
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te



def 		lowercase_     (  A__					,						A__=True					,						A__=True					,						A__=True						)				->				Optional[Any]:
    """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
            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 		lowercase_     (  A__						)				->				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(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  )

