
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
		import transformer_engine.pytorch as te
def       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       List[str]=True							,					__magic_name__  :       Tuple=True							,					__magic_name__  :       Dict=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(_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 % 16 != 0 for p in module.weight.shape					):
																				return
															lowerCAmelCase						:   int							=       module.bias is not None
															lowerCAmelCase						:   int							=       te.Linear(
															    module.in_features							,					module.out_features							,					bias=_lowerCAmelCase							,					params_dtype=module.weight.dtype					)
															lowerCAmelCase						:   Optional[Any]							=       module.weight.data.clone()
															if has_bias:
																				lowerCAmelCase						:   Union[str, Any]							=       module.bias.data.clone()

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

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

															setattr(_lowerCAmelCase							,					_lowerCAmelCase							,					_lowerCAmelCase					)
										elif isinstance(_lowerCAmelCase							,					te.LayerNorm					) and not to_transformer_engine and _convert_ln:
															lowerCAmelCase						:   Tuple							=       nn.LayerNorm(module.normalized_shape[0]							,					eps=module.eps							,					params_dtype=module.weight.dtype					)
															lowerCAmelCase						:   Optional[int]							=       module.weight.data.clone()
															lowerCAmelCase						:   Dict							=       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       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]					):


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


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
