


'''simple docstring'''



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 							__UpperCamelCase    (		tf.keras.layers.Layer ):




	def __init__(       self						,    __a						,    __a						,    __a = None						,    __a = None							):

						'''simple docstring'''





						super().__init__()
						__a      :	Any    =  pad_token_id
						__a      :	Any    =  max_length
						__a      :	Tuple    =  vocab
						__a      :	int    =  merges
						__a      :	Union[str, Any]    =  BytePairTokenizer(_a						,    _a						,    sequence_length=_a							)




	@classmethod
	def 				__UpperCAmelCase							(       cls						,    __a						,    *__a						,    **__a							):

						'''simple docstring'''





						__a      :	List[str]    =  [' '.join(_a							) for m in tokenizer.bpe_ranks.keys()]
						__a      :	Optional[int]    =  tokenizer.get_vocab()
						return cls(_a						,    _a						,    *_a						,    **_a							)




	@classmethod
	def 				__UpperCAmelCase							(       cls						,    __a						,    *__a						,    **__a							):

						'''simple docstring'''





						__a      :	Any    =  GPTaTokenizer.from_pretrained(_a						,    *_a						,    **_a							)
						return cls.from_tokenizer(_a						,    *_a						,    **_a							)




	@classmethod
	def 				__UpperCAmelCase							(       cls						,    __a							):

						'''simple docstring'''





						return cls(**_a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	List[str]    =  self.tf_tokenizer(_a							)
						__a      :	Union[str, Any]    =  tf.ones_like(_a							)

						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      :	Any    =  pad_model_inputs(
																    _a						,    max_seq_length=_a						,    pad_value=self.pad_token_id							)

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







'''simple docstring'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[Any]	=     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



