code: 
  
  
  
  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 		_snake_case      (			tf.keras.layers.Layer				):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  	def __init__(						self	,    a	,    a	,    a = None	,    a = None	,    a = None	,    a = "longest"	,    a = True	,    a = 5_1_2	,    a = None	,    a = True	,    a = True	,    a = True	,    **a	,    )	->							Tuple:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			super().__init__()
  			if use_fast_bert_tokenizer:
  					_A           =    FastBertTokenizer(
  					    a	,    token_out_type=tf.intaa	,    lower_case_nfd_strip_accents=a	,    **a					)
  			else:
  					_A           =    tf.lookup.StaticVocabularyTable(
  					    tf.lookup.KeyValueTensorInitializer(
  					        keys=a	,    key_dtype=tf.string	,    values=tf.range(tf.size(a	,    out_type=tf.intaa					)	,    dtype=tf.intaa					)	,    value_dtype=tf.intaa	,    )	,    num_oov_buckets=1	,    )
  					_A           =    BertTokenizerLayer(
  					    a	,    token_out_type=tf.intaa	,    lower_case=a	,    **a					)
  
  			_A           =    vocab_list
  			_A           =    do_lower_case
  			_A           =    cls_token_id or vocab_list.index('''[CLS]'''					)
  			_A           =    sep_token_id or vocab_list.index('''[SEP]'''					)
  			_A           =    pad_token_id or vocab_list.index('''[PAD]'''					)
  			_A           =    ShrinkLongestTrimmer(max_length - 3	,    axis=1					)  # Allow room for special tokens
  			_A           =    max_length
  			_A           =    padding
  			_A           =    truncation
  			_A           =    pad_to_multiple_of
  			_A           =    return_token_type_ids
  			_A           =    return_attention_mask
  	@classmethod
  	def lowercase_		(						cls	,    a	,    **a					)	->							Dict:  # noqa: F821
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			_A           =    kwargs.pop('''do_lower_case'''	,    a					)
  			_A           =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  			_A           =    kwargs.pop('''cls_token_id'''	,    a					)
  			_A           =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  			_A           =    kwargs.pop('''sep_token_id'''	,    a					)
  			_A           =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  			_A           =    kwargs.pop('''pad_token_id'''	,    a					)
  			_A           =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  			_A           =    tokenizer.get_vocab()
  			_A           =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]	,    key=lambda a					: x[1]					)
  			_A           =    [entry[0] for entry in vocab]
  			return cls(
  			    vocab_list=a	,    do_lower_case=a	,    cls_token_id=a	,    sep_token_id=a	,    pad_token_id=a	,    **a	,    )
  	@classmethod
  	def lowercase_		(						cls	,    a	,    *a	,    **a					)	->							int:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			try:
  					_A           =    BertTokenizer.from_pretrained(a	,    *a	,    **a					)
  			except:  # noqa: E722
  					from .tokenization_bert_fast import BertTokenizerFast
  
  					_A           =    BertTokenizerFast.from_pretrained(a	,    *a	,    **a					)
  			return cls.from_tokenizer(a	,    **a					)
  	def lowercase_		(						self	,    a					)	->							Any:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			if self.do_lower_case:
  					_A           =    case_fold_utfa(a					)
  			_A           =    self.tf_tokenizer.tokenize(a					)
  			return tokens.merge_dims(1	,    -1					)
  	def lowercase_		(						self	,    a	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    )	->							Optional[Any]:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			if padding is None:
  					_A           =    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:
  					_A           =    self.max_length
  			if truncation is None:
  					_A           =    self.truncation
  			if pad_to_multiple_of is None:
  					_A           =    self.pad_to_multiple_of
  			if return_token_type_ids is None:
  					_A           =    self.return_token_type_ids
  			if return_attention_mask is None:
  					_A           =    self.return_attention_mask
  			if not isinstance(a	,    tf.Tensor					):
  					_A           =    tf.convert_to_tensor(a					)
  			if text_pair is not None and not isinstance(a	,    tf.Tensor					):
  					_A           =    tf.convert_to_tensor(a					)
  			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:
  					_A           =    text[:, 0], text[:, 1]
  			_A           =    self.unpaired_tokenize(a					)
  			if text_pair is None:  # Unpaired text
  					if truncation:
  							_A           =    text[:, : max_length - 2]  # Allow room for special tokens
  					_A           =    combine_segments(
  					    (text,)	,    start_of_sequence_id=self.cls_token_id	,    end_of_segment_id=self.sep_token_id					)
  			else:  # Paired text
  					_A           =    self.unpaired_tokenize(a					)
  					if truncation:
  							_A           =    self.paired_trimmer.trim([text, text_pair]					)
  					_A           =    combine_segments(
  					    (text, text_pair)	,    start_of_sequence_id=self.cls_token_id	,    end_of_segment_id=self.sep_token_id					)
  			if padding == "longest":
  					_A           =    input_ids.bounding_shape(axis=1					)
  					if pad_to_multiple_of is not None:
  							# No ceiling division in tensorflow, so we negate floordiv instead
  							_A           =    pad_to_multiple_of * (-tf.math.floordiv(-pad_length	,    a					))
  			else:
  					_A           =    max_length
  
  			_A           =    pad_model_inputs(a	,    max_seq_length=a	,    pad_value=self.pad_token_id					)
  			_A           =    {'''input_ids''': input_ids}
  			if return_attention_mask:
  					_A           =    attention_mask
  			if return_token_type_ids:
  					_A           =    pad_model_inputs(
  					    a	,    max_seq_length=a	,    pad_value=self.pad_token_id					)
  					_A           =    token_type_ids
  			return output
  
  
  
  	def lowercase_		(						self					)	->							List[str]:
  
  
  
  
  			"""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,
  			}
style_context: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output