




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[torch.FloatTensor]    =					None
      lowerCAmelCase__			: torch.FloatTensor    =					None
      lowerCAmelCase__			: Optional[Tuple[torch.FloatTensor]]    =					None
      lowerCAmelCase__			: Optional[Tuple[torch.FloatTensor]]    =					None







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: str ,       snake_case					: List[Any]=1 ,       snake_case					: List[str]=0 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Any=512 ,       snake_case					: Optional[int]="cls" ,       snake_case					: Union[str, Any]=False ,       snake_case					: int=True ,       **snake_case					: Union[str, Any] ,       ):
          super().__init__(pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       **snake_case				)
          __UpperCamelCase  		=						project_dim
          __UpperCamelCase  		=						pooler_fn
          __UpperCamelCase  		=						learn_encoder
          __UpperCamelCase  		=						use_attention_mask







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					[R"pooler", R"logit_scale"]
      lowerCAmelCase__			: List[str]    =					[R"position_ids", R"predictions.decoder.bias"]
      lowerCAmelCase__			: Any    =					"roberta"
      lowerCAmelCase__			: int    =					RobertaSeriesConfig







      def __init__(     self					: List[Any] ,       snake_case					: Tuple				):
          super().__init__(snake_case				)
          __UpperCamelCase  		=						XLMRobertaModel(snake_case				)
          __UpperCamelCase  		=						nn.Linear(config.hidden_size ,       config.project_dim				)
          __UpperCamelCase  		=						getattr(snake_case ,       '''has_pre_transformation''' ,       snake_case				)
          if self.has_pre_transformation:
              __UpperCamelCase  		=						nn.Linear(config.hidden_size ,       config.project_dim				)
              __UpperCamelCase  		=						nn.LayerNorm(config.hidden_size ,       eps=config.layer_norm_eps				)
          self.post_init()







      def       snake_case	(     self					: Tuple ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[torch.Tensor] = None ,       snake_case					: Optional[bool] = None ,       snake_case					: Optional[bool] = None ,       snake_case					: Optional[bool] = None ,       ):
          __UpperCamelCase  		=						return_dict if return_dict is not None else self.config.use_return_dict

          __UpperCamelCase  		=						self.base_model(
              input_ids=snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       position_ids=snake_case ,       head_mask=snake_case ,       inputs_embeds=snake_case ,       encoder_hidden_states=snake_case ,       encoder_attention_mask=snake_case ,       output_attentions=snake_case ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,       return_dict=snake_case ,       )

          if self.has_pre_transformation:
              __UpperCamelCase  		=						outputs['''hidden_states'''][-2]
              __UpperCamelCase  		=						self.pre_LN(snake_case				)
              __UpperCamelCase  		=						self.transformation_pre(snake_case				)

              return TransformationModelOutput(
                  projection_state=snake_case ,       last_hidden_state=outputs.last_hidden_state ,       hidden_states=outputs.hidden_states ,       attentions=outputs.attentions ,       )
          else:
              __UpperCamelCase  		=						self.transformation(outputs.last_hidden_state				)
              return TransformationModelOutput(
                  projection_state=snake_case ,       last_hidden_state=outputs.last_hidden_state ,       hidden_states=outputs.hidden_states ,       attentions=outputs.attentions ,       )












from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Union[str, Any]    =					"rwkv"
      lowerCAmelCase__			: Union[str, Any]    =					{"max_position_embeddings": "context_length"}







      def __init__(     self					: Optional[int] ,       snake_case					: Optional[Any]=50277 ,       snake_case					: str=1024 ,       snake_case					: str=4096 ,       snake_case					: Optional[Any]=32 ,       snake_case					: Union[str, Any]=None ,       snake_case					: Optional[Any]=None ,       snake_case					: Optional[Any]=1E-5 ,       snake_case					: List[str]=0 ,       snake_case					: Optional[Any]=0 ,       snake_case					: Union[str, Any]=6 ,       snake_case					: Tuple=False ,       snake_case					: Any=True ,       **snake_case					: List[str] ,       ):
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						context_length
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						attention_hidden_size if attention_hidden_size is not None else hidden_size
          __UpperCamelCase  		=						intermediate_size if intermediate_size is not None else 4 * hidden_size
          __UpperCamelCase  		=						layer_norm_epsilon
          __UpperCamelCase  		=						rescale_every
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id

          super().__init__(
              tie_word_embeddings=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       **snake_case				)






