



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 transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

