



import copy
import random

from transformers import CLIPTokenizer



class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						*a__ ,						**a__):
         super().__init__(*a__ ,						**a__)
         A__		 =					{}




     def 		snake_case_		(				self ,						a__ ,						*a__ ,						**a__):
         A__		 =					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__):
         A__		 =					[]
         if num_vec_per_token == 1:
             self.try_adding_tokens(a__ ,						*a__ ,						**a__)
             output.append(a__)
         else:
             A__		 =					[]
             for i in range(a__):
                 A__		 =					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")
         A__		 =					output




     def 		snake_case_		(				self ,						a__ ,						a__=False ,						a__=1.0):
         if isinstance(a__ ,						a__):
             A__		 =					[]
             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:
                 A__		 =					self.token_map[placeholder_token]
                 A__		 =					tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                 if vector_shuffle:
                     A__		 =					copy.copy(a__)
                     random.shuffle(a__)
                 A__		 =					text.replace(a__ ,						''' '''.join(a__))
         return text




     def __call__(				self ,						a__ ,						*a__ ,						a__=False ,						a__=1.0 ,						**a__):
         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__):
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 a__ ,						vector_shuffle=a__ ,						prop_tokens_to_load=a__) ,						*a__ ,						**a__ ,						)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMobileViTForImageClassification",
       "TFMobileViTForSemanticSegmentation",
       "TFMobileViTModel",
       "TFMobileViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
