


'''simple docstring'''



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


_lowercase    :    str			     =  logging.get_logger(__name__)

_lowercase    :    List[str]			     =  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase    :    str			     =  {
    """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"""
        ),
    },
}

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


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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Optional[Any]							= VOCAB_FILES_NAMES
				__magic_name__       :   Optional[Any]							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Optional[int]							= PRETRAINED_INIT_CONFIGURATION
				__magic_name__       :   Dict							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Optional[Any]							= SqueezeBertTokenizer




				def __init__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				List[str]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[int]=True   ,					lowerCAmelCase    :				str="[UNK]"   ,					lowerCAmelCase    :				Optional[int]="[SEP]"   ,					lowerCAmelCase    :				int="[PAD]"   ,					lowerCAmelCase    :				Union[str, Any]="[CLS]"   ,					lowerCAmelCase    :				Union[str, Any]="[MASK]"   ,					lowerCAmelCase    :				Tuple=True   ,					lowerCAmelCase    :				Tuple=None   ,					**lowerCAmelCase    :				Union[str, Any]   ,					)->  List[Any]:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase   ,					tokenizer_file=lowerCAmelCase   ,					do_lower_case=lowerCAmelCase   ,					unk_token=lowerCAmelCase   ,					sep_token=lowerCAmelCase   ,					pad_token=lowerCAmelCase   ,					cls_token=lowerCAmelCase   ,					mask_token=lowerCAmelCase   ,					tokenize_chinese_chars=lowerCAmelCase   ,					strip_accents=lowerCAmelCase   ,					**lowerCAmelCase   ,					)

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

										UpperCAmelCase         =			do_lower_case




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Union[str, Any]=None   )->  List[Any]:

										"""simple docstring"""




										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   a__(	self    :				Tuple   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[int]:

										"""simple docstring"""




										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   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str] = None   )->  Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self._tokenizer.model.save(lowerCAmelCase   ,					name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )









'''simple docstring'''



import warnings

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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										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.'''   ,					lowerCAmelCase   ,					)
																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__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										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(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										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(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





