





"""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					):
					UpperCamelCase						=				MPNetTokenizer
					UpperCamelCase						=				MPNetTokenizerFast
					UpperCamelCase						=				True
					UpperCamelCase						=				True


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												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       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :List[Any]						):
												A						=    "UNwant\u00E9d,running"
												A						=    "unwanted, running"
												return input_text, output_text


					def       lowerCamelCase			(   self     :List[str]						):
												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       lowerCamelCase			(   self     :List[Any]						):
												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)





