


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						:   Optional[Any]   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   List[str] = None   ,     __A						:   Any = None						)      ->List[Any]:






									"""simple docstring"""






									super().__init__()
									a__ :int						  =    pad_token_id
									a__ :str						  =    max_length
									a__ :Dict						  =    vocab
									a__ :List[Any]						  =    merges
									a__ :Optional[int]						  =    BytePairTokenizer(UpperCamelCase__   ,     UpperCamelCase__   ,     sequence_length=UpperCamelCase__						)




						@classmethod
						def  _snake_case					(	cls						:   List[Any]   ,     __A						:   Optional[Any]   ,     *__A						:   Optional[Any]   ,     **__A						:   Optional[int]						)      ->List[str]:






									"""simple docstring"""






									a__ :Optional[int]						  =    [" ".join(UpperCamelCase__						) for m in tokenizer.bpe_ranks.keys()]
									a__ :List[str]						  =    tokenizer.get_vocab()
									return cls(UpperCamelCase__   ,     UpperCamelCase__   ,     *UpperCamelCase__   ,     **UpperCamelCase__						)




						@classmethod
						def  _snake_case					(	cls						:   Dict   ,     __A						:   Tuple   ,     *__A						:   List[str]   ,     **__A						:   Optional[int]						)      ->str:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    GPTaTokenizer.from_pretrained(UpperCamelCase__   ,     *UpperCamelCase__   ,     **UpperCamelCase__						)
									return cls.from_tokenizer(UpperCamelCase__   ,     *UpperCamelCase__   ,     **UpperCamelCase__						)




						@classmethod
						def  _snake_case					(	cls						:   Tuple   ,     __A						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									return cls(**UpperCamelCase__						)




						def  _snake_case					(	self						:   Dict						)      ->int:






									"""simple docstring"""






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






						def  _snake_case					(	self						:   int   ,     __A						:   Union[str, Any]   ,     __A						:   Union[str, Any] = None						)      ->Any:






									"""simple docstring"""






									a__ :Dict						  =    self.tf_tokenizer(UpperCamelCase__						)
									a__ :Union[str, Any]						  =    tf.ones_like(UpperCamelCase__						)

									if self.pad_token_id is not None:
												# pad the tokens up to max length
												a__ :Union[str, Any]						  =    max_length if max_length is not None else self.max_length

												if max_length is not None:
															a__  ,  a__ :Tuple						  =    pad_model_inputs(
															    UpperCamelCase__   ,     max_seq_length=UpperCamelCase__   ,     pad_value=self.pad_token_id						)

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__			 =						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'albert'




						def __init__(	self						:   Any   ,     __A						:   Optional[int]=30000   ,     __A						:   List[str]=128   ,     __A						:   int=4096   ,     __A						:   Any=12   ,     __A						:   Union[str, Any]=1   ,     __A						:   Optional[int]=64   ,     __A						:   Dict=16384   ,     __A						:   List[str]=1   ,     __A						:   Any="gelu_new"   ,     __A						:   List[Any]=0   ,     __A						:   str=0   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Tuple=0.02   ,     __A						:   int=1E-12   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]="absolute"   ,     __A						:   Tuple=0   ,     __A						:   str=2   ,     __A						:   Union[str, Any]=3   ,     **__A						:   Union[str, Any]   ,     )      ->str:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    embedding_size
									a__ :Union[str, Any]						  =    hidden_size
									a__ :int						  =    num_hidden_layers
									a__ :List[Any]						  =    num_hidden_groups
									a__ :str						  =    num_attention_heads
									a__ :Optional[Any]						  =    inner_group_num
									a__ :Any						  =    hidden_act
									a__ :Optional[int]						  =    intermediate_size
									a__ :Optional[Any]						  =    hidden_dropout_prob
									a__ :Optional[int]						  =    attention_probs_dropout_prob
									a__ :int						  =    max_position_embeddings
									a__ :List[Any]						  =    type_vocab_size
									a__ :Dict						  =    initializer_range
									a__ :Optional[int]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    classifier_dropout_prob
									a__ :Optional[Any]						  =    position_embedding_type






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :List[Any]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :Dict						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]						)


