




'''simple docstring'''







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
					import transformer_engine.pytorch as te





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     str=True,     SCREAMING_SNAKE_CASE__ :     Tuple=True,     SCREAMING_SNAKE_CASE__ :     Union[str, Any]=True				)	->		List[str]:






						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(SCREAMING_SNAKE_CASE__,     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_     : Optional[Any]         =					module.bias is not None
																		UpperCAmelCase_     : Union[str, Any]         =					te.Linear(
																		    module.in_features,     module.out_features,     bias=SCREAMING_SNAKE_CASE__,     params_dtype=module.weight.dtype				)
																		UpperCAmelCase_     : Any         =					module.weight.data.clone()
																		if has_bias:
																								UpperCAmelCase_     : Tuple         =					module.bias.data.clone()

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

																		setattr(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)
												elif isinstance(SCREAMING_SNAKE_CASE__,     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=SCREAMING_SNAKE_CASE__,     params_dtype=module.weight.dtype				)
																		UpperCAmelCase_     : Dict         =					module.weight.data.clone()
																		if has_bias:
																								UpperCAmelCase_     : Any         =					module.bias.data.clone()

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

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





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int				)	->		int:






						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(SCREAMING_SNAKE_CASE__,     (te.LayerNorm, te.Linear)				):
																		return True
						return False












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





