






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 	_UpperCAmelCase  (  tf.keras.layers.Layer		):




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          super().__init__()
          lowerCAmelCase__   :Union[str, Any]      							=				pad_token_id
          lowerCAmelCase__   :Tuple      							=				max_length
          lowerCAmelCase__   :Tuple      							=				vocab
          lowerCAmelCase__   :List[str]      							=				merges
          lowerCAmelCase__   :Tuple      							=				BytePairTokenizer(_lowerCAmelCase    ,	_lowerCAmelCase    ,	sequence_length=_lowerCAmelCase		)
      @classmethod
      def 			snake_case_      (   cls    ,	_lowerCAmelCase    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				[" ".join(_lowerCAmelCase		) for m in tokenizer.bpe_ranks.keys()]
          lowerCAmelCase__   :Dict      							=				tokenizer.get_vocab()
          return cls(_lowerCAmelCase    ,	_lowerCAmelCase    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		)
      @classmethod
      def 			snake_case_      (   cls    ,	_lowerCAmelCase    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				GPTaTokenizer.from_pretrained(_lowerCAmelCase    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		)
          return cls.from_tokenizer(_lowerCAmelCase    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		)
      @classmethod
      def 			snake_case_      (   cls    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return cls(**_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return {
              "vocab": self.vocab,
              "merges": self.merges,
              "max_length": self.max_length,
              "pad_token_id": self.pad_token_id,
          }



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.tf_tokenizer(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				tf.ones_like(_lowerCAmelCase		)

          if self.pad_token_id is not None:
              # pad the tokens up to max length
              lowerCAmelCase__   :Union[str, Any]      							=				max_length if max_length is not None else self.max_length

              if max_length is not None:
                  lowerCAmelCase__   :List[Any]      							=				pad_model_inputs(
                      _lowerCAmelCase    ,	max_seq_length=_lowerCAmelCase    ,	pad_value=self.pad_token_id		)

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











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


_a    :   Tuple        =			logging.get_logger(__name__)

_a    :   Optional[int]        =			{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''levit'''
      def __init__(   self    ,	_lowerCAmelCase=224    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=16    ,	_lowerCAmelCase=[128, 256, 384]    ,	_lowerCAmelCase=[4, 8, 12]    ,	_lowerCAmelCase=[4, 4, 4]    ,	_lowerCAmelCase=[16, 16, 16]    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=0.02    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				image_size
          lowerCAmelCase__   :Dict      							=				num_channels
          lowerCAmelCase__   :List[Any]      							=				kernel_size
          lowerCAmelCase__   :List[Any]      							=				stride
          lowerCAmelCase__   :Tuple      							=				padding
          lowerCAmelCase__   :str      							=				hidden_sizes
          lowerCAmelCase__   :Union[str, Any]      							=				num_attention_heads
          lowerCAmelCase__   :Optional[int]      							=				depths
          lowerCAmelCase__   :Union[str, Any]      							=				key_dim
          lowerCAmelCase__   :str      							=				drop_path_rate
          lowerCAmelCase__   :int      							=				patch_size
          lowerCAmelCase__   :Dict      							=				attention_ratio
          lowerCAmelCase__   :Dict      							=				mlp_ratio
          lowerCAmelCase__   :Optional[int]      							=				initializer_range
          lowerCAmelCase__   :Tuple      							=				[
              ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    version.parse('''1.11'''		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)



      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 1e-4



