

'''simple docstring'''



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def   lowerCamelCase__       (      a__):



    """simple docstring"""







    if "model" in orig_key:
        _snake_case : Any												=		orig_key.replace('model.'			,      '')
    if "norm1" in orig_key:
        _snake_case : List[str]												=		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 : int												=		orig_key.replace('norm'			,      'LayerNorm')
    if "transformer" in orig_key:
        _snake_case : str												=		orig_key.split('.')[0].split('_')[-1]
        _snake_case : Optional[Any]												=		orig_key.replace(F"""transformer_{layer_num}"""			,      F"""encoder.layer.{layer_num}""")
    if "mha.attn" in orig_key:
        _snake_case : Tuple												=		orig_key.replace('mha.attn'			,      'attention.self')
    if "mha" in orig_key:
        _snake_case : List[Any]												=		orig_key.replace('mha'			,      'attention')
    if "W_q" in orig_key:
        _snake_case : Optional[Any]												=		orig_key.replace('W_q'			,      'self.query')
    if "W_k" in orig_key:
        _snake_case : Union[str, Any]												=		orig_key.replace('W_k'			,      'self.key')
    if "W_v" in orig_key:
        _snake_case : str												=		orig_key.replace('W_v'			,      'self.value')
    if "ff1" in orig_key:
        _snake_case : Union[str, Any]												=		orig_key.replace('ff1'			,      'intermediate.dense')
    if "ff2" in orig_key:
        _snake_case : str												=		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 : Any												=		orig_key.replace('mlm.mlm_class'			,      'cls.predictions.decoder')
    if "mlm" in orig_key:
        _snake_case : Optional[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   lowerCamelCase__       (      a__			,      a__):



    """simple docstring"""







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

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

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

    return orig_state_dict

def   lowerCamelCase__       (      a__			,      a__			,      a__):



    """simple docstring"""







    _snake_case : List[Any]												=		torch.load(a__			,      map_location='cpu')['model_state_dict']
    _snake_case : List[str]												=		NystromformerConfig.from_json_file(a__)
    _snake_case : str												=		NystromformerForMaskedLM(a__)

    _snake_case : Optional[Any]												=		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__":
   SCREAMING_SNAKE_CASE_    =   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."
   )
   SCREAMING_SNAKE_CASE_    =   parser.parse_args()
   convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				'''fsmt_decoder'''




      def __init__(					self :		Any     ,					snake_case :		int=0     ,					snake_case :		Dict=0   ):



          """simple docstring"""





          super().__init__()
          _snake_case : int												=		vocab_size
          _snake_case : Dict												=		bos_token_id



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''fsmt'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Tuple=["en", "de"]     ,					snake_case :		Optional[int]=42024     ,					snake_case :		List[str]=42024     ,					snake_case :		Union[str, Any]="relu"     ,					snake_case :		Optional[Any]=1024     ,					snake_case :		Union[str, Any]=200     ,					snake_case :		int=1024     ,					snake_case :		str=4096     ,					snake_case :		Dict=12     ,					snake_case :		str=16     ,					snake_case :		List[str]=0.0     ,					snake_case :		List[Any]=4096     ,					snake_case :		str=12     ,					snake_case :		str=16     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		str=0.1     ,					snake_case :		Tuple=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		List[str]=2     ,					snake_case :		int=True     ,					snake_case :		Any=True     ,					snake_case :		str=False     ,					snake_case :		Union[str, Any]=5     ,					snake_case :		Dict=1.0     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Optional[int]=1     ,					snake_case :		int=0     ,					snake_case :		Optional[int]=2     ,					snake_case :		Optional[Any]=2     ,					**snake_case :		Any     ,					):



          """simple docstring"""





          _snake_case : int												=		langs
          _snake_case : str												=		src_vocab_size
          _snake_case : List[Any]												=		tgt_vocab_size
          _snake_case : Optional[int]												=		d_model  # encoder_embed_dim and decoder_embed_dim

          _snake_case : Optional[Any]												=		encoder_ffn_dim
          _snake_case : Dict												=		encoder_layers
          _snake_case : List[Any]												=		encoder_attention_heads
          _snake_case : Any												=		encoder_layerdrop
          _snake_case : List[str]												=		decoder_layerdrop
          _snake_case : str												=		decoder_ffn_dim
          _snake_case : Union[str, Any]												=		decoder_layers
          _snake_case : Tuple												=		decoder_attention_heads
          _snake_case : str												=		max_position_embeddings
          _snake_case : Dict												=		init_std  # Normal(0, this parameter)
          _snake_case : List[str]												=		activation_function

          _snake_case : Any												=		DecoderConfig(vocab_size=snake_case     ,					bos_token_id=snake_case   )
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          _snake_case : str												=		scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _snake_case : int												=		attention_dropout
          _snake_case : List[Any]												=		activation_dropout
          _snake_case : Optional[int]												=		dropout

          _snake_case : Union[str, Any]												=		use_cache
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					decoder_start_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					tie_word_embeddings=snake_case     ,					forced_eos_token_id=snake_case     ,					max_length=snake_case     ,					num_beams=snake_case     ,					length_penalty=snake_case     ,					early_stopping=snake_case     ,					**snake_case     ,					)




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Any												=		copy.deepcopy(self.__dict__   )
          _snake_case : str												=		self.decoder.to_dict()
          _snake_case : Dict												=		self.__class__.model_type
          return output



