


from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase	: Optional[Any]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[Any]  				={
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[Any]						=		'''roformer'''


				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Any=50_000			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:Dict=768			,			lowerCAmelCase__			:Optional[Any]=12			,			lowerCAmelCase__			:Dict=12			,			lowerCAmelCase__			:List[str]=3_072			,			lowerCAmelCase__			:Dict="gelu"			,			lowerCAmelCase__			:List[Any]=0.1			,			lowerCAmelCase__			:Dict=0.1			,			lowerCAmelCase__			:Union[str, Any]=1_536			,			lowerCAmelCase__			:Optional[Any]=2			,			lowerCAmelCase__			:Tuple=0.02			,			lowerCAmelCase__			:List[Any]=1E-1_2			,			lowerCAmelCase__			:Any=0			,			lowerCAmelCase__			:Dict=False			,			lowerCAmelCase__			:List[str]=True			,			**lowerCAmelCase__			:List[str]			,			)		->			Optional[int]:
										super().__init__(pad_token_id=lowerCAmelCase__			,			**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     str					=		hidden_size if embedding_size is None else embedding_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     Dict					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     Dict					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     str					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Dict					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		type_vocab_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:     List[str]					=		rotary_value
										__SCREAMING_SNAKE_CASE						:     Tuple					=		use_cache




class        _lowercase      (			A__       ):






				'''simple docstring'''



				@property
				def 		__magic_name__(       self			:List[Any]	)		->			Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE						:     Tuple					=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE						:     int					=		{0: '''batch''', 1: '''sequence'''}
										__SCREAMING_SNAKE_CASE						:     Any					=		{0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''token_type_ids''', dynamic_axis),
										    ]	)




from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:NestedDataStructureLike[PathLike]			,			lowerCAmelCase__			:Optional[NamedSplit] = None			,			lowerCAmelCase__			:Optional[Features] = None			,			lowerCAmelCase__			:str = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional[int] = None			,			**lowerCAmelCase__			:Optional[int]			,			)		->			Tuple:
										super().__init__(
										    lowerCAmelCase__			,			split=lowerCAmelCase__			,			features=lowerCAmelCase__			,			cache_dir=lowerCAmelCase__			,			keep_in_memory=lowerCAmelCase__			,			streaming=lowerCAmelCase__			,			num_proc=lowerCAmelCase__			,			**lowerCAmelCase__			,			)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		path_or_paths if isinstance(lowerCAmelCase__			,			lowerCAmelCase__	) else {self.split: path_or_paths}
										__SCREAMING_SNAKE_CASE						:     int					=		Text(
										    cache_dir=lowerCAmelCase__			,			data_files=lowerCAmelCase__			,			features=lowerCAmelCase__			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:Dict	)		->			Tuple:
										# Build iterable dataset
										if self.streaming:
																__SCREAMING_SNAKE_CASE						:     int					=		self.builder.as_streaming_dataset(split=self.split	)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		None
																__SCREAMING_SNAKE_CASE						:     str					=		None
																__SCREAMING_SNAKE_CASE						:     Dict					=		None
																__SCREAMING_SNAKE_CASE						:     Tuple					=		None

																self.builder.download_and_prepare(
																    download_config=lowerCAmelCase__			,			download_mode=lowerCAmelCase__			,			verification_mode=lowerCAmelCase__			,			base_path=lowerCAmelCase__			,			num_proc=self.num_proc			,			)
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.builder.as_dataset(
																    split=self.split			,			verification_mode=lowerCAmelCase__			,			in_memory=self.keep_in_memory	)
										return dataset
