





'''simple docstring'''

import flax.linen as nn
import jax
import jax.numpy as jnp





class lowerCamelCase						(nn.Module					):
  _lowercase  :	int
  _lowercase  :	jnp.dtype       					=				jnp.floataa







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Dict					     =     nn.Conv(
            self.out_channels     ,     kernel_size=(3, 3)     ,     strides=(1, 1)     ,     padding=((1, 1), (1, 1))     ,     dtype=self.dtype     ,     )







  def __call__(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     hidden_states.shape
        _snake_case		:	List[Any]					     =     jax.image.resize(
            lowerCamelCase__     ,     shape=(batch, height * 2, width * 2, channels)     ,     method='''nearest'''     ,     )
        _snake_case		:	Any					     =     self.conv(lowerCamelCase__		)
        return hidden_states





class lowerCamelCase						(nn.Module					):
  _lowercase  :	int
  _lowercase  :	jnp.dtype       					=				jnp.floataa







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[str]					     =     nn.Conv(
            self.out_channels     ,     kernel_size=(3, 3)     ,     strides=(2, 2)     ,     padding=((1, 1), (1, 1))     ,     dtype=self.dtype     ,     )







  def __call__(		self     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.conv(lowerCamelCase__		)
        return hidden_states





class lowerCamelCase						(nn.Module					):
  _lowercase  :	int
  _lowercase  :	int       					=				None
  _lowercase  :	float       					=				0.0
  _lowercase  :	bool       					=				None
  _lowercase  :	jnp.dtype       					=				jnp.floataa







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	str					     =     self.in_channels if self.out_channels is None else self.out_channels

        _snake_case		:	Any					     =     nn.GroupNorm(num_groups=32     ,     epsilon=1E-5		)
        _snake_case		:	Tuple					     =     nn.Conv(
            lowerCamelCase__     ,     kernel_size=(3, 3)     ,     strides=(1, 1)     ,     padding=((1, 1), (1, 1))     ,     dtype=self.dtype     ,     )

        _snake_case		:	Union[str, Any]					     =     nn.Dense(lowerCamelCase__     ,     dtype=self.dtype		)

        _snake_case		:	Optional[Any]					     =     nn.GroupNorm(num_groups=32     ,     epsilon=1E-5		)
        _snake_case		:	Tuple					     =     nn.Dropout(self.dropout_prob		)
        _snake_case		:	List[str]					     =     nn.Conv(
            lowerCamelCase__     ,     kernel_size=(3, 3)     ,     strides=(1, 1)     ,     padding=((1, 1), (1, 1))     ,     dtype=self.dtype     ,     )

        _snake_case		:	Optional[int]					     =     self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _snake_case		:	Optional[Any]					     =     None
        if use_nin_shortcut:
              _snake_case		:	List[str]					     =     nn.Conv(
                  lowerCamelCase__     ,     kernel_size=(1, 1)     ,     strides=(1, 1)     ,     padding='''VALID'''     ,     dtype=self.dtype     ,     )







  def __call__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__=True		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     hidden_states
        _snake_case		:	int					     =     self.norma(lowerCamelCase__		)
        _snake_case		:	List[str]					     =     nn.swish(lowerCamelCase__		)
        _snake_case		:	Tuple					     =     self.conva(lowerCamelCase__		)

        _snake_case		:	List[str]					     =     self.time_emb_proj(nn.swish(lowerCamelCase__		)		)
        _snake_case		:	str					     =     jnp.expand_dims(jnp.expand_dims(lowerCamelCase__     ,     1		)     ,     1		)
        _snake_case		:	Tuple					     =     hidden_states + temb

        _snake_case		:	Dict					     =     self.norma(lowerCamelCase__		)
        _snake_case		:	int					     =     nn.swish(lowerCamelCase__		)
        _snake_case		:	str					     =     self.dropout(lowerCamelCase__     ,     lowerCamelCase__		)
        _snake_case		:	int					     =     self.conva(lowerCamelCase__		)

        if self.conv_shortcut is not None:
              _snake_case		:	str					     =     self.conv_shortcut(lowerCamelCase__		)

        return hidden_states + residual









'''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


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

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

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





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

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





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

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


