


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def   snake_case_    (			__lowercase							):
   if "model" in orig_key:
      UpperCAmelCase_				:   Any							= orig_key.replace('''model.'''      ,							''''''							)
   if "norm1" in orig_key:
      UpperCAmelCase_				:   Tuple							= orig_key.replace('''norm1'''      ,							'''attention.output.LayerNorm'''							)
   if "norm2" in orig_key:
      UpperCAmelCase_				:   str							= orig_key.replace('''norm2'''      ,							'''output.LayerNorm'''							)
   if "norm" in orig_key:
      UpperCAmelCase_				:   Optional[int]							= orig_key.replace('''norm'''      ,							'''LayerNorm'''							)
   if "transformer" in orig_key:
      UpperCAmelCase_				:   str							= orig_key.split('''.'''							)[0].split('''_'''							)[-1]
      UpperCAmelCase_				:   List[Any]							= orig_key.replace(F'''transformer_{layer_num}'''      ,							F'''encoder.layer.{layer_num}'''							)
   if "mha.attn" in orig_key:
      UpperCAmelCase_				:   int							= orig_key.replace('''mha.attn'''      ,							'''attention.self'''							)
   if "mha" in orig_key:
      UpperCAmelCase_				:   int							= orig_key.replace('''mha'''      ,							'''attention'''							)
   if "W_q" in orig_key:
      UpperCAmelCase_				:   str							= orig_key.replace('''W_q'''      ,							'''self.query'''							)
   if "W_k" in orig_key:
      UpperCAmelCase_				:   Any							= orig_key.replace('''W_k'''      ,							'''self.key'''							)
   if "W_v" in orig_key:
      UpperCAmelCase_				:   int							= orig_key.replace('''W_v'''      ,							'''self.value'''							)
   if "ff.0" in orig_key:
      UpperCAmelCase_				:   Union[str, Any]							= orig_key.replace('''ff.0'''      ,							'''intermediate.dense'''							)
   if "ff.2" in orig_key:
      UpperCAmelCase_				:   Optional[int]							= orig_key.replace('''ff.2'''      ,							'''output.dense'''							)
   if "ff" in orig_key:
      UpperCAmelCase_				:   Union[str, Any]							= orig_key.replace('''ff'''      ,							'''output.dense'''							)
   if "mlm_class" in orig_key:
      UpperCAmelCase_				:   Union[str, Any]							= orig_key.replace('''mlm.mlm_class'''      ,							'''cls.predictions.decoder'''							)
   if "mlm" in orig_key:
      UpperCAmelCase_				:   List[str]							= orig_key.replace('''mlm'''      ,							'''cls.predictions.transform'''							)
   if "backbone.backbone.encoders" in orig_key:
      UpperCAmelCase_				:   int							= orig_key.replace('''backbone.backbone.encoders'''      ,							'''encoder.layer'''							)
   if "cls" not in orig_key:
      UpperCAmelCase_				:   List[Any]							= '''mra.''' + orig_key

   return orig_key
def   snake_case_    (			__lowercase      ,							__lowercase							):
   for key in orig_state_dict.copy().keys():
      UpperCAmelCase_				:   List[str]							= orig_state_dict.pop(__lowercase							)

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         UpperCAmelCase_				:   int							= val

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

   return orig_state_dict
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Union[str, Any]							= torch.load(__lowercase      ,							map_location='''cpu'''							)['''model_state_dict''']
   UpperCAmelCase_				:   Any							= MraConfig.from_json_file(__lowercase							)
   UpperCAmelCase_				:   List[str]							= MraForMaskedLM(__lowercase							)

   UpperCAmelCase_				:   Optional[int]							= convert_checkpoint_helper(config.max_position_embeddings      ,							__lowercase							)

   print(model.load_state_dict(__lowercase							)							)
   model.eval()
   model.save_pretrained(__lowercase							)

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


if __name__ == "__main__":
       __UpperCamelCase					:	str          =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help='The json file for Mra 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_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )





class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def __init__(						self					: Any							, __snake_case					: Tuple							, __snake_case					: Union[str, Any]=13							, __snake_case					: Tuple=7							, __snake_case					: str=True							, __snake_case					: List[Any]=True							, __snake_case					: Optional[int]=True							, __snake_case					: List[str]=True							, __snake_case					: Tuple=99							, __snake_case					: int=24							, __snake_case					: int=32							, __snake_case					: Tuple=5							, __snake_case					: int=4							, __snake_case					: Any=37							, __snake_case					: int="gelu"							, __snake_case					: Union[str, Any]=0.1							, __snake_case					: Optional[Any]=0.1							, __snake_case					: str=512							, __snake_case					: List[Any]=16							, __snake_case					: Tuple=2							, __snake_case					: List[str]=0.02							, __snake_case					: Any=4							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= parent
       UpperCAmelCase_				:   Dict							= batch_size
       UpperCAmelCase_				:   Union[str, Any]							= seq_length
       UpperCAmelCase_				:   Any							= is_training
       UpperCAmelCase_				:   Tuple							= use_attention_mask
       UpperCAmelCase_				:   Dict							= use_token_type_ids
       UpperCAmelCase_				:   str							= use_labels
       UpperCAmelCase_				:   Union[str, Any]							= vocab_size
       UpperCAmelCase_				:   Union[str, Any]							= hidden_size
       UpperCAmelCase_				:   Union[str, Any]							= embedding_size
       UpperCAmelCase_				:   Optional[int]							= num_hidden_layers
       UpperCAmelCase_				:   Tuple							= num_attention_heads
       UpperCAmelCase_				:   List[Any]							= intermediate_size
       UpperCAmelCase_				:   Union[str, Any]							= hidden_act
       UpperCAmelCase_				:   Any							= hidden_dropout_prob
       UpperCAmelCase_				:   List[str]							= attention_probs_dropout_prob
       UpperCAmelCase_				:   List[str]							= max_position_embeddings
       UpperCAmelCase_				:   Dict							= type_vocab_size
       UpperCAmelCase_				:   Tuple							= type_sequence_label_size
       UpperCAmelCase_				:   Dict							= initializer_range
       UpperCAmelCase_				:   Any							= num_choices





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   Any							= ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

       UpperCAmelCase_				:   Any							= None
       if self.use_attention_mask:
          UpperCAmelCase_				:   List[str]							= random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCAmelCase_				:   List[str]							= None
       if self.use_token_type_ids:
          UpperCAmelCase_				:   str							= ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size						)

       UpperCAmelCase_				:   int							= ElectraConfig(
           vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, embedding_size=self.embedding_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, initializer_range=self.initializer_range							, )

       return config, input_ids, token_type_ids, attention_mask





    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= self.prepare_config_and_inputs()
       UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_				:   Union[str, Any]							= config_and_inputs
       UpperCAmelCase_				:   Optional[Any]							= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





@require_flax
class 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Any								=			True

    A_	:  int								=			(
        (
            FlaxElectraModel,
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForPreTraining,
            FlaxElectraForTokenClassification,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForMultipleChoice,
            FlaxElectraForSequenceClassification,
        )
        if is_flax_available()
        else ()
    )





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= FlaxElectraModelTester(self						)





    @slow
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       for model_class_name in self.all_model_classes:
          if model_class_name == FlaxElectraForMaskedLM:
             UpperCAmelCase_				:   List[str]							= model_class_name.from_pretrained('''google/electra-small-generator'''						)
          else:
             UpperCAmelCase_				:   Optional[Any]							= model_class_name.from_pretrained('''google/electra-small-discriminator'''						)
          UpperCAmelCase_				:   Tuple							= model(np.ones((1, 1)						)						)
          self.assertIsNotNone(__snake_case						)