import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class      A							:


   @property
   def 			lowercase_      (self     :						Tuple		)						->				str:
         """simple docstring"""



         return self.get_dummy_input()


   @property
   def 			lowercase_      (self     :						str		)						->				Tuple:
         """simple docstring"""



         if self.block_type == "down":
               return (4, 3_2, 1_6, 1_6)
         elif self.block_type == "mid":
               return (4, 3_2, 3_2, 3_2)
         elif self.block_type == "up":
               return (4, 3_2, 6_4, 6_4)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Optional[int]=True       ,  __UpperCAmelCase     :						List[Any]=False       ,  __UpperCAmelCase     :						str=False       ,  __UpperCAmelCase     :						Optional[Any]=False       ,  )						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  3_2
         UpperCAmelCase__								=  (3_2, 3_2)

         UpperCAmelCase__								=  torch.manual_seed(0		)
         UpperCAmelCase__								=  torch.device(__UpperCAmelCase		)
         UpperCAmelCase__								=  (batch_size, num_channels) + sizes
         UpperCAmelCase__								=  randn_tensor(__UpperCAmelCase       ,  generator=__UpperCAmelCase       ,  device=__UpperCAmelCase		)
         UpperCAmelCase__								=  {"hidden_states": hidden_states}

         if include_temb:
               UpperCAmelCase__								=  1_2_8
               UpperCAmelCase__								=  randn_tensor((batch_size, temb_channels)       ,  generator=__UpperCAmelCase       ,  device=__UpperCAmelCase		)

         if include_res_hidden_states_tuple:
               UpperCAmelCase__								=  torch.manual_seed(1		)
               UpperCAmelCase__								=  (randn_tensor(__UpperCAmelCase       ,  generator=__UpperCAmelCase       ,  device=__UpperCAmelCase		),)

         if include_encoder_hidden_states:
               UpperCAmelCase__								=  floats_tensor((batch_size, 3_2, 3_2)		).to(__UpperCAmelCase		)

         if include_skip_sample:
               UpperCAmelCase__								=  randn_tensor(((batch_size, 3) + sizes)       ,  generator=__UpperCAmelCase       ,  device=__UpperCAmelCase		)

         return dummy_input


   def 			lowercase_      (self     :						Optional[int]		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "in_channels": 3_2,
             "out_channels": 3_2,
             "temb_channels": 1_2_8,
         }
         if self.block_type == "up":
               UpperCAmelCase__								=  3_2

         if self.block_type == "mid":
               init_dict.pop("out_channels"		)

         UpperCAmelCase__								=  self.dummy_input
         return init_dict, inputs_dict


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						Any		)						->				str:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase__								=  self.block_class(**__UpperCAmelCase		)
         unet_block.to(__UpperCAmelCase		)
         unet_block.eval()

         with torch.no_grad():
               UpperCAmelCase__								=  unet_block(**__UpperCAmelCase		)

         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  output[0]

         self.assertEqual(output.shape       ,  self.output_shape		)

         UpperCAmelCase__								=  output[0, -1, -3:, -3:]
         UpperCAmelCase__								=  torch.tensor(__UpperCAmelCase		).to(__UpperCAmelCase		)
         assert torch_all_close(output_slice.flatten()       ,  __UpperCAmelCase       ,  atol=5E-3		)





   @unittest.skipIf(torch_device == "mps"       ,  "Training is not supported in mps"		)
   def 			lowercase_      (self     :						Optional[Any]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase__								=  self.block_class(**__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.train()
         UpperCAmelCase__								=  model(**__UpperCAmelCase		)

         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  output[0]

         UpperCAmelCase__								=  torch.device(__UpperCAmelCase		)
         UpperCAmelCase__								=  randn_tensor(output.shape       ,  device=__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.nn.functional.mse_loss(__UpperCAmelCase       ,  __UpperCAmelCase		)
         loss.backward()




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__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''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=__A,  default="data/dump.txt",  help="The path to the data."							)
      parser.add_argument("--tokenizer_type",  type=__A,  default="bert",  choices=["bert", "roberta", "gpt2"]							)
      parser.add_argument("--tokenizer_name",  type=__A,  default="bert-base-uncased",  help="The tokenizer to use."							)
      parser.add_argument("--dump_file",  type=__A,  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(__A							)} examples to process."""							)

      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  10_000
      UpperCAmelCase__								=  time.time()
      for text in data:
            UpperCAmelCase__								=  f"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase__								=  tokenizer.encode(__A,  add_special_tokens=__A							)
            rslt.append(__A							)

            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(__A							)} examples processed."""							)

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


if __name__ == "__main__":
      main()



