



'''simple docstring'''




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
						from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
						lowerCamelCase							:Tuple				    =					get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
						from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCamelCase							:Tuple				    =					1_2_8_0_2_2
lowerCamelCase							:Optional[Any]				    =					1_2_8_0_2_8







@require_sentencepiece
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Tuple					=					MaMaaaTokenizer
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Any					=					False
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					True


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

				A_	:						Union[str, Any]	=     ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
				A_	:						Optional[Any]	=     dict(zip(A__       ,      range(len(A__							)							)							)							)
				A_	:						Optional[int]	=     Path(self.tmpdirname							)
				save_json(A__       ,      save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
				if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
							copyfile(A__       ,      save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

				A_	:						Tuple	=     MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
				tokenizer.save_pretrained(self.tmpdirname							)


	def     _a	(self       ,      **lowercase							):
				return MaMaaaTokenizer.from_pretrained(self.tmpdirname       ,      **A__							)


	def     _a	(self       ,      lowercase							):
				return (
				    "This is a test",
				    "This is a test",
				)


	def     _a	(self							):
				A_	:						Any	=     """</s>"""
				A_	:						str	=     0

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


	def     _a	(self							):
				A_	:						str	=     self.get_tokenizer()
				A_	:						Dict	=     list(tokenizer.get_vocab().keys()							)

				self.assertEqual(vocab_keys[0]       ,      """</s>"""							)
				self.assertEqual(vocab_keys[1]       ,      """<unk>"""							)
				self.assertEqual(vocab_keys[-1]       ,      """<s>"""							)
				self.assertEqual(len(A__							)       ,      tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)


	@unittest.skip("""Skip this test while all models are still to be uploaded."""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_	:						Optional[int]	=     self.get_tokenizer()

				A_	:						int	=     tokenizer.tokenize("""This is a test"""							)
				self.assertListEqual(A__       ,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(A__							)       ,      [2, 3, 4, 5, 6]       ,      )

				A_	:						Dict	=     tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
				self.assertListEqual(A__       ,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

				A_	:						Any	=     tokenizer.convert_tokens_to_string(A__							)
				self.assertEqual(A__       ,      """This is a test"""							)


	@slow
	def     _a	(self							):
				# fmt: off
				A_	:						int	=     {"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 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]], """attention_mask""": [[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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=A__       ,      model_name="""facebook/m2m100_418M"""       ,      revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""       ,      )







@require_torch
@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase							(			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					'''facebook/m2m100_418M'''
	__SCREAMING_SNAKE_CASE   :		Any					=					[
	    '''In my opinion, there are two levels of response from the French government.''',
	    '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
	]
	__SCREAMING_SNAKE_CASE   :		Any					=					[
	    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
	    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
	]

	# fmt: off
	__SCREAMING_SNAKE_CASE   :		List[str]					=					[EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]


	@classmethod
	def     _a	(cls							):
				A_	:						MaMaaaTokenizer	=     MaMaaaTokenizer.from_pretrained(
				    cls.checkpoint_name       ,      src_lang="""en"""       ,      tgt_lang="""fr"""							)
				A_	:						int	=     1
				return cls


	def     _a	(self							):
				self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)       ,      128006							)
				self.assertEqual(self.tokenizer.get_lang_id("""en"""							)       ,      128022							)
				self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)       ,      128076							)
				self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)       ,      128063							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     self.tokenizer.get_vocab()
				self.assertEqual(len(A__							)       ,      self.tokenizer.vocab_size							)
				self.assertEqual(vocab["""<unk>"""]       ,      3							)
				self.assertIn(self.tokenizer.get_lang_token("""en"""							)       ,      A__							)


	def     _a	(self							):
				A_	:						List[Any]	=     """en"""
				A_	:						str	=     self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
				self.assertListEqual(self.expected_src_tokens       ,      A__							)


	def     _a	(self							):
				self.assertIn(A__       ,      self.tokenizer.all_special_ids							)
				# fmt: off
				A_	:						Dict	=     [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
				# fmt: on
				A_	:						Dict	=     self.tokenizer.decode(A__       ,      skip_special_tokens=A__							)
				A_	:						List[str]	=     self.tokenizer.decode(generated_ids[1:]       ,      skip_special_tokens=A__							)
				self.assertEqual(A__       ,      A__							)
				self.assertNotIn(self.tokenizer.eos_token       ,      A__							)


	def     _a	(self							):
				A_	:						str	=     tempfile.mkdtemp()
				A_	:						Dict	=     self.tokenizer.lang_token_to_id
				self.tokenizer.save_pretrained(A__							)
				A_	:						List[Any]	=     MaMaaaTokenizer.from_pretrained(A__							)
				self.assertDictEqual(new_tok.lang_token_to_id       ,      A__							)


	@require_torch
	def     _a	(self							):
				A_	:						List[str]	=     """en"""
				A_	:						List[str]	=     """fr"""

				A_	:						int	=     self.tokenizer(self.src_text       ,      text_target=self.tgt_text       ,      padding=A__       ,      return_tensors="""pt"""							)

				A_	:						int	=     shift_tokens_right(
				    batch["""labels"""]       ,      self.tokenizer.pad_token_id       ,      self.tokenizer.eos_token_id							)

				for k in batch:
							A_	:						Any	=     batch[k].tolist()
				# batch = {k: v.tolist() for k,v in batch.items()}
				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				# batch.decoder_inputs_ids[0][0] ==
				assert batch.input_ids[1][0] == EN_CODE
				assert batch.input_ids[1][-1] == 2
				assert batch.labels[1][0] == FR_CODE
				assert batch.labels[1][-1] == 2
				assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


	@require_torch
	def     _a	(self							):
				A_	:						List[str]	=     """mr"""
				self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("""mr"""							)]							)
				self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)

				A_	:						Any	=     """zh"""
				self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("""zh"""							)]							)
				self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)


	@require_torch
	def     _a	(self							):
				A_	:						Optional[int]	=     """mr"""
				self.tokenizer._switch_to_target_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("""mr"""							)]							)
				self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)
				self.tokenizer._switch_to_input_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

				A_	:						Any	=     """zh"""
				self.tokenizer._switch_to_target_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id("""zh"""							)]							)
				self.assertListEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]							)
				self.tokenizer._switch_to_input_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens       ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)


	@require_torch
	def     _a	(self							):
				A_	:						Any	=     self.tokenizer._build_translation_inputs("""A test"""       ,      return_tensors="""pt"""       ,      src_lang="""en"""       ,      tgt_lang="""ar"""							)

				self.assertEqual(
				    nested_simplify(A__							)       ,      {
				        # en_XX, A, test, EOS
				        """input_ids""": [[128022, 58, 4183, 2]],
				        """attention_mask""": [[1, 1, 1, 1]],
				        # ar_AR
				        """forced_bos_token_id""": 128006,
				    }       ,      )


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep			) * [0]
						return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



