


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



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

   return orig_key



def      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     List[Any]			)							->				str:
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase:					int	 =						orig_state_dict.pop(snake_case			)

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

   __lowerCamelCase:					Dict	 =						orig_state_dict["""cls.predictions.decoder.bias"""]
   __lowerCamelCase:					Union[str, Any]	 =						(
       torch.arange(config.max_position_embeddings			).expand((1, -1)			) + 2
   )

   return orig_state_dict



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

   __lowerCamelCase:					Any	 =						convert_checkpoint_helper(snake_case    ,   snake_case			)

   model.load_state_dict(snake_case			)
   model.eval()
   model.save_pretrained(snake_case			)

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


if __name__ == "__main__":
   _A		:					Any          =							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.'''
   )
   _A		:					Tuple          =							parser.parse_args()
   convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


