



import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				(VQDiffusionScheduler,)



      def 		snake_case_      (  self  :			List[str]  ,			**A  :			Union[str, Any]		):
           _UpperCAmelCase    :			str              =					{
               "num_vec_classes": 4_0_9_7,
               "num_train_timesteps": 1_0_0,
           }

           config.update(**A		)
           return config



      def 		snake_case_      (  self  :			Any  ,			A  :			Optional[int]		):
           _UpperCAmelCase    :			Optional[int]              =					4
           _UpperCAmelCase    :			Tuple              =					8
           _UpperCAmelCase    :			Tuple              =					8

           _UpperCAmelCase    :			Tuple              =					torch.randint(0  ,			A  ,			(batch_size, height * width)		)

           return sample



      @property
      def 		snake_case_      (  self  :			List[Any]		):
           assert False



      def 		snake_case_      (  self  :			int  ,			A  :			int		):
           def model(A  :			List[str]  ,			A  :			List[str]  ,			*A  :			int		):
                _UpperCAmelCase    :			List[Any]              =					sample.shape
                _UpperCAmelCase    :			Tuple              =					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)		)
                _UpperCAmelCase    :			Union[str, Any]              =					F.log_softmax(logits.double()  ,			dim=1		).float()
                return return_value

           return model



      def 		snake_case_      (  self  :			Union[str, Any]		):
           for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=A		)



      def 		snake_case_      (  self  :			List[Any]		):
           for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
                self.check_over_configs(num_vec_classes=A		)



      def 		snake_case_      (  self  :			Optional[Any]		):
           for t in [0, 5_0, 9_9]:
                self.check_over_forward(time_step=A		)



      def 		snake_case_      (  self  :			str		):
           pass





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:





     '''simple docstring'''

     for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
          yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]


# Configuration
_lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
_lowerCAmelCase       :      Dict							=							parser.parse_args()

# Base tokenizer
_lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())

# Load dataset
_lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
_lowerCAmelCase       :      List[Any]							=							iter(dataset)


# Training and saving
_lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
