



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





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

    return orig_key





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    for key in orig_state_dict.copy().keys():
        __a		 =       orig_state_dict.pop(_UpperCAmelCase       )

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

    __a		 =       orig_state_dict['''cls.predictions.decoder.bias''']
    __a		 =       torch.arange(_UpperCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       torch.load(_UpperCAmelCase     ,				map_location='''cpu'''       )['''model_state_dict''']
    __a		 =       YosoConfig.from_json_file(_UpperCAmelCase       )
    __a		 =       YosoForMaskedLM(_UpperCAmelCase       )

    __a		 =       convert_checkpoint_helper(config.max_position_embeddings     ,				_UpperCAmelCase       )

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

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


if __name__ == "__main__":
     __snake_case		:Union[str, Any]   	=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for YOSO model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     __snake_case		:Optional[int]   	=  parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class 					_A (			__UpperCAmelCase					):



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
           return Dataset.from_dict(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(dset.column_names  ,	['''col_1''', '''col_2'''])
           for i, r in enumerate(__SCREAMING_SNAKE_CASE):
               self.assertDictEqual(__SCREAMING_SNAKE_CASE  ,	example_records[i])



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           __a		 =       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
           self.assertEqual(dset.info  ,	dset_from_dict.info)



       def 	_lowerCamelCase    (	self							:  int):  # checks what happens with missing columns


           '''simple docstring'''

           __a		 =       [{'''col_1''': 1}, {'''col_2''': '''x'''}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertDictEqual(dset[0]  ,	{'''col_1''': 1})
           self.assertDictEqual(dset[1]  ,	{'''col_1''': None})  # NB: first record is used for columns



       def 	_lowerCamelCase    (	self							:  Optional[Any]):  # checks if the type can be inferred from the second record


           '''simple docstring'''

           __a		 =       [{'''col_1''': []}, {'''col_1''': [1, 2]}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertEqual(dset.info.features['''col_1''']  ,	Sequence(Value('''int64''')))





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       Dataset.from_list([])
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	0)
           self.assertListEqual(dset.column_names  ,	[])





