





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      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    '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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """levit"""


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



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase				:  Tuple						=		image_size
             _lowerCamelCase				:  Tuple						=		num_channels
             _lowerCamelCase				:  str						=		kernel_size
             _lowerCamelCase				:  List[Any]						=		stride
             _lowerCamelCase				:  List[Any]						=		padding
             _lowerCamelCase				:  Optional[Any]						=		hidden_sizes
             _lowerCamelCase				:  List[Any]						=		num_attention_heads
             _lowerCamelCase				:  List[Any]						=		depths
             _lowerCamelCase				:  Dict						=		key_dim
             _lowerCamelCase				:  Any						=		drop_path_rate
             _lowerCamelCase				:  str						=		patch_size
             _lowerCamelCase				:  List[str]						=		attention_ratio
             _lowerCamelCase				:  Optional[int]						=		mlp_ratio
             _lowerCamelCase				:  Union[str, Any]						=		initializer_range
             _lowerCamelCase				:  Any						=		[
                 ['''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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   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








import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

