


"""simple docstring"""
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def 						lowercase				(	_snake_case		: int	,  _snake_case		: Dict	,  _snake_case		: int	)	->List[str]:
	"""simple docstring"""





	__snake_case      :		str         =							json.load(open(_snake_case	,  '''r'''	)	)
	__snake_case      :		Dict         =							GPTNeoConfig(
	    hidden_size=config_json['''n_embd''']	,  num_layers=config_json['''n_layer''']	,  num_heads=config_json['''n_head''']	,  attention_types=config_json['''attention_types''']	,  max_position_embeddings=config_json['''n_positions''']	,  resid_dropout=config_json['''res_dropout''']	,  embed_dropout=config_json['''embed_dropout''']	,  attention_dropout=config_json['''attn_dropout''']	,  )
	print(f"""Building PyTorch model from configuration: {config}"""	)
	__snake_case      :		Optional[Any]         =							GPTNeoForCausalLM(_snake_case	)

	# Load weights from tf checkpoint
	load_tf_weights_in_gpt_neo(_snake_case	,  _snake_case	,  _snake_case	)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained mesh-tf model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			SCREAMING_SNAKE_CASE					:       Tuple							   =							parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""
import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput



@dataclass
class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           =42
		lowerCamelCase__           =None
		lowerCamelCase__           =None



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=30   )
			self.assertEqual(x.a							,			30   )
			self.assertIsNone(x.b   )
			self.assertIsNone(x.c   )
			with self.assertRaises(a_   ):
				__snake_case      :		str         =							x.d



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(x[0]							,			30   )
			self.assertEqual(x[1]							,			10   )
			self.assertEqual(x[:2]							,			(30, 10)   )
			self.assertEqual(x[:]							,			(30, 10)   )

			__snake_case      :		List[Any]         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(x[0]							,			30   )
			self.assertEqual(x[1]							,			10   )
			self.assertEqual(x[:2]							,			(30, 10)   )
			self.assertEqual(x[:]							,			(30, 10)   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(x['''a''']							,			30   )
			self.assertEqual(x['''b''']							,			10   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x['''c''']

			__snake_case      :		Any         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(x['''a''']							,			30   )
			self.assertEqual(x['''c''']							,			10   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x['''b''']



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30   )
			self.assertEqual(list(x.keys()   )							,			['''a''']   )
			self.assertEqual(list(x.values()   )							,			[30]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30)]   )
			self.assertEqual(list(a_   )							,			['''a''']   )

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(list(x.values()   )							,			[30, 10]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30), ('''b''', 10)]   )
			self.assertEqual(list(a_   )							,			['''a''', '''b''']   )

			__snake_case      :		Optional[Any]         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''c''']   )
			self.assertEqual(list(x.values()   )							,			[30, 10]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30), ('''c''', 10)]   )
			self.assertEqual(list(a_   )							,			['''a''', '''c''']   )

			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x.update({'''d''': 20}   )
			with self.assertRaises(a_   ):
				del x["a"]
			with self.assertRaises(a_   ):
				__snake_case      :		Union[str, Any]         =							x.pop('''a'''   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[Any]         =							x.setdefault('''d'''							,			32   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							ModelOutputTest(a=30   )
			__snake_case      :		Tuple         =							10
			self.assertEqual(x.a							,			10   )
			self.assertEqual(x['''a''']							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							ModelOutputTest(a=30   )
			__snake_case      :		Any         =							10
			self.assertEqual(x.a							,			10   )
			self.assertEqual(x['''a''']							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							ModelOutputTest({'''a''': 30, '''b''': 10}   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(x.a							,			30   )
			self.assertEqual(x.b							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							ModelOutputTest([('''a''', 30), ('''b''', 10)]   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(x.a							,			30   )
			self.assertEqual(x.b							,			10   )

			with self.assertRaises(a_   ):
				__snake_case      :		List[Any]         =							ModelOutputTest([('''a''', 30), (10, 10)]   )

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=(30, 30)   )
			self.assertEqual(list(x.keys()   )							,			['''a''']   )
			self.assertEqual(x.a							,			(30, 30)   )





