






'''simple docstring'''



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 lowercase       : int						=[
     '''decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(__A       ,   __A      )


def 				lowercase_					(     __A			:							List[str]      )							->   List[Any]:



 """simple docstring"""




 lowercase	,		lowercase       : int						=emb.weight.shape
 lowercase       : Dict						=nn.Linear(__A       ,   __A       ,   bias=__A      )
 lowercase       : Optional[int]						=emb.weight.data
 return lin_layer


def 				lowercase_					(     __A			:							List[str]      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase       : List[Any]						=torch.load(__A       ,   map_location='''cpu'''      )
 lowercase       : int						=Namespace(**checkpoint['''cfg''']['''model''']      )
 lowercase       : List[str]						=checkpoint['''model''']
 remove_ignore_keys_(__A      )
 lowercase       : Dict						=state_dict['''decoder.embed_tokens.weight'''].shape[0]

 lowercase       : Optional[Any]						={key.replace('''decoder'''       ,   '''model'''      ): val for key, val in state_dict.items()}

 lowercase       : List[str]						=XGLMConfig(
     vocab_size=__A       ,   max_position_embeddings=args.max_target_positions       ,   num_layers=args.decoder_layers       ,   attention_heads=args.decoder_attention_heads       ,   ffn_dim=args.decoder_ffn_embed_dim       ,   d_model=args.decoder_embed_dim       ,   layerdrop=args.decoder_layerdrop       ,   dropout=args.dropout       ,   attention_dropout=args.attention_dropout       ,   activation_dropout=args.activation_dropout       ,   activation_function='''gelu'''       ,   scale_embedding=not args.no_scale_embedding       ,   tie_word_embeddings=args.share_decoder_input_output_embed       ,   )

 lowercase       : Union[str, Any]						=XGLMForCausalLM(__A      )
 lowercase       : str						=model.load_state_dict(__A       ,   strict=__A      )
 print(__A      )
 lowercase       : Any						=make_linear_from_emb(model.model.embed_tokens      )

 return model


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
      parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      SCREAMING_SNAKE_CASE							=						parser.parse_args()
      SCREAMING_SNAKE_CASE							=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)











'''simple docstring'''



from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def 					A__   (			self      :		Optional[int]		)	->	List[str]:


       '''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 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

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






      def 					A__   (			self      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self._create_example_records()
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       lowercase       : Tuple						=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 					A__   (			self      :		str		)	->	List[Any]:  # checks what happens with missing columns


       '''simple docstring'''

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






      def 					A__   (			self      :		List[Any]		)	->	int:  # checks if the type can be inferred from the second record


       '''simple docstring'''

       lowercase       : List[Any]						=[{'''col_1''': []}, {'''col_1''': [1, 2]}]
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertEqual(dset.info.features['''col_1''']  ,   Sequence(Value('''int64'''		)		)		)






      def 					A__   (			self      :		int		)	->	str:


       '''simple docstring'''

       lowercase       : List[str]						=Dataset.from_list([]		)
       self.assertEqual(len(UpperCAmelCase		)  ,   0		)
       self.assertListEqual(dset.column_names  ,   []		)



