


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      __A     (tf.keras.layers.Layer):





							'''simple docstring'''

							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      Dict[str, int]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      int = None		,       UpperCAmelCase_							:      int = None       )					->Dict:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		pad_token_id
														snake_case_           =		max_length
														snake_case_           =		vocab
														snake_case_           =		merges
														snake_case_           =		BytePairTokenizer(UpperCAmelCase_		,       UpperCAmelCase_		,       sequence_length=UpperCAmelCase_       )



							@classmethod
							def 		lowerCAmelCase       (				cls							:      Tuple		,       UpperCAmelCase_							:      GPTaTokenizer		,       *UpperCAmelCase_							:      List[Any]		,       **UpperCAmelCase_							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		[""" """.join(UpperCAmelCase_       ) for m in tokenizer.bpe_ranks.keys()]
														snake_case_           =		tokenizer.get_vocab()
														return cls(UpperCAmelCase_		,       UpperCAmelCase_		,       *UpperCAmelCase_		,       **UpperCAmelCase_       )



							@classmethod
							def 		lowerCAmelCase       (				cls							:      Union[str, Any]		,       UpperCAmelCase_							:      Union[str, os.PathLike]		,       *UpperCAmelCase_							:      Optional[Any]		,       **UpperCAmelCase_							:      Union[str, Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		GPTaTokenizer.from_pretrained(UpperCAmelCase_		,       *UpperCAmelCase_		,       **UpperCAmelCase_       )
														return cls.from_tokenizer(UpperCAmelCase_		,       *UpperCAmelCase_		,       **UpperCAmelCase_       )



							@classmethod
							def 		lowerCAmelCase       (				cls							:      List[str]		,       UpperCAmelCase_							:      Union[str, Any]       )					->List[str]:



														"""simple docstring"""



														return cls(**UpperCAmelCase_       )



							def 		lowerCAmelCase       (				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 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      int = None       )					->Any:



														"""simple docstring"""



														snake_case_           =		self.tf_tokenizer(UpperCAmelCase_       )
														snake_case_           =		tf.ones_like(UpperCAmelCase_       )

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

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

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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



