






import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
						import transformer_engine.pytorch as te






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase=True    ,	UpperCAmelCase=True    ,	UpperCAmelCase=True     )     ->		int:







							"""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(UpperCAmelCase    ,	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
																					_a  :    str			=						module.bias is not None
																					_a  :    Optional[int]			=						te.Linear(
																					    module.in_features    ,	module.out_features    ,	bias=UpperCAmelCase    ,	params_dtype=module.weight.dtype     )
																					_a  :    Optional[Any]			=						module.weight.data.clone()
																					if has_bias:
																												_a  :    List[str]			=						module.bias.data.clone()

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

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

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

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







def    UpperCamelCase__  (   UpperCAmelCase     )     ->		List[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(UpperCAmelCase    ,	(te.LayerNorm, te.Linear)     ):
																					return True
							return False







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= ['BartTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)