
"""simple docstring"""


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class    A_     (					tf.keras.layers.Layer       ):



							"""simple docstring"""






							def __init__(  self  :Tuple					,							lowerCamelCase_  :List					,							lowerCamelCase_  :bool					,							lowerCamelCase_  :int = None					,							lowerCamelCase_  :int = None					,							lowerCamelCase_  :int = None					,							lowerCamelCase_  :str = "longest"					,							lowerCamelCase_  :bool = True					,							lowerCamelCase_  :int = 512					,							lowerCamelCase_  :int = None					,							lowerCamelCase_  :bool = True					,							lowerCamelCase_  :bool = True					,							lowerCamelCase_  :bool = True					,							**lowerCamelCase_  :List[str]					,							):





										"""simple docstring"""

										super().__init__()
										if use_fast_bert_tokenizer:
													lowerCamelCase__     :					Any		=FastBertTokenizer(
													    lowerCamelCase_					,							token_out_type=tf.intaa					,							lower_case_nfd_strip_accents=lowerCamelCase_					,							**lowerCamelCase_	)
										else:
													lowerCamelCase__     :					Optional[int]		=tf.lookup.StaticVocabularyTable(
													    tf.lookup.KeyValueTensorInitializer(
													        keys=lowerCamelCase_					,							key_dtype=tf.string					,							values=tf.range(tf.size(lowerCamelCase_					,							out_type=tf.intaa	)					,							dtype=tf.intaa	)					,							value_dtype=tf.intaa					,							)					,							num_oov_buckets=1					,							)
													lowerCamelCase__     :					str		=BertTokenizerLayer(
													    lowerCamelCase_					,							token_out_type=tf.intaa					,							lower_case=lowerCamelCase_					,							**lowerCamelCase_	)

										lowerCamelCase__     :					Optional[Any]		=vocab_list
										lowerCamelCase__     :					int		=do_lower_case
										lowerCamelCase__     :					Union[str, Any]		=cls_token_id or vocab_list.index('[CLS]'	)
										lowerCamelCase__     :					List[Any]		=sep_token_id or vocab_list.index('[SEP]'	)
										lowerCamelCase__     :					Tuple		=pad_token_id or vocab_list.index('[PAD]'	)
										lowerCamelCase__     :					Optional[int]		=ShrinkLongestTrimmer(max_length - 3					,							axis=1	)  # Allow room for special tokens
										lowerCamelCase__     :					Optional[int]		=max_length
										lowerCamelCase__     :					Dict		=padding
										lowerCamelCase__     :					Union[str, Any]		=truncation
										lowerCamelCase__     :					Optional[int]		=pad_to_multiple_of
										lowerCamelCase__     :					Optional[Any]		=return_token_type_ids
										lowerCamelCase__     :					List[Any]		=return_attention_mask





							@classmethod
							def 				UpperCAmelCase__							(  cls  :int					,							lowerCamelCase_  :"PreTrainedTokenizerBase"					,							**lowerCamelCase_  :Dict	):  # noqa: F821





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=kwargs.pop('do_lower_case'					,							lowerCamelCase_	)
										lowerCamelCase__     :					str		=tokenizer.do_lower_case if do_lower_case is None else do_lower_case
										lowerCamelCase__     :					List[Any]		=kwargs.pop('cls_token_id'					,							lowerCamelCase_	)
										lowerCamelCase__     :					Union[str, Any]		=tokenizer.cls_token_id if cls_token_id is None else cls_token_id
										lowerCamelCase__     :					Optional[Any]		=kwargs.pop('sep_token_id'					,							lowerCamelCase_	)
										lowerCamelCase__     :					Optional[int]		=tokenizer.sep_token_id if sep_token_id is None else sep_token_id
										lowerCamelCase__     :					Optional[Any]		=kwargs.pop('pad_token_id'					,							lowerCamelCase_	)
										lowerCamelCase__     :					Any		=tokenizer.pad_token_id if pad_token_id is None else pad_token_id

										lowerCamelCase__     :					str		=tokenizer.get_vocab()
										lowerCamelCase__     :					Tuple		=sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]					,							key=lambda lowerCamelCase_	: x[1]	)
										lowerCamelCase__     :					Dict		=[entry[0] for entry in vocab]
										return cls(
										    vocab_list=lowerCamelCase_					,							do_lower_case=lowerCamelCase_					,							cls_token_id=lowerCamelCase_					,							sep_token_id=lowerCamelCase_					,							pad_token_id=lowerCamelCase_					,							**lowerCamelCase_					,							)





							@classmethod
							def 				UpperCAmelCase__							(  cls  :List[Any]					,							lowerCamelCase_  :Union[str, os.PathLike]					,							*lowerCamelCase_  :Optional[Any]					,							**lowerCamelCase_  :Union[str, Any]	):





										"""simple docstring"""

										try:
													lowerCamelCase__     :					Optional[int]		=BertTokenizer.from_pretrained(lowerCamelCase_					,							*lowerCamelCase_					,							**lowerCamelCase_	)
										except:  # noqa: E722
													from .tokenization_bert_fast import BertTokenizerFast

													lowerCamelCase__     :					Tuple		=BertTokenizerFast.from_pretrained(lowerCamelCase_					,							*lowerCamelCase_					,							**lowerCamelCase_	)
										return cls.from_tokenizer(lowerCamelCase_					,							**lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :str					,							lowerCamelCase_  :Any	):





										"""simple docstring"""

										if self.do_lower_case:
													lowerCamelCase__     :					List[str]		=case_fold_utfa(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=self.tf_tokenizer.tokenize(lowerCamelCase_	)
										return tokens.merge_dims(1					,							-1	)





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Optional[Any]=None					,							lowerCamelCase_  :List[str]=None					,							lowerCamelCase_  :Any=None					,							lowerCamelCase_  :str=None					,							lowerCamelCase_  :int=None					,							lowerCamelCase_  :str=None					,							lowerCamelCase_  :str=None					,							):





										"""simple docstring"""

										if padding is None:
													lowerCamelCase__     :					List[str]		=self.padding
										if padding not in ("longest", "max_length"):
													raise ValueError('Padding must be either \'longest\' or \'max_length\'!'	)
										if max_length is not None and text_pair is not None:
													# Because we have to instantiate a Trimmer to do it properly
													raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'	)
										if max_length is None:
													lowerCamelCase__     :					Union[str, Any]		=self.max_length
										if truncation is None:
													lowerCamelCase__     :					Optional[int]		=self.truncation
										if pad_to_multiple_of is None:
													lowerCamelCase__     :					Dict		=self.pad_to_multiple_of
										if return_token_type_ids is None:
													lowerCamelCase__     :					List[Any]		=self.return_token_type_ids
										if return_attention_mask is None:
													lowerCamelCase__     :					Union[str, Any]		=self.return_attention_mask
										if not isinstance(lowerCamelCase_					,							tf.Tensor	):
													lowerCamelCase__     :					Optional[Any]		=tf.convert_to_tensor(lowerCamelCase_	)
										if text_pair is not None and not isinstance(lowerCamelCase_					,							tf.Tensor	):
													lowerCamelCase__     :					int		=tf.convert_to_tensor(lowerCamelCase_	)
										if text_pair is not None:
													if text.shape.rank > 1:
																raise ValueError('text argument should not be multidimensional when a text pair is supplied!'	)
													if text_pair.shape.rank > 1:
																raise ValueError('text_pair should not be multidimensional!'	)
										if text.shape.rank == 2:
													lowerCamelCase__	,    lowerCamelCase__     :					int		=text[:, 0], text[:, 1]
										lowerCamelCase__     :					Union[str, Any]		=self.unpaired_tokenize(lowerCamelCase_	)
										if text_pair is None:  # Unpaired text
													if truncation:
																lowerCamelCase__     :					List[str]		=text[:, : max_length - 2]  # Allow room for special tokens
													lowerCamelCase__	,    lowerCamelCase__     :					Tuple		=combine_segments(
													    (text,)					,							start_of_sequence_id=self.cls_token_id					,							end_of_segment_id=self.sep_token_id	)
										else:  # Paired text
													lowerCamelCase__     :					Optional[Any]		=self.unpaired_tokenize(lowerCamelCase_	)
													if truncation:
																lowerCamelCase__	,    lowerCamelCase__     :					str		=self.paired_trimmer.trim([text, text_pair]	)
													lowerCamelCase__	,    lowerCamelCase__     :					int		=combine_segments(
													    (text, text_pair)					,							start_of_sequence_id=self.cls_token_id					,							end_of_segment_id=self.sep_token_id	)
										if padding == "longest":
													lowerCamelCase__     :					Union[str, Any]		=input_ids.bounding_shape(axis=1	)
													if pad_to_multiple_of is not None:
																# No ceiling division in tensorflow, so we negate floordiv instead
																lowerCamelCase__     :					List[str]		=pad_to_multiple_of * (-tf.math.floordiv(-pad_length					,							lowerCamelCase_	))
										else:
													lowerCamelCase__     :					Tuple		=max_length

										lowerCamelCase__	,    lowerCamelCase__     :					List[Any]		=pad_model_inputs(lowerCamelCase_					,							max_seq_length=lowerCamelCase_					,							pad_value=self.pad_token_id	)
										lowerCamelCase__     :					int		={'input_ids': input_ids}
										if return_attention_mask:
													lowerCamelCase__     :					Optional[int]		=attention_mask
										if return_token_type_ids:
													lowerCamelCase__	,    lowerCamelCase__     :					Any		=pad_model_inputs(
													    lowerCamelCase_					,							max_seq_length=lowerCamelCase_					,							pad_value=self.pad_token_id	)
													lowerCamelCase__     :					List[Any]		=token_type_ids
										return output





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										return {
										    "vocab_list": self.vocab_list,
										    "do_lower_case": self.do_lower_case,
										    "cls_token_id": self.cls_token_id,
										    "sep_token_id": self.sep_token_id,
										    "pad_token_id": self.pad_token_id,
										}

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)