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


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}



class 						snake_case_ (	__A ):
   __A			:	Any        =       "mobilevit"






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  List[Any]=3			,		lowercase_       :  List[Any]=2_56			,		lowercase_       :  Union[str, Any]=2			,		lowercase_       :  int=[1_44, 1_92, 2_40]			,		lowercase_       :  List[str]=[16, 32, 64, 96, 1_28, 1_60, 6_40]			,		lowercase_       :  Tuple=4			,		lowercase_       :  Optional[int]=2.0			,		lowercase_       :  int=4.0			,		lowercase_       :  str="silu"			,		lowercase_       :  Optional[Any]=3			,		lowercase_       :  Dict=32			,		lowercase_       :  Union[str, Any]=0.1			,		lowercase_       :  str=0.0			,		lowercase_       :  str=0.1			,		lowercase_       :  List[str]=0.02			,		lowercase_       :  Union[str, Any]=1E-5			,		lowercase_       :  Tuple=True			,		lowercase_       :  Optional[Any]=2_56			,		lowercase_       :  List[str]=[6, 12, 18]			,		lowercase_       :  Any=0.1			,		lowercase_       :  List[str]=2_55			,		**lowercase_       :  Tuple			,		)      ->		Optional[Any]:
       super().__init__(**lowercase_						)

       lowercase__				:     List[str]					      =       num_channels
       lowercase__				:     Optional[int]					      =       image_size
       lowercase__				:     Any					      =       patch_size
       lowercase__				:     int					      =       hidden_sizes
       lowercase__				:     str					      =       neck_hidden_sizes
       lowercase__				:     Optional[int]					      =       num_attention_heads
       lowercase__				:     Tuple					      =       mlp_ratio
       lowercase__				:     Tuple					      =       expand_ratio
       lowercase__				:     List[str]					      =       hidden_act
       lowercase__				:     Optional[int]					      =       conv_kernel_size
       lowercase__				:     Union[str, Any]					      =       output_stride
       lowercase__				:     Optional[int]					      =       hidden_dropout_prob
       lowercase__				:     List[str]					      =       attention_probs_dropout_prob
       lowercase__				:     Union[str, Any]					      =       classifier_dropout_prob
       lowercase__				:     Union[str, Any]					      =       initializer_range
       lowercase__				:     List[str]					      =       layer_norm_eps
       lowercase__				:     Dict					      =       qkv_bias

       # decode head attributes for semantic segmentation
       lowercase__				:     List[Any]					      =       aspp_out_channels
       lowercase__				:     List[Any]					      =       atrous_rates
       lowercase__				:     str					      =       aspp_dropout_prob
       lowercase__				:     int					      =       semantic_loss_ignore_index



class 						snake_case_ (	__A ):
   __A			:	int        =       version.parse("1.11" )






   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Mapping[str, Mapping[int, str]]:
       return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]						)






   @property
   def    __UpperCamelCase  (   self       :  str						)      ->		Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([("logits", {0: "batch"})]						)
       else:
           return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)







   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		float:
       return 1E-4




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 ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

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






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     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:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       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:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       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,
       }



