ,	):


						"""simple docstring"""




						super().__init__(
						    UpperCamelCase_   ,	UpperCamelCase_   ,	tokenizer_file=UpperCamelCase_   ,	unk_token=UpperCamelCase_   ,	bos_token=UpperCamelCase_   ,	eos_token=UpperCamelCase_   ,	add_prefix_space=UpperCamelCase_   ,	**UpperCamelCase_   ,	)

						__UpperCAmelCase						:			Union[str, Any]												=       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						:			int												=       add_prefix_space
									__UpperCAmelCase						:			str												=       pre_tok_class(**UpperCamelCase_)

						__UpperCAmelCase						:			Union[str, Any]												=       add_prefix_space


			def    a_  (   self   :     List[Any]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Optional[str] = None):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self._tokenizer.model.save(UpperCamelCase_   ,	name=UpperCamelCase_)
						return tuple(UpperCamelCase_)


			def    a_  (   self   :     Optional[Any]   ,	UpperCamelCase_   :     "Conversation"):


						"""simple docstring"""




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

						if len(UpperCamelCase_) > self.model_max_length:
									__UpperCAmelCase						:			int												=       input_ids[-self.model_max_length :]
						return input_ids





'''simple docstring'''




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 							lowercase_						(		nn.Module			):

  '''simple docstring'''





  def __init__(  self ,			a_ = 1_6 ,			a_ = 8_8 ,			a_ = None ,			a_ = 1 ,			a_ = 0.0 ,			a_ = 3_2 ,			a_ = None ,			a_ = False ,			a_ = None ,			a_ = None ,			a_ = "geglu" ,			a_ = None ,			)				->					List[str]:


    """simple docstring"""
    super().__init__()
    UpperCAmelCase    				=						nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=a_ ,			attention_head_dim=a_ ,			in_channels=a_ ,			num_layers=a_ ,			dropout=a_ ,			norm_num_groups=a_ ,			cross_attention_dim=a_ ,			attention_bias=a_ ,			sample_size=a_ ,			num_vector_embeds=a_ ,			activation_fn=a_ ,			num_embeds_ada_norm=a_ ,			)
            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
    UpperCAmelCase    				=						0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase    				=						[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])`
    UpperCAmelCase    				=						[1, 0]





  def     snake_case_      (  self ,			a_ ,			a_ ,			a_=None ,			a_=None ,			a_=None ,			a_ = True ,			)				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_states

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

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

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=a_  )


