
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[Any]					):
       if "model" in orig_key:
              snake_case :     Dict           =   orig_key.replace("model."       ,      ""					)
       if "norm1" in orig_key:
              snake_case :     Optional[Any]           =   orig_key.replace("norm1"       ,      "attention.output.LayerNorm"					)
       if "norm2" in orig_key:
              snake_case :     List[Any]           =   orig_key.replace("norm2"       ,      "output.LayerNorm"					)
       if "norm" in orig_key:
              snake_case :     Any           =   orig_key.replace("norm"       ,      "LayerNorm"					)
       if "transformer" in orig_key:
              snake_case :     Optional[int]           =   orig_key.split("."					)[0].split("_"					)[-1]
              snake_case :     Tuple           =   orig_key.replace(f"""transformer_{layer_num}"""       ,      f"""encoder.layer.{layer_num}"""					)
       if "mha.attn" in orig_key:
              snake_case :     Any           =   orig_key.replace("mha.attn"       ,      "attention.self"					)
       if "mha" in orig_key:
              snake_case :     List[str]           =   orig_key.replace("mha"       ,      "attention"					)
       if "W_q" in orig_key:
              snake_case :     int           =   orig_key.replace("W_q"       ,      "self.query"					)
       if "W_k" in orig_key:
              snake_case :     Any           =   orig_key.replace("W_k"       ,      "self.key"					)
       if "W_v" in orig_key:
              snake_case :     int           =   orig_key.replace("W_v"       ,      "self.value"					)
       if "ff1" in orig_key:
              snake_case :     Any           =   orig_key.replace("ff1"       ,      "intermediate.dense"					)
       if "ff2" in orig_key:
              snake_case :     Optional[Any]           =   orig_key.replace("ff2"       ,      "output.dense"					)
       if "ff" in orig_key:
              snake_case :     List[Any]           =   orig_key.replace("ff"       ,      "output.dense"					)
       if "mlm_class" in orig_key:
              snake_case :     Union[str, Any]           =   orig_key.replace("mlm.mlm_class"       ,      "cls.predictions.decoder"					)
       if "mlm" in orig_key:
              snake_case :     int           =   orig_key.replace("mlm"       ,      "cls.predictions.transform"					)
       if "cls" not in orig_key:
              snake_case :     List[Any]           =   "nystromformer." + orig_key

       return orig_key


def     UpperCamelCase      (					__lowerCamelCase  :				Tuple       ,      __lowerCamelCase  :				List[Any]					):
       for key in orig_state_dict.copy().keys():
              snake_case :     List[Any]           =   orig_state_dict.pop(__lowerCamelCase					)

              if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
                     continue
              else:
                     snake_case :     Tuple           =   val

       snake_case :     List[Any]           =   orig_state_dict["cls.predictions.decoder.bias"]
       snake_case :     Optional[int]           =   (
           torch.arange(config.max_position_embeddings					).expand((1, -1)					) + 2
       )

       return orig_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Optional[int]					):
       snake_case :     Any           =   torch.load(__lowerCamelCase       ,      map_location="cpu"					)["model_state_dict"]
       snake_case :     List[Any]           =   NystromformerConfig.from_json_file(__lowerCamelCase					)
       snake_case :     int           =   NystromformerForMaskedLM(__lowerCamelCase					)

       snake_case :     List[str]           =   convert_checkpoint_helper(__lowerCamelCase       ,      __lowerCamelCase					)

       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       model.save_pretrained(__lowerCamelCase					)

       print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""					)


if __name__ == "__main__":
       __lowerCamelCase							= argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The json file for Nystromformer model config.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       __lowerCamelCase							= parser.parse_args()
       convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







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 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


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

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [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(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





