



"""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
)
lowercase  							=					logging.getLogger(__name__)




def 	UpperCAmelCase     (		):

  '''simple docstring'''


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

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

  logger.info('Start encoding'       )
  logger.info(f'{len(__snake_case       )} examples to process.'       )

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

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

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


if __name__ == "__main__":
     main()







"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




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







							def __init__(  self	,       snake_case = 16	,       snake_case = 88	,       snake_case = None	,       snake_case = 1	,       snake_case = 0.0	,       snake_case = 32	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = "geglu"	,       snake_case = None	,       )			->							str:
									super().__init__()
									_UpperCAmelCase							 =		nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=snake_case	,       attention_head_dim=snake_case	,       in_channels=snake_case	,       num_layers=snake_case	,       dropout=snake_case	,       norm_num_groups=snake_case	,       cross_attention_dim=snake_case	,       attention_bias=snake_case	,       sample_size=snake_case	,       num_vector_embeds=snake_case	,       activation_fn=snake_case	,       num_embeds_ada_norm=snake_case	,       )
									        for _ in range(2						)
									    ]						)

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									_UpperCAmelCase							 =		0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									_UpperCAmelCase							 =		[77, 257]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									_UpperCAmelCase							 =		[1, 0]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case = True	,       )			->							Any:
									_UpperCAmelCase							 =		hidden_states

									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		0
									# attention_mask is not used yet
									for i in range(2						):
											# for each of the two transformers, pass the corresponding condition tokens
											_UpperCAmelCase							 =		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											_UpperCAmelCase							 =		self.transformer_index_for_condition[i]
											_UpperCAmelCase							 =		self.transformers[transformer_index](
											    snake_case	,       encoder_hidden_states=snake_case	,       timestep=snake_case	,       cross_attention_kwargs=snake_case	,       return_dict=snake_case	,       )[0]
											encoded_states.append(encoded_state - input_states						)
											tokens_start += self.condition_lengths[i]

									_UpperCAmelCase							 =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									_UpperCAmelCase							 =		output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=snake_case						)


