

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


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

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

lowercase_   							=  {
    'gpt-neox-20b': 2_0_4_8,
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    VOCAB_FILES_NAMES
  _a		     =    PRETRAINED_VOCAB_FILES_MAP
  _a		     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a		     =    ["""input_ids""", """attention_mask"""]




  def __init__(							self				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase="<|endoftext|>"				,  lowerCAmelCase="<|endoftext|>"				,  lowerCAmelCase="<|endoftext|>"				,  lowerCAmelCase=False				,  **lowerCAmelCase				,  )    ->					int:
        '''simple docstring'''






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

        _lowercase      			=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
        if pre_tok_state.get('add_prefix_space'				,  lowerCAmelCase   ) != add_prefix_space:
              _lowercase      			=getattr(lowerCAmelCase				,  pre_tok_state.pop('type'   )   )
              _lowercase      			=add_prefix_space
              _lowercase      			=pre_tok_class(**lowerCAmelCase   )

        _lowercase      			=add_prefix_space




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None   )    ->					Tuple[str]:
        '''simple docstring'''






        _lowercase      			=self._tokenizer.model.save(lowerCAmelCase				,  name=lowerCAmelCase   )
        return tuple(lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					List[int]:
        '''simple docstring'''






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

        if len(lowerCAmelCase   ) > self.model_max_length:
              _lowercase      			=input_ids[-self.model_max_length :]
        return input_ids





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





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=hidden_states.shape
        _lowercase      			=jax.image.resize(
            lowerCAmelCase				,  shape=(batch, height * 2, width * 2, channels)				,  method='nearest'				,  )
        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(2, 2)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    None
  _a		     =    0.0
  _a		     =    None
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.in_channels if self.out_channels is None else self.out_channels

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=nn.Dense(lowerCAmelCase				,  dtype=self.dtype   )

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Dropout(self.dropout_prob   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _lowercase      			=None
        if use_nin_shortcut:
              _lowercase      			=nn.Conv(
                  lowerCAmelCase				,  kernel_size=(1, 1)				,  strides=(1, 1)				,  padding='VALID'				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=True   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=hidden_states
        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        _lowercase      			=self.time_emb_proj(nn.swish(lowerCAmelCase   )   )
        _lowercase      			=jnp.expand_dims(jnp.expand_dims(lowerCAmelCase				,  1   )				,  1   )
        _lowercase      			=hidden_states + temb

        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.dropout(lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        if self.conv_shortcut is not None:
              _lowercase      			=self.conv_shortcut(lowerCAmelCase   )

        return hidden_states + residual


