





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class       a							(   a__  ):
 snake_case__             =       42







class       a							(   a__   , a__  ):



 @register_to_config
 def __init__(     self				,       _snake_case = 3				,       _snake_case = 3				,       _snake_case = ("DownEncoderBlock2D",)				,       _snake_case = ("UpDecoderBlock2D",)				,       _snake_case = (64,)				,       _snake_case = 1				,       _snake_case = "silu"				,       _snake_case = 3				,       _snake_case = 32				,       _snake_case = 2_56				,       _snake_case = 32				,       _snake_case = None				,       _snake_case = 0.18_215				,       _snake_case = "group"				,       ):







  """simple docstring"""






  super().__init__()

  # pass init params to Encoder
  lowerCAmelCase    				= Encoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       down_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       double_z=_snake_case				,       )

  lowerCAmelCase    				= vq_embed_dim if vq_embed_dim is not None else latent_channels

  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )
  lowerCAmelCase    				= VectorQuantizer(_snake_case				,       _snake_case				,       beta=0.25				,       remap=_snake_case				,       sane_index_shape=_snake_case     )
  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )

  # pass init params to Decoder
  lowerCAmelCase    				= Decoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       up_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       norm_type=_snake_case				,       )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= self.encoder(_snake_case     )
  lowerCAmelCase    				= self.quant_conv(_snake_case     )

  if not return_dict:
   return (h,)

  return VQEncoderOutput(latents=_snake_case     )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = False				,       _snake_case = True     ):







  """simple docstring"""






  if not force_not_quantize:
   lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self.quantize(_snake_case     )
  else:
   lowerCAmelCase    				= h
  lowerCAmelCase    				= self.post_quant_conv(_snake_case     )
  lowerCAmelCase    				= self.decoder(_snake_case				,       quant if self.config.norm_type == 'spatial' else None     )

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= sample
  lowerCAmelCase    				= self.encode(_snake_case     ).latents
  lowerCAmelCase    				= self.decode(_snake_case     ).sample

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )












"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase     :						str						       =  logging.getLogger(__name__)

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path'  ,     type=_UpperCAmelCase  ,     default='data/dump.txt'  ,     help='The path to the data.'			)
 parser.add_argument('--tokenizer_type'  ,     type=_UpperCAmelCase  ,     default='bert'  ,     choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name'  ,     type=_UpperCAmelCase  ,     default='bert-base-uncased'  ,     help='The tokenizer to use.'			)
 parser.add_argument('--dump_file'  ,     type=_UpperCAmelCase  ,     default='data/dump'  ,     help='The dump file prefix.'			)
 lowerCAmelCase    				= parser.parse_args()

 logger.info(F'Loading Tokenizer ({args.tokenizer_name})'			)
 if args.tokenizer_type == "bert":
  lowerCAmelCase    				= BertTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  lowerCAmelCase    				= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `<s>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  lowerCAmelCase    				= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(F'Loading text from {args.file_path}'			)
 with open(args.file_path  ,     'r'  ,     encoding='utf8'			) as fp:
  lowerCAmelCase    				= fp.readlines()

 logger.info('Start encoding'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples to process.'			)

 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 1_0000
 lowerCAmelCase    				= time.time()
 for text in data:
  lowerCAmelCase    				= F'{bos} {text.strip()} {sep}'
  lowerCAmelCase    				= tokenizer.encode(_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase			)
  rslt.append(_UpperCAmelCase			)

  iter += 1
  if iter % interval == 0:
   lowerCAmelCase    				= time.time()
   logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
   lowerCAmelCase    				= time.time()
 logger.info('Finished binarization'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples processed.'			)

 lowerCAmelCase    				= F'{args.dump_file}.{args.tokenizer_name}.pickle'
 lowerCAmelCase    				= tokenizer.vocab_size
 if vocab_size < (1 << 16):
  lowerCAmelCase    				= [np.uintaa(_UpperCAmelCase			) for d in rslt]
 else:
  lowerCAmelCase    				= [np.intaa(_UpperCAmelCase			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(F'Dump to {dp_file}'			)
 with open(_UpperCAmelCase  ,     'wb'			) as handle:
  pickle.dump(rslt_  ,     _UpperCAmelCase  ,     protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
       main()





