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


snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case__						      =			{
    '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'
        ),
    },
}

snake_case__						      =			{
    '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,
}

snake_case__						      =			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class 			UpperCamelCase					(			_lowercase			):

     '''simple docstring'''

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



     def __init__(       self					,					A_=None					,					A_=None					,					A_=None					,					A_="replace"					,					A_="[CLS]"					,					A_="[SEP]"					,					A_="[SEP]"					,					A_="[CLS]"					,					A_="[UNK]"					,					A_="[PAD]"					,					A_="[MASK]"					,					A_=False					,					**A_					,					)			->					List[Any]:






           """simple docstring"""
           super().__init__(
               snake_case__					,					snake_case__					,					tokenizer_file=snake_case__					,					errors=snake_case__					,					bos_token=snake_case__					,					eos_token=snake_case__					,					unk_token=snake_case__					,					sep_token=snake_case__					,					cls_token=snake_case__					,					pad_token=snake_case__					,					mask_token=snake_case__					,					add_prefix_space=snake_case__					,					**snake_case__					,					)
           _lowerCamelCase           =					kwargs.pop('''add_bos_token'''					,					snake_case__    )

           _lowerCamelCase           =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
           if pre_tok_state.get('''add_prefix_space'''					,					snake_case__    ) != add_prefix_space:
                 _lowerCamelCase           =					getattr(snake_case__					,					pre_tok_state.pop('''type'''    )    )
                 _lowerCamelCase           =					add_prefix_space
                 _lowerCamelCase           =					pre_tok_class(**snake_case__    )

           _lowerCamelCase           =					add_prefix_space



     @property
     def 		UpperCamelCase_    (       self    )			->					Optional[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					,					A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					AddedToken(snake_case__					,					lstrip=snake_case__					,					rstrip=snake_case__    ) if isinstance(snake_case__					,					snake_case__    ) else value
           _lowerCamelCase           =					value



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Union[str, Any]:






           """simple docstring"""



           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]
           _lowerCamelCase           =					[self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					[self.sep_token_id]
           _lowerCamelCase           =					[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					,					*A_					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.get('''is_split_into_words'''					,					snake_case__    )
           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(*snake_case__					,					**snake_case__    )



     def 		UpperCamelCase_    (       self					,					*A_					,					**A_    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.get('''is_split_into_words'''					,					snake_case__    )

           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(*snake_case__					,					**snake_case__    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					self._tokenizer.model.save(snake_case__					,					name=snake_case__    )
           return tuple(snake_case__    )





     def 		UpperCamelCase_    (       self					,					A_    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					[]
           for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(snake_case__					,					add_special_tokens=snake_case__    ) + [self.eos_token_id]    )

           if len(snake_case__    ) > self.model_max_length:
                 _lowerCamelCase           =					input_ids[-self.model_max_length :]
           return input_ids

"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			lowercase		:    Union[str, Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
