

'''simple docstring'''



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def      a__ (						a__		):




     """simple docstring"""


     if "model" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""model."""   ,					""""""		)
     if "norm1" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""norm1"""   ,					"""attention.output.LayerNorm"""		)
     if "norm2" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""norm2"""   ,					"""output.LayerNorm"""		)
     if "norm" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""norm"""   ,					"""LayerNorm"""		)
     if "transformer" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.split("""."""		)[0].split("""_"""		)[-1]
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace(F'transformer_{layer_num}'   ,					F'encoder.layer.{layer_num}'		)
     if "mha.attn" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""mha.attn"""   ,					"""attention.self"""		)
     if "mha" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""mha"""   ,					"""attention"""		)
     if "W_q" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""W_q"""   ,					"""self.query"""		)
     if "W_k" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""W_k"""   ,					"""self.key"""		)
     if "W_v" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""W_v"""   ,					"""self.value"""		)
     if "ff1" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""ff1"""   ,					"""intermediate.dense"""		)
     if "ff2" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""ff2"""   ,					"""output.dense"""		)
     if "ff" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""ff"""   ,					"""output.dense"""		)
     if "mlm_class" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""mlm.mlm_class"""   ,					"""cls.predictions.decoder"""		)
     if "mlm" in orig_key:
          __SCREAMING_SNAKE_CASE						     =   orig_key.replace("""mlm"""   ,					"""cls.predictions.transform"""		)
     if "cls" not in orig_key:
          __SCREAMING_SNAKE_CASE						     =   """nystromformer.""" + orig_key

     return orig_key







def      a__ (						a__   ,					a__		):




     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __SCREAMING_SNAKE_CASE						     =   orig_state_dict.pop(a__		)

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

     __SCREAMING_SNAKE_CASE						     =   orig_state_dict["""cls.predictions.decoder.bias"""]
     __SCREAMING_SNAKE_CASE						     =   (
         torch.arange(config.max_position_embeddings		).expand((1, -1)		) + 2
     )

     return orig_state_dict







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   torch.load(a__   ,					map_location="""cpu"""		)["""model_state_dict"""]
     __SCREAMING_SNAKE_CASE						     =   NystromformerConfig.from_json_file(a__		)
     __SCREAMING_SNAKE_CASE						     =   NystromformerForMaskedLM(a__		)

     __SCREAMING_SNAKE_CASE						     =   convert_checkpoint_helper(a__   ,					a__		)

     model.load_state_dict(a__		)
     model.eval()
     model.save_pretrained(a__		)

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


if __name__ == "__main__":
  UpperCAmelCase				: Union[str, 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.'
  )
  UpperCAmelCase				: Dict         =  parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      super().__init__(
          is_encoder_decoder=__SCREAMING_SNAKE_CASE      ,							tokenizer_class=__SCREAMING_SNAKE_CASE      ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							pad_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							decoder_start_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   act_info[0] == """gated"""

      if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 2:
           raise ValueError(
               f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
               """'gated-gelu' or 'relu'"""    )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )

      return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






