





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A_        =  get_tests_dir("fixtures/spiece.model")

@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_			(       snake_case_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     ErnieMTokenizer
					A__								=     False
					A__								=     True
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowerCamelCase__      =							ErnieMTokenizer(_lowerCAmelCase						,	unk_token="<unk>"						,	pad_token="<pad>"						)
										tokenizer.save_pretrained(self.tmpdirname						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							"this is a test"
										lowerCamelCase__      =							"this is a test"
										return input_text, output_text





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"<pad>"
										lowerCamelCase__      =							0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase						)						,	_lowerCAmelCase						)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase						)						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							list(self.get_tokenizer().get_vocab().keys()						)

										self.assertEqual(vocab_keys[0]						,	"<pad>"						)
										self.assertEqual(vocab_keys[1]						,	"<unk>"						)
										self.assertEqual(vocab_keys[-1]						,	"▁eloquent"						)
										self.assertEqual(len(_lowerCAmelCase						)						,	3_0000						)





					def        __magic_name__			(			self						):
										self.assertEqual(self.get_tokenizer().vocab_size						,	3_0000						)





					def        __magic_name__			(			self						):






										if not self.test_rust_tokenizer:
															return

										lowerCamelCase__      =							self.get_tokenizer()
										lowerCamelCase__      =							self.get_rust_tokenizer()

										lowerCamelCase__      =							"I was born in 92000, and this is falsé."

										lowerCamelCase__      =							tokenizer.tokenize(_lowerCAmelCase						)
										lowerCamelCase__      =							rust_tokenizer.tokenize(_lowerCAmelCase						)
										self.assertListEqual(_lowerCAmelCase						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tokenizer.encode(_lowerCAmelCase						,	add_special_tokens=_lowerCAmelCase						)
										lowerCamelCase__      =							rust_tokenizer.encode(_lowerCAmelCase						,	add_special_tokens=_lowerCAmelCase						)
										self.assertListEqual(_lowerCAmelCase						,	_lowerCAmelCase						)

										lowerCamelCase__      =							self.get_rust_tokenizer()
										lowerCamelCase__      =							tokenizer.encode(_lowerCAmelCase						)
										lowerCamelCase__      =							rust_tokenizer.encode(_lowerCAmelCase						)
										self.assertListEqual(_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ErnieMTokenizer(_lowerCAmelCase						,	do_lower_case=_lowerCAmelCase						,	unk_token="<unk>"						,	pad_token="<pad>"						)

										lowerCamelCase__      =							tokenizer.tokenize("This is a test"						)
										self.assertListEqual(_lowerCAmelCase						,	["▁this", "▁is", "▁a", "▁test"]						)

										self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase						)						,	[48, 25, 21, 1289]						)

										lowerCamelCase__      =							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
										# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
										self.assertListEqual(
										    _lowerCAmelCase						,	["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]						)
										lowerCamelCase__      =							tokenizer.convert_tokens_to_ids(_lowerCAmelCase						)
										self.assertListEqual(_lowerCAmelCase						,	[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]						)

										lowerCamelCase__      =							tokenizer.convert_ids_to_tokens(_lowerCAmelCase						)
										self.assertListEqual(
										    _lowerCAmelCase						,	["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]						,	)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ErnieMTokenizer(_lowerCAmelCase						,	unk_token="<unk>"						,	pad_token="<pad>"						)

										lowerCamelCase__      =							tokenizer.encode("sequence builders"						)
										lowerCamelCase__      =							tokenizer.encode("multi-sequence build"						)

										lowerCamelCase__      =							tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase						)
										lowerCamelCase__      =							tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase						,	_lowerCAmelCase						)

										assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
										assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
										    tokenizer.sep_token_id
										] + text_a + [tokenizer.sep_token_id]






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							{"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=_lowerCAmelCase						,	model_name="susnato/ernie-m-base_pytorch"						,	sequences=[
										        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
										        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
										        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
										        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
										        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
										        "conditioning on both left and right context in all layers.",
										        "The quick brown fox jumps over the lazy dog.",
										    ]						,	)





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							SCREAMING_SNAKE_CASE__       			=				None


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       			=				{
    """facebook/xglm-564M""": 2_0_4_8,
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]
							lowerCAmelCase__          =				XGLMTokenizer




							def __init__(  self						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 7
								lowercase_			 = [F'<madeupword{i}>' for i in range(self.num_madeup_words  )]

								lowercase_			 = kwargs.get("additional_special_tokens"						,       []  )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    UpperCAmelCase						,       tokenizer_file=UpperCAmelCase						,       bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       cls_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

								lowercase_			 = vocab_file
								lowercase_			 = False if not self.vocab_file else True




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a  ) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

								if not os.path.isdir(UpperCAmelCase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'  )
									return
								lowercase_			 = os.path.join(
								    UpperCAmelCase						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
									copyfile(self.vocab_file						,       UpperCAmelCase  )

								return (out_vocab_file,)

