

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_       : Dict         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			{
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Any  							=       '''funnel'''
					UpperCAmelCase__:   int  							=       {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''n_head''',
					}






					def __init__( self    ,       A__=3_0522    ,       A__=[4, 4, 4]    ,       A__=None    ,       A__=2    ,       A__=768    ,       A__=12    ,       A__=64    ,       A__=3072    ,       A__="gelu_new"    ,       A__=0.1    ,       A__=0.1    ,       A__=0.0    ,       A__=0.1    ,       A__=None    ,       A__=1e-9    ,       A__="mean"    ,       A__="relative_shift"    ,       A__=True    ,       A__=True    ,       A__=True    ,       **A__    ,       ):
						A__							:    str        =		vocab_size
						A__							:    List[Any]        =		block_sizes
						A__							:    Optional[Any]        =		[1] * len(A__			) if block_repeats is None else block_repeats
						assert len(A__			) == len(
						    self.block_repeats			), "`block_sizes` and `block_repeats` should have the same length."
						A__							:    Union[str, Any]        =		num_decoder_layers
						A__							:    Optional[Any]        =		d_model
						A__							:    List[Any]        =		n_head
						A__							:    Optional[Any]        =		d_head
						A__							:    str        =		d_inner
						A__							:    List[Any]        =		hidden_act
						A__							:    Optional[int]        =		hidden_dropout
						A__							:    Optional[Any]        =		attention_dropout
						A__							:    int        =		activation_dropout
						A__							:    Tuple        =		initializer_range
						A__							:    Any        =		initializer_std
						A__							:    Optional[int]        =		layer_norm_eps
						assert pooling_type in [
						    "mean",
						    "max",
						], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
						A__							:    int        =		pooling_type
						assert attention_type in [
						    "relative_shift",
						    "factorized",
						], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
						A__							:    Tuple        =		attention_type
						A__							:    Any        =		separate_cls
						A__							:    int        =		truncate_seq
						A__							:    Any        =		pool_q_only

						super().__init__(**A__			)






					@property
					def   __A			( self			):
						return sum(self.block_sizes			)






					@num_hidden_layers.setter
					def   __A			( self    ,       A__			):
						raise NotImplementedError(
						    """This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."""			)






					@property
					def   __A			( self			):
						return len(self.block_sizes			)






					@num_blocks.setter
					def   __A			( self    ,       A__			):
						raise NotImplementedError("""This model does not support the setting of `num_blocks`. Please set `block_sizes`."""			)






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


A_       : List[str]         =			logging.get_logger(__name__)

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

A_       : List[Any]         =			{
    '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'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    '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						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						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__    ,       )

						A__							:    Tuple        =		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
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[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    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[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    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



