






"""simple docstring"""





import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te






def 							A(    snake_case_		,  snake_case_=True		,  snake_case_=True		,  snake_case_=True						):
       """simple docstring"""





       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(snake_case_		,  nn.Linear						) and to_transformer_engine and _convert_linear:
                     # Return early if the linear layer weights are not multiples of 16
                     if any(p % 16 != 0 for p in module.weight.shape						):
                            return
                     lowercase__:							int         =     module.bias is not None
                     lowercase__:							Union[str, Any]         =     te.Linear(
                         module.in_features		,  module.out_features		,  bias=snake_case_		,  params_dtype=module.weight.dtype						)
                     lowercase__:							Tuple         =     module.weight.data.clone()
                     if has_bias:
                            lowercase__:							Dict         =     module.bias.data.clone()

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

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

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

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







def 							A(    snake_case_						):
       """simple docstring"""





       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(snake_case_		,  (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			)