



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
      import transformer_engine.pytorch as te







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						int=True   ,		_a       :						Dict=True   ,		_a       :						int=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(_a   ,		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
            UpperCAmelCase_     :       Dict  		=					module.bias is not None
            UpperCAmelCase_     :       Optional[int]  		=					te.Linear(
                module.in_features   ,		module.out_features   ,		bias=_a   ,		params_dtype=module.weight.dtype    )
            UpperCAmelCase_     :       Tuple  		=					module.weight.data.clone()
            if has_bias:
                UpperCAmelCase_     :       Optional[Any]  		=					module.bias.data.clone()

            setattr(_a   ,		_a   ,		_a    )
        elif isinstance(_a   ,		nn.LayerNorm    ) and to_transformer_engine and _convert_ln:
            UpperCAmelCase_     :       Optional[int]  		=					te.LayerNorm(module.normalized_shape[0]   ,		eps=module.eps   ,		params_dtype=module.weight.dtype    )
            UpperCAmelCase_     :       Optional[int]  		=					module.weight.data.clone()
            UpperCAmelCase_     :       List[str]  		=					module.bias.data.clone()

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

            setattr(_a   ,		_a   ,		_a    )
        elif isinstance(_a   ,		te.LayerNorm    ) and not to_transformer_engine and _convert_ln:
            UpperCAmelCase_     :       Dict  		=					nn.LayerNorm(module.normalized_shape[0]   ,		eps=module.eps   ,		params_dtype=module.weight.dtype    )
            UpperCAmelCase_     :       Optional[int]  		=					module.weight.data.clone()
            UpperCAmelCase_     :       Tuple  		=					module.bias.data.clone()

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







def       lowerCamelCase_  (    _a       :						Tuple    ):



    '''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(_a   ,		(te.LayerNorm, te.Linear)    ):
            return True
    return False





from math import cos, sin







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					float(_a    )
    return [[scaling_factor * int(x == y    ) for x in range(2    )] for y in range(2    )]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cos(_a    ), sin(_a    )
    return [[c, -s], [s, c]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Dict  		=					c * s
    return [[c * c, cs], [cs, s * s]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					c * s
    return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"    {scaling(5) = }")
print(F"  {rotation(45) = }")
print(F"{projection(45) = }")






print(F"{reflection(45) = }")
