





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 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        return True




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= CpmTokenizer.from_pretrained('''TsinghuaAI/CPM-Generate'''    )
        SCREAMING_SNAKE_CASE__       							= '''Hugging Face大法好，谁用谁知道。'''
        SCREAMING_SNAKE_CASE__       							= '''Hugging Face大法好,谁用谁知道。<unk>'''
        SCREAMING_SNAKE_CASE__       							= '''▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'''.split()

        SCREAMING_SNAKE_CASE__       							= tokenizer.tokenize(A_    )
        self.assertListEqual(A_      ,							A_    )

        SCREAMING_SNAKE_CASE__       							= tokens + [tokenizer.unk_token]

        SCREAMING_SNAKE_CASE__       							= [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )      ,							A_    )

        SCREAMING_SNAKE_CASE__       							= tokenizer.decode(A_    )
        self.assertEqual(A_      ,							A_    )







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True  )							->	Tuple:
    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(lowerCAmelCase_   ,       nn.Linear  ) and to_transformer_engine and _convert_linear:
            # Return early if the linear layer weights are not multiples of 16
            if any(p % 1_6 != 0 for p in module.weight.shape  ):
                return
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= te.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

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

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

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

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



def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    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(lowerCAmelCase_   ,       (te.LayerNorm, te.Linear)  ):
            return True
    return False
