






import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase  							= logging.get_logger(__name__)




class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	def __init__(	self	,      *a	,      **a )	->   None:
								warnings.warn(
								    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use ViTImageProcessor instead.'	,      a	,      )
								super().__init__(*a	,      **a )













import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
	import transformer_engine.pytorch as te





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

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

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

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





