

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						:     List[str]					,      __A						:     Dict[str, int]					,      __A						:     List[str]					,      __A						:     int = None					,      __A						:     int = None							):
		super().__init__()
		__A :			Optional[Any]		    =			pad_token_id
		__A :			Any		    =			max_length
		__A :			Any		    =			vocab
		__A :			int		    =			merges
		__A :			int		    =			BytePairTokenizer(__A					,      __A					,      sequence_length=__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Dict					,      __A						:     GPTaTokenizer					,      *__A						:     int					,      **__A						:     str							):
		__A :			Optional[int]		    =			[""" """.join(__A							) for m in tokenizer.bpe_ranks.keys()]
		__A :			Tuple		    =			tokenizer.get_vocab()
		return cls(__A					,      __A					,      *__A					,      **__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Tuple					,      __A						:     Union[str, os.PathLike]					,      *__A						:     Any					,      **__A						:     Optional[Any]							):
		__A :			Any		    =			GPTaTokenizer.from_pretrained(__A					,      *__A					,      **__A							)
		return cls.from_tokenizer(__A					,      *__A					,      **__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Optional[int]					,      __A						:     Union[str, Any]							):
		return cls(**__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return {
		    "vocab": self.vocab,
		    "merges": self.merges,
		    "max_length": self.max_length,
		    "pad_token_id": self.pad_token_id,
		}




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Optional[Any]					,      __A						:     int = None							):
		__A :			Tuple		    =			self.tf_tokenizer(__A							)
		__A :			Tuple		    =			tf.ones_like(__A							)

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

			if max_length is not None:
				__A    ,				__A :			Optional[int]		    =			pad_model_inputs(
				    __A					,      max_seq_length=__A					,      pad_value=self.pad_token_id							)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
