



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case     :Tuple			       =HfArgumentParser(InitializationArguments)
__snake_case     :Optional[int]			       =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case     :int			       =AutoTokenizer.from_pretrained(args.tokenizer_name)

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

# Initialize new model with config
__snake_case     :List[Any]			       =AutoModelForCausalLM.from_config(config)

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




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     CTRLTokenizer
				A_   :     List[str]								=     False
				A_   :     str								=     False


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											A      						=    ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, **__UpperCamelCase      :  List[str]       )		->     List[Any]:
											kwargs.update(self.special_tokens_map       )
											return CTRLTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  Optional[int]       )		->     Optional[Any]:
											A      						=    'adapt react readapt apt'
											A      						=    'adapt react readapt apt'
											return input_text, output_text


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[int]:
											A      						=    CTRLTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map       )
											A      						=    'adapt react readapt apt'
											A      						=    'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
											A      						=    tokenizer.tokenize(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    tokens + [tokenizer.unk_token]

											A      						=    [0, 1, 2, 4, 5, 1, 0, 3, 6]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase       )						, __UpperCamelCase       )