


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE :					int			=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :					Tuple			=	{
    """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
}

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     int								=						'mobilevitv2'






      def __init__(     self					:   Dict,		__lowerCamelCase					:   List[Any]=3,		__lowerCamelCase					:   Optional[int]=2_56,		__lowerCamelCase					:   List[Any]=2,		__lowerCamelCase					:   Dict=2.0,		__lowerCamelCase					:   Dict="swish",		__lowerCamelCase					:   Any=3,		__lowerCamelCase					:   List[Any]=32,		__lowerCamelCase					:   Optional[Any]=0.1,		__lowerCamelCase					:   Tuple=0.02,		__lowerCamelCase					:   Optional[Any]=1e-5,		__lowerCamelCase					:   Dict=5_12,		__lowerCamelCase					:   Tuple=[6, 12, 18],		__lowerCamelCase					:   int=0.1,		__lowerCamelCase					:   Dict=2_55,		__lowerCamelCase					:   Dict=[2, 4, 3],		__lowerCamelCase					:   Any=[1_28, 1_92, 2_56],		__lowerCamelCase					:   Optional[int]=1.0,		__lowerCamelCase					:   Optional[int]=2,		__lowerCamelCase					:   List[Any]=0.0,		__lowerCamelCase					:   int=0.0,		**__lowerCamelCase					:   Dict,		)  -> List[Any]:
           super().__init__(**__lowerCamelCase   )

           UpperCamelCase__							:  Any								=     num_channels
           UpperCamelCase__							:  int								=     image_size
           UpperCamelCase__							:  str								=     patch_size
           UpperCamelCase__							:  List[Any]								=     expand_ratio
           UpperCamelCase__							:  Union[str, Any]								=     hidden_act
           UpperCamelCase__							:  List[str]								=     conv_kernel_size
           UpperCamelCase__							:  Dict								=     output_stride
           UpperCamelCase__							:  int								=     initializer_range
           UpperCamelCase__							:  Tuple								=     layer_norm_eps
           UpperCamelCase__							:  List[Any]								=     n_attn_blocks
           UpperCamelCase__							:  List[str]								=     base_attn_unit_dims
           UpperCamelCase__							:  List[str]								=     width_multiplier
           UpperCamelCase__							:  List[str]								=     ffn_multiplier
           UpperCamelCase__							:  str								=     ffn_dropout
           UpperCamelCase__							:  Union[str, Any]								=     attn_dropout
           UpperCamelCase__							:  int								=     classifier_dropout_prob

           # decode head attributes for semantic segmentation
           UpperCamelCase__							:  Dict								=     aspp_out_channels
           UpperCamelCase__							:  int								=     atrous_rates
           UpperCamelCase__							:  Optional[Any]								=     aspp_dropout_prob
           UpperCamelCase__							:  int								=     semantic_loss_ignore_index

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     int								=						version.parse('1.11'			)






      @property
      def 	__lowercase(     self					:   str   )  -> Mapping[str, Mapping[int, str]]:
           return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]   )






      @property
      def 	__lowercase(     self					:   List[str]   )  -> Mapping[str, Mapping[int, str]]:
           if self.task == "image-classification":
                return OrderedDict([('''logits''', {0: '''batch'''})]   )
           else:
                return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )






      @property
      def 	__lowercase(     self					:   Union[str, Any]   )  -> float:
           return 1e-4








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


_SCREAMING_SNAKE_CASE :					Tuple			=	logging.get_logger(__name__)

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

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

_SCREAMING_SNAKE_CASE :					Any			=	{
    """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,
}

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

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     int								=						VOCAB_FILES_NAMES
      a__   :     Optional[Any]								=						PRETRAINED_VOCAB_FILES_MAP
      a__   :     int								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__   :     str								=						['input_ids', 'attention_mask', 'token_type_ids']
      a__   :     int								=						DebertaTokenizer






      def __init__(     self					:   Optional[int],		__lowerCamelCase					:   List[str]=None,		__lowerCamelCase					:   Optional[Any]=None,		__lowerCamelCase					:   Union[str, Any]=None,		__lowerCamelCase					:   str="replace",		__lowerCamelCase					:   Any="[CLS]",		__lowerCamelCase					:   Optional[int]="[SEP]",		__lowerCamelCase					:   List[Any]="[SEP]",		__lowerCamelCase					:   Optional[Any]="[CLS]",		__lowerCamelCase					:   List[Any]="[UNK]",		__lowerCamelCase					:   Optional[Any]="[PAD]",		__lowerCamelCase					:   Dict="[MASK]",		__lowerCamelCase					:   List[Any]=False,		**__lowerCamelCase					:   Union[str, Any],		)  -> Optional[int]:
           super().__init__(
               __lowerCamelCase,		__lowerCamelCase,		tokenizer_file=__lowerCamelCase,		errors=__lowerCamelCase,		bos_token=__lowerCamelCase,		eos_token=__lowerCamelCase,		unk_token=__lowerCamelCase,		sep_token=__lowerCamelCase,		cls_token=__lowerCamelCase,		pad_token=__lowerCamelCase,		mask_token=__lowerCamelCase,		add_prefix_space=__lowerCamelCase,		**__lowerCamelCase,		)
           UpperCamelCase__							:  Any								=     kwargs.pop('''add_bos_token''',		__lowerCamelCase   )

           UpperCamelCase__							:  List[Any]								=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
           if pre_tok_state.get('''add_prefix_space''',		__lowerCamelCase   ) != add_prefix_space:
                UpperCamelCase__							:  Tuple								=     getattr(__lowerCamelCase,		pre_tok_state.pop('''type'''   )   )
                UpperCamelCase__							:  Tuple								=     add_prefix_space
                UpperCamelCase__							:  Union[str, Any]								=     pre_tok_class(**__lowerCamelCase   )

           UpperCamelCase__							:  Optional[int]								=     add_prefix_space






      @property
      def 	__lowercase(     self					:   Dict   )  -> str:
           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 	__lowercase(     self					:   List[Any],		__lowerCamelCase					:   Union[str, Any]   )  -> List[str]:
           UpperCamelCase__							:  List[Any]								=     AddedToken(__lowerCamelCase,		lstrip=__lowerCamelCase,		rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase,		__lowerCamelCase   ) else value
           UpperCamelCase__							:  List[str]								=     value






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCamelCase__							:  Union[str, Any]								=     [self.cls_token_id]
           UpperCamelCase__							:  Union[str, Any]								=     [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           UpperCamelCase__							:  Dict								=     [self.sep_token_id]
           UpperCamelCase__							:  int								=     [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 	__lowercase(     self					:   Union[str, Any],		*__lowerCamelCase					:   Any,		**__lowerCamelCase					:   List[Any]   )  -> BatchEncoding:
           UpperCamelCase__							:  str								=     kwargs.get('''is_split_into_words''',		__lowerCamelCase   )
           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(*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Optional[Any],		*__lowerCamelCase					:   str,		**__lowerCamelCase					:   int   )  -> BatchEncoding:
           UpperCamelCase__							:  str								=     kwargs.get('''is_split_into_words''',		__lowerCamelCase   )

           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(*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   str,		__lowerCamelCase					:   Optional[str] = None   )  -> Tuple[str]:
           UpperCamelCase__							:  Dict								=     self._tokenizer.model.save(__lowerCamelCase,		name=__lowerCamelCase   )
           return tuple(__lowerCamelCase   )






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   "Conversation"   )  -> List[int]:
           UpperCamelCase__							:  List[Any]								=     []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(__lowerCamelCase,		add_special_tokens=__lowerCamelCase   ) + [self.eos_token_id]   )

           if len(__lowerCamelCase   ) > self.model_max_length:
                UpperCamelCase__							:  List[Any]								=     input_ids[-self.model_max_length :]
           return input_ids




