





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer



class    __lowerCAmelCase			(     tf.keras.layers.Layer	):






			def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = None       )	->  Optional[Any]:


							'''simple docstring'''



							super().__init__()
							snake_case_		:       Any           =    pad_token_id
							snake_case_		:       Any           =    max_length
							snake_case_		:       Optional[Any]           =    vocab
							snake_case_		:       Dict           =    merges
							snake_case_		:       Dict           =    BytePairTokenizer(__magic_name__   ,				__magic_name__   ,				sequence_length=__magic_name__       )






			@classmethod
			def      lowerCamelCase    (cls   ,				__magic_name__   ,				*__magic_name__   ,				**__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    [''' '''.join(__magic_name__       ) for m in tokenizer.bpe_ranks.keys()]
							snake_case_		:       List[Any]           =    tokenizer.get_vocab()
							return cls(__magic_name__   ,				__magic_name__   ,				*__magic_name__   ,				**__magic_name__       )






			@classmethod
			def      lowerCamelCase    (cls   ,				__magic_name__   ,				*__magic_name__   ,				**__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    GPTaTokenizer.from_pretrained(__magic_name__   ,				*__magic_name__   ,				**__magic_name__       )
							return cls.from_tokenizer(__magic_name__   ,				*__magic_name__   ,				**__magic_name__       )






			@classmethod
			def      lowerCamelCase    (cls   ,				__magic_name__       )	->  str:


							'''simple docstring'''



							return cls(**__magic_name__       )






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    self.tf_tokenizer(__magic_name__       )
							snake_case_		:       Optional[Any]           =    tf.ones_like(__magic_name__       )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											snake_case_		:       Optional[Any]           =    max_length if max_length is not None else self.max_length

											if max_length is not None:
															snake_case_     ,				snake_case_		:       Any           =    pad_model_inputs(
															    __magic_name__   ,				max_seq_length=__magic_name__   ,				pad_value=self.pad_token_id       )

							return {"attention_mask": attention_mask, "input_ids": input_ids}




from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		NestedDataStructureLike[PathLike] ,   _UpperCAmelCase       :		Optional[NamedSplit] = None ,   _UpperCAmelCase       :		Optional[Features] = None ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		Optional[int] = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  List[str]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase ,   split=_UpperCAmelCase ,   features=_UpperCAmelCase ,   cache_dir=_UpperCAmelCase ,   keep_in_memory=_UpperCAmelCase ,   streaming=_UpperCAmelCase ,   num_proc=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						path_or_paths if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else {self.split: path_or_paths}
							lowercase__	     =						Text(
							    cache_dir=_UpperCAmelCase ,   data_files=_UpperCAmelCase ,   features=_UpperCAmelCase ,   **_UpperCAmelCase ,   )


					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							if self.streaming:
									lowercase__	     =						self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase ,   download_mode=_UpperCAmelCase ,   verification_mode=_UpperCAmelCase ,   base_path=_UpperCAmelCase ,   num_proc=self.num_proc ,   )
									lowercase__	     =						self.builder.as_dataset(
									    split=self.split ,   verification_mode=_UpperCAmelCase ,   in_memory=self.keep_in_memory						)
							return dataset

