





"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class  _UpperCAmelCase  (   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : List[Any]	    =   PhobertTokenizer
      lowercase_      : Dict	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        A_  :       Tuple										=  ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
        A_  :       Optional[int]										=  dict(zip(snake_case_					,      range(len(snake_case_  )  )  )  )
        A_  :       Dict										=  ['#version: 0.2', 'l à</w>']
        A_  :       Optional[int]										=  {'unk_token': '<unk>'}

        A_  :       str										=  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['vocab_file']  )
        A_  :       Any										=  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['merges_file']  )

        with open(self.vocab_file					,      'w'					,      encoding='utf-8'  ) as fp:
          for token in vocab_tokens:
            fp.write(F"""{token} {vocab_tokens[token]}\n"""  )
        with open(self.merges_file					,      'w'					,      encoding='utf-8'  ) as fp:
          fp.write('\n'.join(snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      **snake_case_  ):

        """simple docstring"""

        kwargs.update(self.special_tokens_map  )
        return PhobertTokenizer.from_pretrained(self.tmpdirname					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  'Tôi là VinAI Research'
        A_  :       Dict										=  'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
        return input_text, output_text

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  PhobertTokenizer(self.vocab_file					,      self.merges_file					,      **self.special_tokens_map  )
        A_  :       Optional[Any]										=  'Tôi là VinAI Research'
        A_  :       Any										=  'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
        A_  :       str										=  tokenizer.tokenize(snake_case_  )
        print(snake_case_  )
        self.assertListEqual(snake_case_					,      snake_case_  )

        A_  :       int										=  tokens + [tokenizer.unk_token]

        A_  :       Optional[Any]										=  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_  )					,      snake_case_  )






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)