
'''simple docstring'''







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
					import transformer_engine.pytorch as te

def 				_snake_case (		lowercase			,    lowercase=True			,    lowercase=True			,    lowercase=True						)       ->       Tuple:
				if not is_fpa_available():
								raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""						)
				for name, module in model.named_children():
								if isinstance(lowercase			,    nn.Linear						) and to_transformer_engine and _convert_linear:
												# Return early if the linear layer weights are not multiples of 16
												if any(p % 1_6 != 0 for p in module.weight.shape						):
																return
												__a       :     int								=	module.bias is not None
												__a       :     Union[str, Any]								=	te.Linear(
												    module.in_features			,    module.out_features			,    bias=lowercase			,    params_dtype=module.weight.dtype						)
												__a       :     Tuple								=	module.weight.data.clone()
												if has_bias:
																__a       :     Dict								=	module.bias.data.clone()

												setattr(lowercase			,    lowercase			,    lowercase						)
								elif isinstance(lowercase			,    nn.LayerNorm						) and to_transformer_engine and _convert_ln:
												__a       :     Dict								=	te.LayerNorm(module.normalized_shape[0]			,    eps=module.eps			,    params_dtype=module.weight.dtype						)
												__a       :     Union[str, Any]								=	module.weight.data.clone()
												__a       :     List[str]								=	module.bias.data.clone()

												setattr(lowercase			,    lowercase			,    lowercase						)
								elif isinstance(lowercase			,    te.Linear						) and not to_transformer_engine and _convert_linear:
												__a       :     Any								=	module.bias is not None
												__a       :     List[str]								=	nn.Linear(
												    module.in_features			,    module.out_features			,    bias=lowercase			,    params_dtype=module.weight.dtype						)
												__a       :     Any								=	module.weight.data.clone()
												if has_bias:
																__a       :     int								=	module.bias.data.clone()

												setattr(lowercase			,    lowercase			,    lowercase						)
								elif isinstance(lowercase			,    te.LayerNorm						) and not to_transformer_engine and _convert_ln:
												__a       :     Tuple								=	nn.LayerNorm(module.normalized_shape[0]			,    eps=module.eps			,    params_dtype=module.weight.dtype						)
												__a       :     List[str]								=	module.weight.data.clone()
												__a       :     Optional[Any]								=	module.bias.data.clone()

												setattr(lowercase			,    lowercase			,    lowercase						)
								else:
												convert_model(
												    lowercase			,    to_transformer_engine=lowercase			,    _convert_linear=_convert_linear			,    _convert_ln=_convert_ln			,    )

def 				_snake_case (		lowercase						)       ->       List[Any]:
				if not is_fpa_available():
								raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""						)
				for m in model.modules():
								if isinstance(lowercase			,    (te.LayerNorm, te.Linear)						):
												return True
				return False

'''simple docstring'''







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	vocab
					__a       :     int								=	normalizers.BertNormalizer(
					    clean_text=__UpperCamelCase  ,    handle_chinese_chars=__UpperCamelCase  ,    strip_accents=__UpperCamelCase  ,    lowercase=__UpperCamelCase  ,    )
					try:
									import rjieba
					except ImportError:
									raise ImportError(
									    """You need to install rjieba to use RoFormerTokenizer. """
									    """See https://pypi.org/project/rjieba/ for installation."""			)
					__a       :     Optional[Any]								=	rjieba
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	[]

					# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
					for token, start, end in self.jieba.tokenize(str(__UpperCamelCase			)  ,    hmm=__UpperCamelCase			):
									if token in self.vocab:
													splits.append(normalized_string[start:end]			)
									else:
													__a       :     str								=	self.normalizers.normalize_str(__UpperCamelCase			).split()
													for token in token_list:
																	if token:
																					__a       :     str								=	start + len(__UpperCamelCase			)
																					splits.append(normalized_string[start:end]			)
																					__a       :     Union[str, Any]								=	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

					return splits

	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					pretok.split(self.jieba_split			)