





'''simple docstring'''




import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	BioGptTokenizer
     lowerCAmelCase		      =	False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          __A						:			Any								=	[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              'w</w>',
              'r</w>',
              't</w>',
              'lo',
              'low',
              'er</w>',
              'low</w>',
              'lowest</w>',
              'newer</w>',
              'wider</w>',
              '<unk>',
          ]
          __A						:			Dict								=	dict(zip(_UpperCAmelCase						,     range(len(_UpperCAmelCase))))
          __A						:			Optional[Any]								=	['l o 123', 'lo w 1456', 'e r</w> 1789', '']

          __A						:			int								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['vocab_file'])
          __A						:			Union[str, Any]								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file						,     'w') as fp:
               fp.write(json.dumps(_UpperCAmelCase))
          with open(self.merges_file						,     'w') as fp:
               fp.write('\n'.join(_UpperCAmelCase))


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[Any]								=	'lower newer'
          __A						:			Optional[Any]								=	'lower newer'
          return input_text, output_text


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	BioGptTokenizer(self.vocab_file						,     self.merges_file)

          __A						:			List[str]								=	'lower'
          __A						:			Any								=	['low', 'er</w>']
          __A						:			int								=	tokenizer.tokenize(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          __A						:			int								=	tokens + ['<unk>']
          __A						:			str								=	[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase)						,     _UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	BioGptTokenizer.from_pretrained('microsoft/biogpt')

          __A						:			Optional[int]								=	tokenizer.encode('sequence builders'						,     add_special_tokens=_UpperCAmelCase)
          __A						:			Optional[int]								=	tokenizer.encode('multi-sequence build'						,     add_special_tokens=_UpperCAmelCase)

          __A						:			Optional[int]								=	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						,     _UpperCAmelCase)

          self.assertTrue(encoded_sentence == [2] + text)
          self.assertTrue(encoded_pair == [2] + text + [2] + text_a)






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''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)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

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