"""simple docstring"""







import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Any					=	CTRLTokenizer
				UpperCAmelCase       :   List[Any]					=	False
				UpperCAmelCase       :   str					=	False



				def       _lowercase  (self       :   Optional[int])					->			Tuple:
											super().setUp()

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

											__snake_case    :							Any   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file'])
											__snake_case    :							Dict   	=				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(_A) + '\n')
											with open(self.merges_file			,						'w'			,						encoding='utf-8') as fp:
																		fp.write('\n'.join(_A))



				def       _lowercase  (self       :   Union[str, Any]			,						**_A       :   Union[str, Any])					->			Tuple:
											kwargs.update(self.special_tokens_map)
											return CTRLTokenizer.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   str			,						_A       :   Any)					->			Tuple:
											__snake_case    :							int   	=				'adapt react readapt apt'
											__snake_case    :							int   	=				'adapt react readapt apt'
											return input_text, output_text






				def       _lowercase  (self       :   Tuple)					->			str:
											__snake_case    :							int   	=				CTRLTokenizer(self.vocab_file			,						self.merges_file			,						**self.special_tokens_map)
											__snake_case    :							Dict   	=				'adapt react readapt apt'
											__snake_case    :							int   	=				'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
											__snake_case    :							Optional[Any]   	=				tokenizer.tokenize(_A)
											self.assertListEqual(_A			,						_A)

											__snake_case    :							List[str]   	=				tokens + [tokenizer.unk_token]

											__snake_case    :							Union[str, Any]   	=				[0, 1, 2, 4, 5, 1, 0, 3, 6]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)			,						_A)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

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

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

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

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





