
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_											=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_											=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	VOCAB_FILES_NAMES
     lowerCAmelCase_     =	PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_     =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     lowerCAmelCase_     =	DebertaTokenizer



     def __init__(							self    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """simple docstring"""




       super().__init__(
           _A      ,		_A      ,		tokenizer_file=_A      ,		errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       if self._mask_token is None:
         if self.verbose:
           logger.error('''Using mask_token, but it is not set yet.'''				)
         return None
       return str(self._mask_token				)



     @mask_token.setter
     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[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 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.get('''is_split_into_words'''      ,		_A				)
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.get('''is_split_into_words'''      ,		_A				)

       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(_A      ,		add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						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__(_A      ,		_A				)



     def __call__(							self    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

       if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''				)

       # first, apply the image processor
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.image_processor(images=_A      ,		return_tensors=_A				)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						features['''words''']

       __SCREAMING_SNAKE_CASE     :					str			=						self.tokenizer(
           text=text if text is not None else features['''words''']      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features['''boxes''']      ,		word_labels=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

       # add pixel values
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

       if len(_A				) != len(_A				):
         raise ValueError(
             '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
             F''' {len(_A				)} and {len(_A				)}'''				)

       return images_with_overflow



     def 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




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



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




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




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




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