


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_	   =     {
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

lowercase_	   =     {
    'junnyu/roformer_chinese_small': 1_5_3_6,
    'junnyu/roformer_chinese_base': 1_5_3_6,
    'junnyu/roformer_chinese_char_small': 5_1_2,
    'junnyu/roformer_chinese_char_base': 5_1_2,
    'junnyu/roformer_small_discriminator': 1_2_8,
    'junnyu/roformer_small_generator': 1_2_8,
}


lowercase_	   =     {
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       VOCAB_FILES_NAMES
      __snake_case 	=       PRETRAINED_VOCAB_FILES_MAP
      __snake_case 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case 	=       PRETRAINED_INIT_CONFIGURATION
      __snake_case 	=       RoFormerTokenizer




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

       __lowerCamelCase		:							Optional[Any]		 =						json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
       if (
           pre_tok_state.get('lowercase'						,				a    ) != do_lower_case
           or pre_tok_state.get('strip_accents'						,				a    ) != strip_accents
       ):
        __lowerCamelCase		:							List[str]		 =						getattr(a						,				pre_tok_state.pop('type'    )    )
        __lowerCamelCase		:							List[str]		 =						do_lower_case
        __lowerCamelCase		:							Tuple		 =						strip_accents
        __lowerCamelCase		:							List[Any]		 =						pre_tok_class(**a    )

       __lowerCamelCase		:							Dict		 =						do_lower_case




      def __getstate__(    self:	str    ):
       __lowerCamelCase		:							int		 =						self.__dict__.copy()
       __lowerCamelCase		:							List[str]		 =						BertPreTokenizer()
       return state




      def __setstate__(    self:	Union[str, Any]						,				a:	Dict    ):
       __lowerCamelCase		:							Tuple		 =						d
       __lowerCamelCase		:							Optional[Any]		 =						self.__dict__['_tokenizer'].get_vocab()
       __lowerCamelCase		:							Dict		 =						PreTokenizer.custom(JiebaPreTokenizer(a    )    )




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	List[str]						,				a:	Any=None    ):
       __lowerCamelCase		:							int		 =						[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:	List[str]						,				a:	List[int]						,				a:	Optional[List[int]] = None    ):
       __lowerCamelCase		:							List[str]		 =						[self.sep_token_id]
       __lowerCamelCase		:							Any		 =						[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:	Dict						,				a:	str						,				a:	Optional[str] = None    ):
       __lowerCamelCase		:							List[str]		 =						self._tokenizer.model.save(a						,				name=a    )
       return tuple(a    )




      def 					_snake_case     (    self:	Any						,				a:	Any						,				a:	Any=None						,				a:	List[str]=None						,				a:	Dict=False						,				**a:	Tuple						,				):
       __lowerCamelCase		:							Union[str, Any]		 =						BertPreTokenizer()
       return super().save_pretrained(a						,				a						,				a						,				a						,				**a    )





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """vision-encoder-decoder"""
      __snake_case 	=       True




      def __init__(    self:	Union[str, Any]						,				**a:	Optional[Any]    ):
       super().__init__(**a    )
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'A configuraton of type {self.model_type} cannot be instantiated because '
            F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'    )

       __lowerCamelCase		:							Dict		 =						kwargs.pop('encoder'    )
       __lowerCamelCase		:							int		 =						encoder_config.pop('model_type'    )
       __lowerCamelCase		:							Any		 =						kwargs.pop('decoder'    )
       __lowerCamelCase		:							Union[str, Any]		 =						decoder_config.pop('model_type'    )

       __lowerCamelCase		:							Optional[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							List[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						True




      @classmethod
      def 					_snake_case     (    cls:	Optional[Any]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				**a:	Dict    ):
       logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'    )
       __lowerCamelCase		:							Optional[Any]		 =						True
       __lowerCamelCase		:							Optional[Any]		 =						True

       return cls(encoder=encoder_config.to_dict()						,				decoder=decoder_config.to_dict()						,				**a    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							int		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Dict		 =						self.encoder.to_dict()
       __lowerCamelCase		:							Union[str, Any]		 =						self.decoder.to_dict()
       __lowerCamelCase		:							Optional[int]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       version.parse("""1.11"""   )




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return 1e-4




      @property
      def 					_snake_case     (    self:	List[str]    ):
       return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Union[str, Any]		 =						OrderedDict()
       __lowerCamelCase		:							Union[str, Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Optional[Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Dict		 =						{0: 'batch', 1: 'encoder_sequence'}

       return common_inputs




      def 					_snake_case     (    self:	List[Any]						,				a:	"PreTrainedTokenizerBase"						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional["TensorType"] = None						,				):
       import torch

       __lowerCamelCase		:							str		 =						OrderedDict()

       __lowerCamelCase		:							List[Any]		 =						super().generate_dummy_inputs(
           a						,				batch_size=a						,				seq_length=a						,				is_pair=a						,				framework=a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						dummy_input['input_ids'].shape
       __lowerCamelCase		:							str		 =						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowerCamelCase		:							str		 =						dummy_input.pop('input_ids'    )
       __lowerCamelCase		:							Union[str, Any]		 =						dummy_input.pop('attention_mask'    )
       __lowerCamelCase		:							str		 =						torch.zeros(a    )

       return common_inputs



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	List[Any]    ):
       pass




      def 					_snake_case     (    self:	List[str]						,				a:	PretrainedConfig    ):
       return VisionEncoderDecoderEncoderOnnxConfig(a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				a:	str = "default"    ):
       __lowerCamelCase		:							List[Any]		 =						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(a						,				a    )

