code: 
  
  
  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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		json.load(open(UpperCAmelCase__       ,    """r"""      )      )
  						a_					=		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}"      )
  						a_					=		GPTNeoForCausalLM(UpperCAmelCase__      )
  
  						# Load weights from tf checkpoint
  						load_tf_weights_in_gpt_neo(UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  						# Save pytorch-model
  						print(F"Save PyTorch model to {pytorch_dump_path}"      )
  						model.save_pretrained(UpperCAmelCase__      )
  
  
  if __name__ == "__main__":
  	A_    :					int             =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."""
  	)
  	A_    :					int             =parser.parse_args()
  	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
  																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
  													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
  													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]