



'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class   snake_case_ (__lowercase , __lowercase , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				VQModel
       _lowerCamelCase			    =				"""sample"""



       @property
       def  A_   (     self     ,lowercase=(32, 32)):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		4
             UpperCAmelCase_  :	int				      =		3

             UpperCAmelCase_  :	str				      =		floats_tensor((batch_size, num_channels) + sizes).to(lowercase)

             return {"sample": image}



       @property
       def  A_   (     self):

             """simple docstring"""





             return (3, 32, 32)



       @property
       def  A_   (     self):

             """simple docstring"""





             return (3, 32, 32)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		{
                 "block_out_channels": [32, 64],
                 "in_channels": 3,
                 "out_channels": 3,
                 "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                 "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                 "latent_channels": 3,
             }
             UpperCAmelCase_  :	int				      =		self.dummy_input
             return init_dict, inputs_dict



       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		VQModel.from_pretrained("fusing/vqgan-dummy"     ,output_loading_info=lowercase)
             self.assertIsNotNone(lowercase)
             self.assertEqual(len(loading_info["missing_keys"])     ,0)

             model.to(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		model(**self.dummy_input)

             assert image is not None, "Make sure output is not None"



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		VQModel.from_pretrained("fusing/vqgan-dummy")
             model.to(lowercase).eval()

             torch.manual_seed(0)
             if torch.cuda.is_available():
                   torch.cuda.manual_seed_all(0)

             UpperCAmelCase_  :	Tuple				      =		torch.randn(1     ,model.config.in_channels     ,model.config.sample_size     ,model.config.sample_size)
             UpperCAmelCase_  :	str				      =		image.to(lowercase)
             with torch.no_grad():
                   UpperCAmelCase_  :	List[Any]				      =		model(lowercase).sample

             UpperCAmelCase_  :	Optional[int]				      =		output[0, -1, -3:, -3:].flatten().cpu()
             # fmt: off
             UpperCAmelCase_  :	Optional[int]				      =		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
             # fmt: on
             self.assertTrue(torch.allclose(lowercase     ,lowercase     ,atol=1E-3))





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
)
__lowerCamelCase         =			logging.getLogger(__name__)







def    _snake_case (       )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		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_  :	str				      =		parser.parse_args()

      logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""    )
      if args.tokenizer_type == "bert":
            UpperCAmelCase_  :	int				      =		BertTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase_  :	Dict				      =		tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase_  :	str				      =		RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	int				      =		tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase_  :	Tuple				      =		GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	List[Any]				      =		tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase_  :	List[str]				      =		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_  :	Union[str, Any]				      =		fp.readlines()

      logger.info("Start encoding"    )
      logger.info(F"""{len(__snake_case    )} examples to process."""    )

      UpperCAmelCase_  :	List[str]				      =		[]
      UpperCAmelCase_  :	str				      =		0
      UpperCAmelCase_  :	Union[str, Any]				      =		1_0_0_0_0
      UpperCAmelCase_  :	int				      =		time.time()
      for text in data:
            UpperCAmelCase_  :	str				      =		F"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase_  :	Any				      =		tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case    )
            rslt.append(__snake_case    )

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase_  :	List[str]				      =		time.time()
                  logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""    )
                  UpperCAmelCase_  :	Optional[Any]				      =		time.time()
      logger.info("Finished binarization"    )
      logger.info(F"""{len(__snake_case    )} examples processed."""    )

      UpperCAmelCase_  :	List[Any]				      =		F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase_  :	Optional[int]				      =		tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            UpperCAmelCase_  :	List[Any]				      =		[np.uintaa(__snake_case    ) for d in rslt]
      else:
            UpperCAmelCase_  :	str				      =		[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()
