'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
  import torch


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCamelCase__  :						List[str]					=      logging.get_logger(__name__)
UpperCamelCase__  :						Any					=      {'vocab_file': 'spiece.model'}

UpperCamelCase__  :						Dict					=      {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

UpperCamelCase__  :						List[Any]					=      {
    'AI-Sweden/gpt-sw3-126m': 2_048,
    'AI-Sweden/gpt-sw3-350m': 2_048,
    'AI-Sweden/gpt-sw3-1.6b': 2_048,
    'AI-Sweden/gpt-sw3-6.7b': 2_048,
    'AI-Sweden/gpt-sw3-20b': 2_048,
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   List[Any]   				= VOCAB_FILES_NAMES
      UpperCAmelCase_   :   str   				= PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_   :   Optional[int]   				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_   :   Tuple   				= ['''input_ids''', '''attention_mask''']




      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=False       ,lowerCamelCase_=False       ,lowerCamelCase_=False       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	{} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase__				:					Union[str, Any] 					=	kwargs.get('''name_or_path'''				)
         if name_or_path is None:
            logger.warning(
                '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
                ''' you are testing the model, this can safely be ignored'''				)
            UpperCAmelCase__				:					Dict 					=	'''None'''

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         UpperCAmelCase__				:					Any 					=	'''<|endoftext|>''' if eos_token is None else eos_token
         UpperCAmelCase__				:					Any 					=	'''<unk>''' if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
            UpperCAmelCase__				:					List[str] 					=	unk_token if pad_token is None else pad_token
            UpperCAmelCase__				:					int 					=	eos_token if bos_token is None else bos_token
         else:
            UpperCAmelCase__				:					int 					=	'''<pad>''' if pad_token is None else pad_token
            UpperCAmelCase__				:					Optional[Any] 					=	'''<s>''' if bos_token is None else bos_token

         super().__init__(
             do_lower_case=lowerCamelCase_       ,remove_space=lowerCamelCase_       ,keep_accents=lowerCamelCase_       ,bos_token=lowerCamelCase_       ,eos_token=lowerCamelCase_       ,unk_token=lowerCamelCase_       ,pad_token=lowerCamelCase_       ,sp_model_kwargs=self.sp_model_kwargs       ,**lowerCamelCase_       ,)

         UpperCAmelCase__				:					Union[str, Any] 					=	do_lower_case
         UpperCAmelCase__				:					Optional[Any] 					=	remove_space
         UpperCAmelCase__				:					List[str] 					=	keep_accents
         UpperCAmelCase__				:					Tuple 					=	vocab_file

         UpperCAmelCase__				:					Any 					=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(lowerCamelCase_				)

         # Used for whitespace normalization in input texts
         # fmt : off
         UpperCAmelCase__				:					List[str] 					=	{''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         UpperCAmelCase__				:					Optional[int] 					=	re.compile(
             f'''[{''.join(map(lowerCamelCase_       ,list(range(0       ,9				)				) + list(range(11       ,32				)				) + list(range(127       ,160				)				) + [160, 173, 8203]				)				)}]'''				)




      def __getstate__(					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.__dict__.copy()
         UpperCAmelCase__				:					List[str] 					=	None
         return state




      def __setstate__(					self       ,lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	d

         # for backward compatibility
         if not hasattr(self       ,'''sp_model_kwargs'''				):
            UpperCAmelCase__				:					str 					=	{}

         UpperCAmelCase__				:					Any 					=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(self.vocab_file				)




      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         return len(self.sp_model				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.non_printing_characters_re.sub(''''''       ,lowerCamelCase_				)

         # Normalize whitespaces
         UpperCAmelCase__				:					Optional[Any] 					=	''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]				)

         # NFC Unicode normalization
         UpperCAmelCase__				:					Any 					=	unicodedata.normalize('''NFC'''       ,lowerCamelCase_				)
         return text




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,**lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.preprocess_text(lowerCamelCase_				)
         return self.sp_model.encode(lowerCamelCase_       ,out_type=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         return self.sp_model.PieceToId(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         return self.sp_model.IdToPiece(lowerCamelCase_				)




      @staticmethod
      def 		lowerCAmelCase__       (					lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         return out_string




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	[]
         UpperCAmelCase__				:					Optional[int] 					=	''''''
         UpperCAmelCase__				:					Dict 					=	False
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
               if not prev_is_special:
                  out_string += " "

               out_string += self.sp_model.decode(lowerCamelCase_				) + token
               UpperCAmelCase__				:					List[Any] 					=	True
               UpperCAmelCase__				:					Optional[Any] 					=	[]
            else:
               current_sub_tokens.append(lowerCamelCase_				)
               UpperCAmelCase__				:					Optional[int] 					=	False
         out_string += self.sp_model.decode(lowerCamelCase_				)

         return out_string




      def 		lowerCAmelCase__       (					self				)      ->      Dict[str, int]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	{self.convert_ids_to_tokens(lowerCamelCase_				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_				):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
            return
         UpperCAmelCase__				:					Union[str, Any] 					=	os.path.join(
             lowerCamelCase_       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

         if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase_				) and os.path.isfile(self.vocab_file				):
            copyfile(self.vocab_file       ,lowerCamelCase_				)
         elif not os.path.isfile(self.vocab_file				):
            with open(lowerCamelCase_       ,'''wb'''				) as fi:
               UpperCAmelCase__				:					str 					=	self.sp_model.serialized_model_proto()
               fi.write(lowerCamelCase_				)

         return (out_vocab_file,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = False				)      ->      Union[List[int], List[List[int]], "torch.Tensor"]:



         '''simple docstring'''



         if isinstance(lowerCamelCase_       ,lowerCamelCase_				):
            UpperCAmelCase__				:					Dict 					=	self.preprocess_text(lowerCamelCase_				)
            UpperCAmelCase__				:					List[Any] 					=	self.sp_model.encode(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Optional[int] 					=	[self.preprocess_text(lowerCamelCase_				) for t in text]
            UpperCAmelCase__				:					List[Any] 					=	self.sp_model.encode(lowerCamelCase_				)

         if return_tensors is True or return_tensors == "pt":
            UpperCAmelCase__				:					Optional[Any] 					=	torch.tensor(lowerCamelCase_				)

         return token_ids




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         return self.sp_model.decode(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	[f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
         UpperCAmelCase__				:					Optional[Any] 					=	(
             f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(lowerCamelCase_				) + f'''{self.bos_token}Bot:'''
         )
         return self.encode(text=lowerCamelCase_				)





'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin


class 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase ):
      '''simple docstring'''


      @register_to_config
      def __init__(					self       ,lowerCamelCase_ = 128       ,lowerCamelCase_ = 256       ,lowerCamelCase_ = 2000.0       ,lowerCamelCase_ = 768       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 64       ,lowerCamelCase_ = 2048       ,lowerCamelCase_ = 0.1       ,)      ->      List[Any]:



         '''simple docstring'''



         super().__init__()

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Sequential(
             nn.Linear(lowerCamelCase_       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,nn.Linear(d_model * 4       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,)

         UpperCAmelCase__				:					List[str] 					=	nn.Embedding(lowerCamelCase_       ,lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	False

         UpperCAmelCase__				:					List[str] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(p=lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	nn.ModuleList()
         for lyr_num in range(lowerCamelCase_				):
            # FiLM conditional T5 decoder
            UpperCAmelCase__				:					str 					=	DecoderLayer(d_model=lowerCamelCase_       ,d_kv=lowerCamelCase_       ,num_heads=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)
            self.decoders.append(lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	TaLayerNorm(lowerCamelCase_				)

         UpperCAmelCase__				:					Any 					=	nn.Dropout(p=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	torch.mul(query_input.unsqueeze(-1				)       ,key_input.unsqueeze(-2				)				)
         return mask.unsqueeze(-3				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         UpperCAmelCase__				:					str 					=	get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time       ,embedding_dim=self.config.d_model       ,max_period=self.config.max_decoder_noise_time       ,).to(dtype=self.dtype				)

         UpperCAmelCase__				:					List[Any] 					=	self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

         assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

         UpperCAmelCase__				:					List[Any] 					=	decoder_input_tokens.shape[1]

         # If we want to use relative positions for audio context, we can just offset
         # this sequence by the length of encodings_and_masks.
         UpperCAmelCase__				:					int 					=	torch.broadcast_to(
             torch.arange(lowerCamelCase_       ,device=decoder_input_tokens.device				)       ,(batch, seq_length)       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	self.position_encoding(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         UpperCAmelCase__				:					str 					=	self.dropout(lowerCamelCase_				)

         # decoder: No padding present.
         UpperCAmelCase__				:					Optional[Any] 					=	torch.ones(
             decoder_input_tokens.shape[:2]       ,device=decoder_input_tokens.device       ,dtype=inputs.dtype				)

         # Translate encoding masks to encoder-decoder masks.
         UpperCAmelCase__				:					Dict 					=	[(x, self.encoder_decoder_mask(lowerCamelCase_       ,lowerCamelCase_				)) for x, y in encodings_and_masks]

         # cross attend style: concat encodings
         UpperCAmelCase__				:					str 					=	torch.cat([x[0] for x in encodings_and_encdec_masks]       ,dim=1				)
         UpperCAmelCase__				:					Dict 					=	torch.cat([x[1] for x in encodings_and_encdec_masks]       ,dim=-1				)

         for lyr in self.decoders:
            UpperCAmelCase__				:					Optional[Any] 					=	lyr(
                lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,encoder_attention_mask=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					List[Any] 					=	self.decoder_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	self.post_dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.spec_out(lowerCamelCase_				)
         return spec_out




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					int 					=	nn.ModuleList()

         # cond self attention: layer 0
         self.layer.append(
             TaLayerSelfAttentionCond(d_model=lowerCamelCase_       ,d_kv=lowerCamelCase_       ,num_heads=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)				)

         # cross attention: layer 1
         self.layer.append(
             TaLayerCrossAttention(
                 d_model=lowerCamelCase_       ,d_kv=lowerCamelCase_       ,num_heads=lowerCamelCase_       ,dropout_rate=lowerCamelCase_       ,layer_norm_epsilon=lowerCamelCase_       ,)				)

         # Film Cond MLP + dropout: last layer
         self.layer.append(
             TaLayerFFCond(d_model=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_       ,layer_norm_epsilon=lowerCamelCase_				)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[0](
             lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         if encoder_hidden_states is not None:
            UpperCAmelCase__				:					Any 					=	torch.where(encoder_attention_mask > 0       ,0       ,-1e10				).to(
                encoder_hidden_states.dtype				)

            UpperCAmelCase__				:					Union[str, Any] 					=	self.layer[1](
                lowerCamelCase_       ,key_value_states=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         # Apply Film Conditional Feed Forward layer
         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[-1](lowerCamelCase_       ,lowerCamelCase_				)

         return (hidden_states,)




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	TaLayerNorm(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
            UpperCAmelCase__				:					Any 					=	self.FiLMLayer(lowerCamelCase_       ,lowerCamelCase_				)

         # Self-attention block
         UpperCAmelCase__				:					List[Any] 					=	self.attention(lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Optional[int] 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.layer_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	self.attention(
             lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,attention_mask=attention_mask.squeeze(1				)       ,)
         UpperCAmelCase__				:					int 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	TaDenseGatedActDense(d_model=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
            UpperCAmelCase__				:					Optional[Any] 					=	self.film(lowerCamelCase_       ,lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	self.DenseReluDense(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Dropout(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	NewGELUActivation()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.act(self.wi_a(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[str] 					=	self.wi_a(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	hidden_gelu * hidden_linear
         UpperCAmelCase__				:					int 					=	self.dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.wo(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Tuple 					=	nn.Parameter(torch.ones(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Dict 					=	eps




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	hidden_states.to(torch.floataa				).pow(2				).mean(-1       ,keepdim=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

         # convert into half-precision if necessary
         if self.weight.dtype in [torch.floataa, torch.bfloataa]:
            UpperCAmelCase__				:					Union[str, Any] 					=	hidden_states.to(self.weight.dtype				)

         return self.weight * hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      torch.Tensor:



         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(lowerCamelCase_       ,3.0				))				))




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,out_features * 2       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scale_bias(lowerCamelCase_				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	torch.chunk(lowerCamelCase_       ,2       ,-1				)
         UpperCAmelCase__				:					List[Any] 					=	x * (1 + scale) + shift
         return x




