





from typing import Any



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= []
        SCREAMING_SNAKE_CASE__       							= 0



    def __str__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= '''<''' + str(self.stack    )[1:-1] + '''>'''
        return printed



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        self.stack.append(A_    )
        SCREAMING_SNAKE_CASE__       							= self.length + 1



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.rotate(1    )
        SCREAMING_SNAKE_CASE__       							= self.stack[self.length - 1]
        SCREAMING_SNAKE_CASE__       							= self.stack[:-1]
        self.rotate(self.length - 1    )
        SCREAMING_SNAKE_CASE__       							= self.length - 1
        return dequeued



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        for _ in range(A_    ):
            SCREAMING_SNAKE_CASE__       							= self.stack[0]
            SCREAMING_SNAKE_CASE__       							= self.stack[1:]
            self.put(A_    )
            SCREAMING_SNAKE_CASE__       							= self.length - 1



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.get()
        self.put(A_    )
        self.rotate(self.length - 1    )
        return front



    def    lowercase_				(					self    ):





        '''simple docstring'''

        return self.length







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=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(lowerCAmelCase_   ,       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
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= te.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       nn.LayerNorm  ) and to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= te.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.Linear  ) and not to_transformer_engine and _convert_linear:
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= nn.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.LayerNorm  ) and not to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= nn.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

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



def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, 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(lowerCAmelCase_   ,       (te.LayerNorm, te.Linear)  ):
            return True
    return False
