


from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''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  _a							(    _lowercase):
	_a  :  Optional[int]				   =							'''roformer'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		str=5_0000		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		Dict=768		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=12		, _SCREAMING_SNAKE_CASE						:		Dict=12		, _SCREAMING_SNAKE_CASE						:		Optional[int]=3072		, _SCREAMING_SNAKE_CASE						:		Any="gelu"		, _SCREAMING_SNAKE_CASE						:		List[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Tuple=0.1		, _SCREAMING_SNAKE_CASE						:		List[Any]=1536		, _SCREAMING_SNAKE_CASE						:		List[Any]=2		, _SCREAMING_SNAKE_CASE						:		List[str]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		List[Any]=0		, _SCREAMING_SNAKE_CASE						:		str=False		, _SCREAMING_SNAKE_CASE						:		List[Any]=True		, **_SCREAMING_SNAKE_CASE						:		Tuple		, )->	Dict:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

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





class  _a							(    _lowercase):




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








import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__       :      str   					= seq_shapes or {}
		for i in range(_a	):
				lowerCAmelCase__       :      List[str]   					= {}
				for col_id, (k, v) in enumerate(features.items()	):
						if isinstance(_a     ,       _ArrayXD	):
								lowerCAmelCase__       :      List[str]   					= np.random.rand(*v.shape	).astype(v.dtype	)
						elif isinstance(_a     ,       datasets.Value	):
								if v.dtype == "string":
										lowerCAmelCase__       :      Dict   					= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										lowerCAmelCase__       :      Any   					= np.random.randint(10     ,       size=1	).astype(v.dtype	).item()
						elif isinstance(_a     ,       datasets.Sequence	):
								while isinstance(_a     ,       datasets.Sequence	):
										lowerCAmelCase__       :      Optional[int]   					= v.feature
								lowerCAmelCase__       :      str   					= seq_shapes[k]
								lowerCAmelCase__       :      Any   					= np.random.rand(*_a	).astype(v.dtype	)
						lowerCAmelCase__       :      int   					= data

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= generate_examples(_a     ,       num_examples=_a     ,       seq_shapes=_a	)

		with ArrowWriter(features=_a     ,       path=_a	) as writer:
				for key, record in dummy_data:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'	)

		lowerCAmelCase__       :      List[Any]   					= datasets.Dataset.from_file(filename=_a     ,       info=datasets.DatasetInfo(features=_a	)	)

		return dataset




