


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCAmelCase      			=  logging.get_logger(__name__)



class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					['''input_values''', '''attention_mask''']




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE=1   ,			__SCREAMING_SNAKE_CASE=1_60_00   ,			__SCREAMING_SNAKE_CASE=0.0   ,			__SCREAMING_SNAKE_CASE=False   ,			__SCREAMING_SNAKE_CASE=True   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						int:







         """simple docstring"""







         super().__init__(feature_size=__SCREAMING_SNAKE_CASE   ,			sampling_rate=__SCREAMING_SNAKE_CASE   ,			padding_value=__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			List[str] 						=return_attention_mask
         UpperCamelCase__ :			Any 						=do_normalize




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = 0.0)					->						List[np.ndarray]:







         """simple docstring"""







         if attention_mask is not None:
            UpperCamelCase__ :			List[str] 						=np.array(__SCREAMING_SNAKE_CASE   ,			np.intaa)
            UpperCamelCase__ :			Any 						=[]

            for vector, length in zip(__SCREAMING_SNAKE_CASE   ,			attention_mask.sum(-1)):
               UpperCamelCase__ :			Dict 						=(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
               if length < normed_slice.shape[0]:
                  UpperCamelCase__ :			Union[str, Any] 						=padding_value

               normed_input_values.append(__SCREAMING_SNAKE_CASE)
         else:
            UpperCamelCase__ :			str 						=[(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

         return normed_input_values




      def __call__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						BatchFeature:







         """simple docstring"""







         if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
               raise ValueError(
                   F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                   F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                   F''' {self.sampling_rate} and not {sampling_rate}.''')
         else:
            logger.warning(
                "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                "Failing to do so can result in silent errors that might be hard to debug.")

         UpperCamelCase__ :			Tuple 						=isinstance(__SCREAMING_SNAKE_CASE   ,			np.ndarray) and len(raw_speech.shape) > 1
         if is_batched_numpy and len(raw_speech.shape) > 2:
            raise ValueError(F'''Only mono-channel audio is supported for input to {self}''')
         UpperCamelCase__ :			str 						=is_batched_numpy or (
             isinstance(__SCREAMING_SNAKE_CASE   ,			(list, tuple)) and (isinstance(raw_speech[0]   ,			(np.ndarray, tuple, list)))
         )

         # always return batch
         if not is_batched:
            UpperCamelCase__ :			Union[str, Any] 						=[raw_speech]

         # convert into correct format for padding
         UpperCamelCase__ :			str 						=BatchFeature({"input_values": raw_speech})

         UpperCamelCase__ :			Dict 						=self.pad(
             __SCREAMING_SNAKE_CASE   ,			padding=__SCREAMING_SNAKE_CASE   ,			max_length=__SCREAMING_SNAKE_CASE   ,			truncation=__SCREAMING_SNAKE_CASE   ,			pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,			return_attention_mask=__SCREAMING_SNAKE_CASE   ,			)

         # convert input values to correct format
         UpperCamelCase__ :			Any 						=padded_inputs["input_values"]
         if not isinstance(input_values[0]   ,			np.ndarray):
            UpperCamelCase__ :			Tuple 						=[np.asarray(__SCREAMING_SNAKE_CASE   ,			dtype=np.floataa) for array in input_values]
         elif (
             not isinstance(__SCREAMING_SNAKE_CASE   ,			np.ndarray)
             and isinstance(input_values[0]   ,			np.ndarray)
             and input_values[0].dtype is np.dtype(np.floataa)
         ):
            UpperCamelCase__ :			int 						=[array.astype(np.floataa) for array in input_values]
         elif isinstance(__SCREAMING_SNAKE_CASE   ,			np.ndarray) and input_values.dtype is np.dtype(np.floataa):
            UpperCamelCase__ :			Dict 						=input_values.astype(np.floataa)

         # convert attention_mask to correct format
         UpperCamelCase__ :			Dict 						=padded_inputs.get("attention_mask")
         if attention_mask is not None:
            UpperCamelCase__ :			Dict 						=[np.asarray(__SCREAMING_SNAKE_CASE   ,			dtype=np.intaa) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
            UpperCamelCase__ :			Optional[int] 						=(
                attention_mask
                if self._get_padding_strategies(__SCREAMING_SNAKE_CASE   ,			max_length=__SCREAMING_SNAKE_CASE) is not PaddingStrategy.DO_NOT_PAD
                else None
            )
            UpperCamelCase__ :			Optional[int] 						=self.zero_mean_unit_var_norm(
                padded_inputs["input_values"]   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			padding_value=self.padding_value)

         if return_tensors is not None:
            UpperCamelCase__ :			List[Any] 						=padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE)

         return padded_inputs





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 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






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


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  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)

