




"""simple docstring"""



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       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = MPNetTokenizer
    lowercase__		   = MPNetTokenizerFast
    lowercase__		   = True
    lowercase__		   = True




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Union[str, Any]:




     '''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 				__magic_name__				(       self		:			str      ,					snake_case_		:			List[Any] ) ->				str:




     '''simple docstring'''

     A__			=							"UNwant\u00E9d,running"
     A__			=							"unwanted, running"
     return input_text, output_text




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




     '''simple docstring'''

     A__			=							self.tokenizer_class(self.vocab_file )

     A__			=							tokenizer.tokenize("UNwant\u00E9d,running" )
     self.assertListEqual(snake_case_      ,					["un", "##want", "##ed", ",", "runn", "##ing"] )
     self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )      ,					[9, 6, 7, 12, 10, 11] )




    @slow
    def 				__magic_name__				(       self		:			List[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

     A__			=							tokenizer.encode("sequence builders"      ,					add_special_tokens=snake_case_ )
     A__			=							tokenizer.encode("multi-sequence build"      ,					add_special_tokens=snake_case_ )

     A__			=							tokenizer.build_inputs_with_special_tokens(snake_case_ )
     A__			=							tokenizer.build_inputs_with_special_tokens(snake_case_      ,					snake_case_ )

     assert encoded_sentence == [0] + text + [2]
     assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]











"""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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"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(lowercase_		)

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			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							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





