'''simple docstring'''

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    lowerCamelCase__  (   __snake_case       ,				__snake_case       ,				__snake_case     ):



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = False					,      )				->     Dict:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :List[Any]       =nn.Linear(lowerCAmelCase__					,      lowerCAmelCase__					,      bias=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =nn.Embedding(lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =False

        _UpperCamelCase  :Optional[Any]       =nn.Dropout(p=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =TaConfig(
            d_model=lowerCAmelCase__					,      num_heads=lowerCAmelCase__					,      d_kv=lowerCAmelCase__					,      d_ff=lowerCAmelCase__					,      feed_forward_proj=lowerCAmelCase__					,      dropout_rate=lowerCAmelCase__					,      is_decoder=lowerCAmelCase__					,      is_encoder_decoder=lowerCAmelCase__					,      )
        _UpperCamelCase  :Dict       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase__     ):
             _UpperCamelCase  :List[str]       =TaBlock(lowerCAmelCase__     )
             self.encoders.append(lowerCAmelCase__     )

        _UpperCamelCase  :str       =TaLayerNorm(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =nn.Dropout(p=lowerCAmelCase__     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :str       =self.input_proj(lowerCAmelCase__     )

        # terminal relative positional encodings
        _UpperCamelCase  :List[str]       =encoder_inputs.shape[1]
        _UpperCamelCase  :Union[str, Any]       =torch.arange(lowerCAmelCase__					,      device=encoder_inputs.device     )

        _UpperCamelCase  :Dict       =encoder_inputs_mask.sum(-1     )
        _UpperCamelCase  :Optional[Any]       =torch.roll(input_positions.unsqueeze(0     )					,      tuple(seq_lens.tolist()     )					,      dims=0     )
        x += self.position_encoding(lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =self.dropout_pre(lowerCAmelCase__     )

        # inverted the attention mask
        _UpperCamelCase  :Dict       =encoder_inputs.size()
        _UpperCamelCase  :Any       =self.get_extended_attention_mask(lowerCAmelCase__					,      lowerCAmelCase__     )

        for lyr in self.encoders:
             _UpperCamelCase  :Union[str, Any]       =lyr(lowerCAmelCase__					,      lowerCAmelCase__     )[0]
        _UpperCamelCase  :str       =self.layer_norm(lowerCAmelCase__     )

        return self.dropout_post(lowerCAmelCase__     ), encoder_inputs_mask



"""simple docstring"""







def _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)