




import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
	from transformers import LlamaTokenizerFast
except ImportError as e:
	warnings.warn(e)
	warnings.warn(
	    '''The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'''
	)
	a__		     =     None
a__		     =     {
    '''7B''': 1_1008,
    '''13B''': 1_3824,
    '''30B''': 1_7920,
    '''65B''': 2_2016,
    '''70B''': 2_8672,
}
a__		     =     {
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}





def 				A__ (snake_case   :		str		,	snake_case   :		Optional[Any]=1		,	snake_case   :		Optional[int]=2_56				)     ->						Dict:
					return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3				)				) + multiple_of - 1) // multiple_of)





def 				A__ (snake_case   :		Dict				)     ->						Optional[Any]:
					with open(snake_case		,	"""r"""				) as f:
										return json.load(snake_case				)





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		Any				)     ->						Dict:
					with open(snake_case		,	"""w"""				) as f:
										json.dump(snake_case		,	snake_case				)





def 				A__ (snake_case   :		Tuple		,	snake_case   :		List[Any]		,	snake_case   :		List[str]		,	snake_case   :		str=True				)     ->						int:
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					__UpperCamelCase					:	Optional[int]             =      os.path.join(snake_case		,	"""tmp"""				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)

					__UpperCamelCase					:	Any             =      read_json(os.path.join(snake_case		,	"""params.json"""				)				)
					__UpperCamelCase					:	Dict             =      NUM_SHARDS[model_size]
					__UpperCamelCase					:	List[Any]             =      params["""n_layers"""]
					__UpperCamelCase					:	Any             =      params["""n_heads"""]
					__UpperCamelCase					:	Optional[int]             =      n_heads // num_shards
					__UpperCamelCase					:	Tuple             =      params["""dim"""]
					__UpperCamelCase					:	Optional[int]             =      dim // n_heads
					__UpperCamelCase					:	Dict             =      10000.0
					__UpperCamelCase					:	str             =      1.0 / (base ** (torch.arange(0		,	snake_case		,	2				).float() / dims_per_head))

					if "n_kv_heads" in params:
										__UpperCamelCase					:	Any             =      params["""n_kv_heads"""]  # for GQA / MQA
										__UpperCamelCase					:	Optional[int]             =      n_heads_per_shard // num_key_value_heads
										__UpperCamelCase					:	List[Any]             =      dim // num_key_value_heads
					else:  # compatibility with other checkpoints
										__UpperCamelCase					:	Optional[Any]             =      n_heads
										__UpperCamelCase					:	Any             =      n_heads_per_shard
										__UpperCamelCase					:	str             =      dim

					# permute for sliced rotary
					def permute(snake_case   :		Tuple		,	snake_case   :		Dict=n_heads		,	snake_case   :		str=dim		,	snake_case   :		Optional[Any]=dim				):
										return w.view(snake_case		,	dima // n_heads // 2		,	2		,	snake_case				).transpose(1		,	2				).reshape(snake_case		,	snake_case				)

					print(F'''Fetching all parameters from the checkpoint at {input_base_path}.'''				)
					# Load weights
					if model_size == "7B":
										# Not sharded
										# (The sharded implementation would also work, but this is simpler.)
										__UpperCamelCase					:	Any             =      torch.load(os.path.join(snake_case		,	"""consolidated.00.pth"""				)		,	map_location="""cpu"""				)
					else:
										# Sharded
										__UpperCamelCase					:	Optional[int]             =      [
										    torch.load(os.path.join(snake_case		,	F'''consolidated.{i:02d}.pth'''				)		,	map_location="""cpu"""				)
										    for i in range(snake_case				)
										]
					__UpperCamelCase					:	Optional[int]             =      0
					__UpperCamelCase					:	List[str]             =      {"""weight_map""": {}}
					for layer_i in range(snake_case				):
										__UpperCamelCase					:	Tuple             =      F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
										if model_size == "7B":
															# Unsharded
															__UpperCamelCase					:	Optional[Any]             =      {
															    F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
															        loaded[F'''layers.{layer_i}.attention.wq.weight''']				),
															    F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
															        loaded[F'''layers.{layer_i}.attention.wk.weight''']				),
															    F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
															    F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
															    F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
															    F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
															    F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
															    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
															    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
															}
										else:
															# Sharded
															# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
															# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
															# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

															__UpperCamelCase					:	int             =      {
															    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
															        F'''layers.{layer_i}.attention_norm.weight'''
															    ].clone(),
															    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
															        F'''layers.{layer_i}.ffn_norm.weight'''
															    ].clone(),
															}
															__UpperCamelCase					:	Union[str, Any]             =      permute(
															    torch.cat(
															        [
															            loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(snake_case		,	snake_case		,	snake_case				)
															            for i in range(snake_case				)
															        ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)				)
															__UpperCamelCase					:	int             =      permute(
															    torch.cat(
															        [
															            loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
															                snake_case		,	snake_case		,	snake_case				)
															            for i in range(snake_case				)
															        ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)		,	snake_case		,	snake_case		,	snake_case		,	)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [
															        loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
															            snake_case		,	snake_case		,	snake_case				)
															        for i in range(snake_case				)
															    ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)

															__UpperCamelCase					:	str             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(snake_case				)]		,	dim=1				)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(snake_case				)]		,	dim=0				)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(snake_case				)]		,	dim=1				)
															__UpperCamelCase					:	List[str]             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(snake_case				)]		,	dim=0				)

										__UpperCamelCase					:	Dict             =      inv_freq
										for k, v in state_dict.items():
															__UpperCamelCase					:	str             =      filename
															param_count += v.numel()
										torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)

					__UpperCamelCase					:	Dict             =      F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
					if model_size == "7B":
										# Unsharded
										__UpperCamelCase					:	Dict             =      {
										    """model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
										    """model.norm.weight""": loaded["""norm.weight"""],
										    """lm_head.weight""": loaded["""output.weight"""],
										}
					else:
										__UpperCamelCase					:	Dict             =      {
										    """model.norm.weight""": loaded[0]["""norm.weight"""],
										    """model.embed_tokens.weight""": torch.cat(
										        [loaded[i]["""tok_embeddings.weight"""] for i in range(snake_case				)]		,	dim=1				),
										    """lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(snake_case				)]		,	dim=0				),
										}

					for k, v in state_dict.items():
										__UpperCamelCase					:	str             =      filename
										param_count += v.numel()
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)

					# Write configs
					__UpperCamelCase					:	Union[str, Any]             =      {"""total_size""": param_count * 2}
					write_json(snake_case		,	os.path.join(snake_case		,	"""pytorch_model.bin.index.json"""				)				)
					__UpperCamelCase					:	str             =      params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
					__UpperCamelCase					:	str             =      params["""multiple_of"""] if """multiple_of""" in params else 2_56
					__UpperCamelCase					:	str             =      LlamaConfig(
					    hidden_size=snake_case		,	intermediate_size=compute_intermediate_size(snake_case		,	snake_case		,	snake_case				)		,	num_attention_heads=params["""n_heads"""]		,	num_hidden_layers=params["""n_layers"""]		,	rms_norm_eps=params["""norm_eps"""]		,	num_key_value_heads=snake_case		,	)
					config.save_pretrained(snake_case				)

					# Make space so we can load the model properly now.
					del state_dict
					del loaded
					gc.collect()

					print("""Loading the checkpoint in a Llama model."""				)
					__UpperCamelCase					:	Optional[int]             =      LlamaForCausalLM.from_pretrained(snake_case		,	torch_dtype=torch.floataa		,	low_cpu_mem_usage=snake_case				)
					# Avoid saving this as part of the config.
					del model.config._name_or_path

					print("""Saving in the Transformers format."""				)
					model.save_pretrained(snake_case		,	safe_serialization=snake_case				)
					shutil.rmtree(snake_case				)





def 				A__ (snake_case   :		Any		,	snake_case   :		str				)     ->						Union[str, Any]:
					# Initialize the tokenizer based on the `spm` model
					__UpperCamelCase					:	Union[str, Any]             =      LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
					print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''				)
					__UpperCamelCase					:	Tuple             =      tokenizer_class(snake_case				)
					tokenizer.save_pretrained(snake_case				)





def 				A__ ()     ->						int:
					__UpperCamelCase					:	Union[str, Any]             =      argparse.ArgumentParser()
					parser.add_argument(
					    """--input_dir"""		,	help="""Location of LLaMA weights, which contains tokenizer.model and model folders"""		,	)
					parser.add_argument(
					    """--model_size"""		,	choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""]		,	)
					parser.add_argument(
					    """--output_dir"""		,	help="""Location to write HF model and tokenizer"""		,	)
					parser.add_argument("""--safe_serialization"""		,	type=snake_case		,	help="""Whether or not to save using `safetensors`."""				)
					__UpperCamelCase					:	str             =      parser.parse_args()
					if args.model_size != "tokenizer_only":
										write_model(
										    model_path=args.output_dir		,	input_base_path=os.path.join(args.input_dir		,	args.model_size				)		,	model_size=args.model_size		,	safe_serialization=args.safe_serialization		,	)
					__UpperCamelCase					:	int             =      os.path.join(args.input_dir		,	"""tokenizer.model"""				)
					write_tokenizer(args.output_dir		,	snake_case				)


if __name__ == "__main__":
	main()












import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		ProphetNetTokenizer
	__magic_name__   :       Dict      	=		False






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       List[str]:







						"""simple docstring"""







						super().setUp()

						__UpperCamelCase					:	Optional[Any]             =      [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """want""",
						    """##want""",
						    """##ed""",
						    """wa""",
						    """un""",
						    """runn""",
						    """##ing""",
						    """,""",
						    """low""",
						    """lowest""",
						]
						__UpperCamelCase					:	List[Any]             =      os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["""vocab_file"""]	)
						with open(self.vocab_file					,     """w"""					,     encoding="""utf-8"""	) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						int	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      """UNwant\u00E9d,running"""
						__UpperCamelCase					:	List[str]             =      """unwanted, running"""
						return input_text, output_text






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      self.tokenizer_class(self.vocab_file	)

						__UpperCamelCase					:	List[str]             =      tokenizer.tokenize("""UNwant\u00E9d,running"""	)
						self.assertListEqual(lowerCAmelCase					,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]	)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase	)					,     [9, 6, 7, 12, 10, 11]	)






	def 					lowerCamelCase__     (					self		:						str	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      BasicTokenizer()

						self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""	)					,     ["""ah""", """\u535A""", """\u63A8""", """zz"""]	)






	def 					lowerCamelCase__     (					self		:						int	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      BasicTokenizer(do_lower_case=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """	)					,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]	)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)					,     ["""hello"""]	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)					,     ["""hällo""", """!""", """how""", """are""", """you""", """?"""]	)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)					,     ["""h\u00E9llo"""]	)






	def 					lowerCamelCase__     (					self		:						Any	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)					,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""]	)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)					,     ["""hello"""]	)






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      BasicTokenizer(do_lower_case=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)					,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""]	)
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)					,     ["""hello"""]	)






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      BasicTokenizer(do_lower_case=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """	)					,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)					,     ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)					,     ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     never_split=["""[UNK]"""]	)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""	)					,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

						__UpperCamelCase					:	List[Any]             =      {}
						for i, token in enumerate(lowerCAmelCase	):
											__UpperCamelCase					:	Dict             =      i
						__UpperCamelCase					:	Any             =      WordpieceTokenizer(vocab=lowerCAmelCase					,     unk_token="""[UNK]"""	)

						self.assertListEqual(tokenizer.tokenize(""""""	)					,     []	)

						self.assertListEqual(tokenizer.tokenize("""unwanted running"""	)					,     ["""un""", """##want""", """##ed""", """runn""", """##ing"""]	)

						self.assertListEqual(tokenizer.tokenize("""unwantedX running"""	)					,     ["""[UNK]""", """runn""", """##ing"""]	)






	@require_torch
	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

						__UpperCamelCase					:	int             =      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						__UpperCamelCase					:	Optional[int]             =      [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
						__UpperCamelCase					:	Optional[int]             =      tokenizer(lowerCAmelCase					,     padding=lowerCAmelCase					,     return_tensors="""pt"""	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	int             =      list(batch.input_ids.numpy()[0]	)
						self.assertListEqual(lowerCAmelCase					,     lowerCAmelCase	)

						self.assertEqual((2, 9)					,     batch.input_ids.shape	)
						self.assertEqual((2, 9)					,     batch.attention_mask.shape	)






	def 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						self.assertTrue(_is_whitespace(""" """	)	)
						self.assertTrue(_is_whitespace("""\t"""	)	)
						self.assertTrue(_is_whitespace("""\r"""	)	)
						self.assertTrue(_is_whitespace("""\n"""	)	)
						self.assertTrue(_is_whitespace("""\u00A0"""	)	)

						self.assertFalse(_is_whitespace("""A"""	)	)
						self.assertFalse(_is_whitespace("""-"""	)	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Optional[int]:







						"""simple docstring"""







						self.assertTrue(_is_control("""\u0005"""	)	)

						self.assertFalse(_is_control("""A"""	)	)
						self.assertFalse(_is_control(""" """	)	)
						self.assertFalse(_is_control("""\t"""	)	)
						self.assertFalse(_is_control("""\r"""	)	)






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Optional[Any]:







						"""simple docstring"""







						self.assertTrue(_is_punctuation("""-"""	)	)
						self.assertTrue(_is_punctuation("""$"""	)	)
						self.assertTrue(_is_punctuation("""`"""	)	)
						self.assertTrue(_is_punctuation("""."""	)	)

						self.assertFalse(_is_punctuation("""A"""	)	)
						self.assertFalse(_is_punctuation(""" """	)	)






	@slow
	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

						__UpperCamelCase					:	Optional[int]             =      tokenizer.encode("""sequence builders"""					,     add_special_tokens=lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      tokenizer.encode("""multi-sequence build"""					,     add_special_tokens=lowerCAmelCase	)

						__UpperCamelCase					:	Optional[Any]             =      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase					,     lowerCAmelCase	)

						assert encoded_sentence == text + [102]
						assert encoded_pair == text + [102] + text_a + [102]






