
"""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 					a__ ( a :					Union[str, Any]=10		):

  """simple docstring"""







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


# Configuration
_a   :      int       =				HfArgumentParser(TokenizerTrainingArguments)
_a   :      Tuple       =				parser.parse_args()

# Base tokenizer
_a   :      Tuple       =				AutoTokenizer.from_pretrained(args.base_tokenizer)
_a   :      List[Any]       =				list(bytes_to_unicode().values())

# Load dataset
_a   :      Tuple       =				load_dataset(args.dataset_name, split="""train""", streaming=True)
_a   :      Optional[Any]       =				iter(dataset)


# Training and saving
_a   :      Optional[Any]       =				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)








"""simple docstring"""
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


_snake_case	:						List[str]					=					logging.get_logger(__name__)






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				['''input_values''', '''attention_mask''']


					def __init__(   self     :str	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :Tuple=1_60_00	,			__UpperCamelCase     :Optional[int]=0.0	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Optional[Any]=True	,			**__UpperCamelCase     :Optional[Any]	,			):
												super().__init__(feature_size=__UpperCamelCase	,			sampling_rate=__UpperCamelCase	,			padding_value=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    return_attention_mask
												A						=    do_normalize


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :float = 0.0						):
												if attention_mask is not None:
																			A						=    np.array(__UpperCamelCase	,			np.intaa						)
																			A						=    []

																			for vector, length in zip(__UpperCamelCase	,			attention_mask.sum(-1						)						):
																										A						=    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
																										if length < normed_slice.shape[0]:
																																	A						=    padding_value

																										normed_input_values.append(__UpperCamelCase						)
												else:
																			A						=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

												return normed_input_values


					def __call__(   self     :Tuple	,			__UpperCamelCase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			__UpperCamelCase     :Optional[int] = None	,			**__UpperCamelCase     :List[str]	,			):

												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."						)

												A						=    isinstance(__UpperCamelCase	,			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}"						)
												A						=    is_batched_numpy or (
												    isinstance(__UpperCamelCase	,			(list, tuple)						) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)						))
												)

												# always return batch
												if not is_batched:
																			A						=    [raw_speech]

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

												A						=    self.pad(
												    __UpperCamelCase	,			padding=__UpperCamelCase	,			max_length=__UpperCamelCase	,			truncation=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			)

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

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

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

												if return_tensors is not None:
																			A						=    padded_inputs.convert_to_tensors(__UpperCamelCase						)

												return padded_inputs


