




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


a_   		=     logging.get_logger(__name__)

a_   		=     {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a_   		=     {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

a_   		=     {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


a_   		=     {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Union[str, Any]    =					VOCAB_FILES_NAMES
      lowerCAmelCase__			: List[Any]    =					PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__			: Union[str, Any]    =					PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__			: Union[str, Any]    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__			: Any    =					SqueezeBertTokenizer







      def __init__(     self					: Optional[int] ,       snake_case					: int=None ,       snake_case					: Optional[Any]=None ,       snake_case					: Union[str, Any]=True ,       snake_case					: Tuple="[UNK]" ,       snake_case					: Dict="[SEP]" ,       snake_case					: List[str]="[PAD]" ,       snake_case					: List[str]="[CLS]" ,       snake_case					: Union[str, Any]="[MASK]" ,       snake_case					: List[Any]=True ,       snake_case					: str=None ,       **snake_case					: List[str] ,       ):
          super().__init__(
              snake_case ,       tokenizer_file=snake_case ,       do_lower_case=snake_case ,       unk_token=snake_case ,       sep_token=snake_case ,       pad_token=snake_case ,       cls_token=snake_case ,       mask_token=snake_case ,       tokenize_chinese_chars=snake_case ,       strip_accents=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
          if (
              normalizer_state.get('''lowercase''' ,       snake_case				) != do_lower_case
              or normalizer_state.get('''strip_accents''' ,       snake_case				) != strip_accents
              or normalizer_state.get('''handle_chinese_chars''' ,       snake_case				) != tokenize_chinese_chars
          ):
              __UpperCamelCase  		=						getattr(snake_case ,       normalizer_state.pop('''type'''				)				)
              __UpperCamelCase  		=						do_lower_case
              __UpperCamelCase  		=						strip_accents
              __UpperCamelCase  		=						tokenize_chinese_chars
              __UpperCamelCase  		=						normalizer_class(**snake_case				)

          __UpperCamelCase  		=						do_lower_case







      def       snake_case	(     self					: str ,       snake_case					: int ,       snake_case					: Tuple=None				):
          __UpperCamelCase  		=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: List[int] ,       snake_case					: Optional[List[int]] = None				):
          __UpperCamelCase  		=						[self.sep_token_id]
          __UpperCamelCase  		=						[self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]







      def       snake_case	(     self					: Any ,       snake_case					: str ,       snake_case					: Optional[str] = None				):
          __UpperCamelCase  		=						self._tokenizer.model.save(snake_case ,       name=snake_case				)
          return tuple(snake_case				)












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''				)
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''				)

          super().__init__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






