



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):



 """simple docstring"""

 def __init__(    self					:					List[str]			,						__lowerCAmelCase					:					Dict[str, int]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					int = None			,						__lowerCAmelCase					:					int = None      )   ->	str:
    super().__init__()
    _A      =       pad_token_id
    _A      =       max_length
    _A      =       vocab
    _A      =       merges
    _A      =       BytePairTokenizer(__lowerCAmelCase			,						__lowerCAmelCase			,						sequence_length=__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					str			,						__lowerCAmelCase					:					GPTaTokenizer			,						*__lowerCAmelCase					:					Union[str, Any]			,						**__lowerCAmelCase					:					Dict      )   ->	str:
    _A      =       [''' '''.join(__lowerCAmelCase      ) for m in tokenizer.bpe_ranks.keys()]
    _A      =       tokenizer.get_vocab()
    return cls(__lowerCAmelCase			,						__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					Tuple			,						__lowerCAmelCase					:					Union[str, os.PathLike]			,						*__lowerCAmelCase					:					str			,						**__lowerCAmelCase					:					Any      )   ->	Any:
    _A      =       GPTaTokenizer.from_pretrained(__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )
    return cls.from_tokenizer(__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					Union[str, Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Dict:
    return cls(**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    return {
        "vocab": self.vocab,
        "merges": self.merges,
        "max_length": self.max_length,
        "pad_token_id": self.pad_token_id,
    }

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					int = None      )   ->	Any:
    _A      =       self.tf_tokenizer(__lowerCAmelCase      )
    _A      =       tf.ones_like(__lowerCAmelCase      )

    if self.pad_token_id is not None:
       # pad the tokens up to max length
       _A      =       max_length if max_length is not None else self.max_length

       if max_length is not None:
          _A  ,    _A      =       pad_model_inputs(
              __lowerCAmelCase			,						max_seq_length=__lowerCAmelCase			,						pad_value=self.pad_token_id      )

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





import os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





