code: 
  
  
  
  """simple docstring"""
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _lowerCAmelCase					:List[Any]  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Union[str, Any]  =      {
      'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             ='''swiftformer'''
  
  
  
  
  
       def __init__(      self ,			A=3 ,			A=[3, 3, 6, 4] ,			A=[4_8, 5_6, 1_1_2, 2_2_0] ,			A=4 ,			A=[True, True, True, True] ,			A="gelu" ,			A=3 ,			A=2 ,			A=1 ,			A=0.0 ,			A=True ,			A=1E-5 ,			A=1E-5 ,			**A ,			)							->      Optional[Any]:
           super().__init__(**A	)
           _UpperCAmelCase							:						List[str]       							=					num_channels
           _UpperCAmelCase							:						Optional[Any]       							=					depths
           _UpperCAmelCase							:						Optional[Any]       							=					embed_dims
           _UpperCAmelCase							:						Tuple       							=					mlp_ratio
           _UpperCAmelCase							:						List[str]       							=					downsamples
           _UpperCAmelCase							:						List[str]       							=					hidden_act
           _UpperCAmelCase							:						Optional[int]       							=					down_patch_size
           _UpperCAmelCase							:						List[str]       							=					down_stride
           _UpperCAmelCase							:						str       							=					down_pad
           _UpperCAmelCase							:						List[Any]       							=					drop_path_rate
           _UpperCAmelCase							:						Any       							=					use_layer_scale
           _UpperCAmelCase							:						Optional[Any]       							=					layer_scale_init_value
           _UpperCAmelCase							:						Dict       							=					batch_norm_eps
  
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =version.parse('''1.11'''   )
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ]	)
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      float:
           return 1E-4
  
  
  
style_context: 
  
  
  
  """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 							_UpperCAmelCase							(				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:
           super().__init__()
           if use_fast_bert_tokenizer:
               _UpperCAmelCase							:						Union[str, Any]       							=					FastBertTokenizer(
                   A ,			token_out_type=tf.intaa ,			lower_case_nfd_strip_accents=A ,			**A	)
           else:
               _UpperCAmelCase							:						List[Any]       							=					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 ,			)
               _UpperCAmelCase							:						str       							=					BertTokenizerLayer(
                   A ,			token_out_type=tf.intaa ,			lower_case=A ,			**A	)
  
           _UpperCAmelCase							:						int       							=					vocab_list
           _UpperCAmelCase							:						Dict       							=					do_lower_case
           _UpperCAmelCase							:						Optional[int]       							=					cls_token_id or vocab_list.index('''[CLS]'''	)
           _UpperCAmelCase							:						Any       							=					sep_token_id or vocab_list.index('''[SEP]'''	)
           _UpperCAmelCase							:						List[Any]       							=					pad_token_id or vocab_list.index('''[PAD]'''	)
           _UpperCAmelCase							:						List[str]       							=					ShrinkLongestTrimmer(max_length - 3 ,			axis=1	)  # Allow room for special tokens
           _UpperCAmelCase							:						Tuple       							=					max_length
           _UpperCAmelCase							:						List[Any]       							=					padding
           _UpperCAmelCase							:						List[Any]       							=					truncation
           _UpperCAmelCase							:						Union[str, Any]       							=					pad_to_multiple_of
           _UpperCAmelCase							:						Union[str, Any]       							=					return_token_type_ids
           _UpperCAmelCase							:						Union[str, Any]       							=					return_attention_mask
  
  
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls ,			A ,			**A	)							->      Dict:  # noqa: F821
           _UpperCAmelCase							:						Optional[int]       							=					kwargs.pop('''do_lower_case''' ,			A	)
           _UpperCAmelCase							:						int       							=					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
           _UpperCAmelCase							:						Optional[Any]       							=					kwargs.pop('''cls_token_id''' ,			A	)
           _UpperCAmelCase							:						str       							=					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
           _UpperCAmelCase							:						str       							=					kwargs.pop('''sep_token_id''' ,			A	)
           _UpperCAmelCase							:						int       							=					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
           _UpperCAmelCase							:						List[Any]       							=					kwargs.pop('''pad_token_id''' ,			A	)
           _UpperCAmelCase							:						Optional[Any]       							=					tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
           _UpperCAmelCase							:						int       							=					tokenizer.get_vocab()
           _UpperCAmelCase							:						List[str]       							=					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()] ,			key=lambda A	: x[1]	)
           _UpperCAmelCase							:						Dict       							=					[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 				__lowerCAmelCase			(      cls ,			A ,			*A ,			**A	)							->      int:
           try:
               _UpperCAmelCase							:						int       							=					BertTokenizer.from_pretrained(A ,			*A ,			**A	)
           except:  # noqa: E722
               from .tokenization_bert_fast import BertTokenizerFast
  
               _UpperCAmelCase							:						Dict       							=					BertTokenizerFast.from_pretrained(A ,			*A ,			**A	)
           return cls.from_tokenizer(A ,			**A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           if self.do_lower_case:
               _UpperCAmelCase							:						int       							=					case_fold_utfa(A	)
           _UpperCAmelCase							:						str       							=					self.tf_tokenizer.tokenize(A	)
           return tokens.merge_dims(1 ,			-1	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      Optional[Any]:
           if padding is None:
               _UpperCAmelCase							:						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:
               _UpperCAmelCase							:						Union[str, Any]       							=					self.max_length
           if truncation is None:
               _UpperCAmelCase							:						List[Any]       							=					self.truncation
           if pad_to_multiple_of is None:
               _UpperCAmelCase							:						List[Any]       							=					self.pad_to_multiple_of
           if return_token_type_ids is None:
               _UpperCAmelCase							:						List[str]       							=					self.return_token_type_ids
           if return_attention_mask is None:
               _UpperCAmelCase							:						Any       							=					self.return_attention_mask
           if not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						Dict       							=					tf.convert_to_tensor(A	)
           if text_pair is not None and not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						str       							=					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:
               _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					text[:, 0], text[:, 1]
           _UpperCAmelCase							:						Optional[Any]       							=					self.unpaired_tokenize(A	)
           if text_pair is None:  # Unpaired text
               if truncation:
                   _UpperCAmelCase							:						Optional[int]       							=					text[:, : max_length - 2]  # Allow room for special tokens
               _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					combine_segments(
                   (text,) ,			start_of_sequence_id=self.cls_token_id ,			end_of_segment_id=self.sep_token_id	)
           else:  # Paired text
               _UpperCAmelCase							:						Tuple       							=					self.unpaired_tokenize(A	)
               if truncation:
                   _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[int]       							=					self.paired_trimmer.trim([text, text_pair]	)
               _UpperCAmelCase							,       _UpperCAmelCase							:						Union[str, Any]       							=					combine_segments(
                   (text, text_pair) ,			start_of_sequence_id=self.cls_token_id ,			end_of_segment_id=self.sep_token_id	)
           if padding == "longest":
               _UpperCAmelCase							:						List[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
                   _UpperCAmelCase							:						Tuple       							=					pad_to_multiple_of * (-tf.math.floordiv(-pad_length ,			A	))
           else:
               _UpperCAmelCase							:						int       							=					max_length
  
           _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					pad_model_inputs(A ,			max_seq_length=A ,			pad_value=self.pad_token_id	)
           _UpperCAmelCase							:						Dict       							=					{'''input_ids''': input_ids}
           if return_attention_mask:
               _UpperCAmelCase							:						Optional[int]       							=					attention_mask
           if return_token_type_ids:
               _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					pad_model_inputs(
                   A ,			max_seq_length=A ,			pad_value=self.pad_token_id	)
               _UpperCAmelCase							:						int       							=					token_type_ids
           return output
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[str]:
           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,
           }
  
  
  