
import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class 						__magic_name__				(  snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		SpeechaTextaTokenizer
		_lowerCAmelCase						=		False





		def  _A							(							self      :		List[Any]			):
							super().setUp()

							lowerCAmelCase						:   Dict							=       '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''			)
							lowerCAmelCase						:   Any							=       ['''he re</w> 123''', '''here a 1456''']
							lowerCAmelCase						:   Optional[int]							=       dict(zip(lowerCamelCase__  ,					range(len(lowerCamelCase__			)			)			)			)

							lowerCAmelCase						:   str							=       {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

							lowerCAmelCase						:   Any							=       tempfile.mkdtemp()
							lowerCAmelCase						:   Dict							=       os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['''vocab_file''']			)
							lowerCAmelCase						:   Optional[Any]							=       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(lowerCamelCase__			) + '''\n'''			)

							with open(self.merges_file  ,					'''w'''			) as fp:
												fp.write('''\n'''.join(lowerCamelCase__			)			)





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   Union[str, Any]							=       list(self.get_tokenizer().get_vocab().keys()			)

							self.assertEqual(vocab_keys[0]  ,					'''<s>'''			)
							self.assertEqual(vocab_keys[1]  ,					'''<pad>'''			)
							self.assertEqual(vocab_keys[-1]  ,					'''vocab'''			)
							self.assertEqual(len(lowerCamelCase__			)  ,					1_4			)





		def  _A							(							self      :		Any			):
							self.assertEqual(self.get_tokenizer().vocab_size  ,					1_4			)





		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   Tuple							=       SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

							# make sure @@ is correctly concatenated
							lowerCAmelCase						:   Union[str, Any]							=       [4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
							lowerCAmelCase						:   int							=       tokenizer.decode(lowerCamelCase__			)

							self.assertTrue(output_string == '''herecouple words ofthe'''			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Optional[Any]							=       SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(lowerCamelCase__			)
												os.remove(os.path.join(lowerCamelCase__  ,					'''merges.txt'''			)			)

												# load tokenizer without merges file should not throw an error
												lowerCAmelCase						:   List[Any]							=       SpeechaTextaTokenizer.from_pretrained(lowerCamelCase__			)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(lowerCamelCase__			)
												lowerCAmelCase						:   Optional[int]							=       SpeechaTextaTokenizer.from_pretrained(lowerCamelCase__			)

							self.assertIsNotNone(lowerCamelCase__			)





		def  _A							(							self      :		List[str]			):
							if not self.test_slow_tokenizer:
												return

							lowerCAmelCase						:   str							=       inspect.signature(self.tokenizer_class.__init__			)
							lowerCAmelCase						:   Tuple							=       self.get_tokenizer()

							for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																	self.assertIn(lowerCamelCase__  ,					tokenizer.init_kwargs			)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
