
from unittest import TestCase

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



class    _snake_case (						UpperCAmelCase_				):


 def   lowercase__  (       self):


   '''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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   {"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
   return Dataset.from_dict(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self._create_example_records()
   lowercase__	: Union[str, Any]     		=   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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self._create_example_records()
   lowercase__	: Tuple     		=   Dataset.from_list(SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   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   lowercase__  (       self):  # checks what happens with missing columns


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   [{"""col_1""": 1}, {"""col_2""": """x"""}]
   lowercase__	: Dict     		=   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   lowercase__  (       self):  # checks if the type can be inferred from the second record


   '''simple docstring'''




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



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   Dataset.from_list([])
   self.assertEqual(len(SCREAMING_SNAKE_CASE_)       ,	0)
   self.assertListEqual(dset.column_names       ,	[])










import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

