






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase : Optional[int]						   =       get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
@require_tokenizers
class 		lowerCAmelCase     ( _a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Tuple									=      XLMRobertaTokenizer
					_A						:			Dict									=      XLMRobertaTokenizerFast
					_A						:			Tuple									=      True
					_A						:			Optional[int]									=      True



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[int]:


												"""simple docstring"""

												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowercase      :							str				=     XLMRobertaTokenizer(snake_case_		,     keep_accents=snake_case_							)
												tokenizer.save_pretrained(self.tmpdirname							)



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


												"""simple docstring"""

												__lowercase      :							int				=     """<pad>"""
												__lowercase      :							Tuple				=     1

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



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


												"""simple docstring"""

												__lowercase      :							int				=     list(self.get_tokenizer().get_vocab().keys()							)

												self.assertEqual(vocab_keys[0]		,     """<s>"""							)
												self.assertEqual(vocab_keys[1]		,     """<pad>"""							)
												self.assertEqual(vocab_keys[-1]		,     """<mask>"""							)
												self.assertEqual(len(snake_case_							)		,     1002							)



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


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size		,     1002							)



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


												"""simple docstring"""

												__lowercase      :							List[str]				=     XLMRobertaTokenizer(snake_case_		,     keep_accents=snake_case_							)

												__lowercase      :							str				=     tokenizer.tokenize("""This is a test"""							)
												self.assertListEqual(snake_case_		,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(snake_case_							)		,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,     )

												__lowercase      :							Tuple				=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
												self.assertListEqual(
												    snake_case_		,     [
												        SPIECE_UNDERLINE + """I""",
												        SPIECE_UNDERLINE + """was""",
												        SPIECE_UNDERLINE + """b""",
												        """or""",
												        """n""",
												        SPIECE_UNDERLINE + """in""",
												        SPIECE_UNDERLINE + """""",
												        """9""",
												        """2""",
												        """0""",
												        """0""",
												        """0""",
												        """,""",
												        SPIECE_UNDERLINE + """and""",
												        SPIECE_UNDERLINE + """this""",
												        SPIECE_UNDERLINE + """is""",
												        SPIECE_UNDERLINE + """f""",
												        """al""",
												        """s""",
												        """é""",
												        """.""",
												    ]		,     )
												__lowercase      :							int				=     tokenizer.convert_tokens_to_ids(snake_case_							)
												self.assertListEqual(
												    snake_case_		,     [
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
												    ]		,     )

												__lowercase      :							int				=     tokenizer.convert_ids_to_tokens(snake_case_							)
												self.assertListEqual(
												    snake_case_		,     [
												        SPIECE_UNDERLINE + """I""",
												        SPIECE_UNDERLINE + """was""",
												        SPIECE_UNDERLINE + """b""",
												        """or""",
												        """n""",
												        SPIECE_UNDERLINE + """in""",
												        SPIECE_UNDERLINE + """""",
												        """<unk>""",
												        """2""",
												        """0""",
												        """0""",
												        """0""",
												        """,""",
												        SPIECE_UNDERLINE + """and""",
												        SPIECE_UNDERLINE + """this""",
												        SPIECE_UNDERLINE + """is""",
												        SPIECE_UNDERLINE + """f""",
												        """al""",
												        """s""",
												        """<unk>""",
												        """.""",
												    ]		,     )



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


												"""simple docstring"""

												if not self.test_slow_tokenizer:
																			# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																			return

												__lowercase      :							List[str]				=     (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
																										__lowercase      :							int				=     self.rust_tokenizer_class.from_pretrained(snake_case_		,     **snake_case_							)
																										__lowercase      :							str				=     self.tokenizer_class.from_pretrained(snake_case_		,     **snake_case_							)

																										__lowercase      :							Union[str, Any]				=     tempfile.mkdtemp()

																										__lowercase      :							List[Any]				=     tokenizer_r.save_pretrained(snake_case_							)
																										__lowercase      :							Union[str, Any]				=     tokenizer_p.save_pretrained(snake_case_							)

																										# Checks it save with the same files + the tokenizer.json file for the fast one
																										self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)
																										__lowercase      :							List[Any]				=     tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f							)
																										self.assertSequenceEqual(snake_case_		,     snake_case_							)

																										# Checks everything loads correctly in the same way
																										__lowercase      :							Dict				=     tokenizer_r.from_pretrained(snake_case_							)
																										__lowercase      :							Union[str, Any]				=     tokenizer_p.from_pretrained(snake_case_							)

																										# Check special tokens are set accordingly on Rust and Python
																										for key in tokenizer_pp.special_tokens_map:
																																	self.assertTrue(hasattr(snake_case_		,     snake_case_							)							)
																																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																										shutil.rmtree(snake_case_							)

																										# Save tokenizer rust, legacy_format=True
																										__lowercase      :							Union[str, Any]				=     tempfile.mkdtemp()

																										__lowercase      :							str				=     tokenizer_r.save_pretrained(snake_case_		,     legacy_format=snake_case_							)
																										__lowercase      :							Optional[Any]				=     tokenizer_p.save_pretrained(snake_case_							)

																										# Checks it save with the same files
																										self.assertSequenceEqual(snake_case_		,     snake_case_							)

																										# Checks everything loads correctly in the same way
																										__lowercase      :							int				=     tokenizer_r.from_pretrained(snake_case_							)
																										__lowercase      :							Any				=     tokenizer_p.from_pretrained(snake_case_							)

																										# Check special tokens are set accordingly on Rust and Python
																										for key in tokenizer_pp.special_tokens_map:
																																	self.assertTrue(hasattr(snake_case_		,     snake_case_							)							)

																										shutil.rmtree(snake_case_							)

																										# Save tokenizer rust, legacy_format=False
																										__lowercase      :							List[Any]				=     tempfile.mkdtemp()

																										__lowercase      :							str				=     tokenizer_r.save_pretrained(snake_case_		,     legacy_format=snake_case_							)
																										__lowercase      :							int				=     tokenizer_p.save_pretrained(snake_case_							)

																										# Checks it saved the tokenizer.json file
																										self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)

																										# Checks everything loads correctly in the same way
																										__lowercase      :							str				=     tokenizer_r.from_pretrained(snake_case_							)
																										__lowercase      :							Tuple				=     tokenizer_p.from_pretrained(snake_case_							)

																										# Check special tokens are set accordingly on Rust and Python
																										for key in tokenizer_pp.special_tokens_map:
																																	self.assertTrue(hasattr(snake_case_		,     snake_case_							)							)

																										shutil.rmtree(snake_case_							)



					@cached_property
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""							)



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Tuple:


												"""simple docstring"""

												with tempfile.NamedTemporaryFile() as f:
																			shutil.copyfile(snake_case_		,     f.name							)
																			__lowercase      :							Optional[Any]				=     XLMRobertaTokenizer(f.name		,     keep_accents=snake_case_							)
																			__lowercase      :							Union[str, Any]				=     pickle.dumps(snake_case_							)
												pickle.loads(snake_case_							)



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


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase      :							str				=     self.get_tokenizer()
												__lowercase      :							Union[str, Any]				=     self.get_rust_tokenizer()

												__lowercase      :							Optional[Any]				=     """I was born in 92000, and this is falsé."""

												__lowercase      :							str				=     tokenizer.tokenize(snake_case_							)
												__lowercase      :							Optional[int]				=     rust_tokenizer.tokenize(snake_case_							)
												self.assertListEqual(snake_case_		,     snake_case_							)

												__lowercase      :							Union[str, Any]				=     tokenizer.encode(snake_case_		,     add_special_tokens=snake_case_							)
												__lowercase      :							str				=     rust_tokenizer.encode(snake_case_		,     add_special_tokens=snake_case_							)
												self.assertListEqual(snake_case_		,     snake_case_							)

												__lowercase      :							List[Any]				=     self.get_rust_tokenizer()
												__lowercase      :							Union[str, Any]				=     tokenizer.encode(snake_case_							)
												__lowercase      :							Optional[int]				=     rust_tokenizer.encode(snake_case_							)
												self.assertListEqual(snake_case_		,     snake_case_							)



					@slow
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							List[str]				=     """Hello World!"""
												__lowercase      :							List[str]				=     [0, 35378, 6661, 38, 2]
												# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
												# xlmr.eval()
												# xlmr.encode(symbols)

												self.assertListEqual(snake_case_		,     self.big_tokenizer.encode(snake_case_							)							)



					@slow
					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     (
												    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
												    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
												)
												__lowercase      :							List[Any]				=     [
												    0,
												    3293,
												    83,
												    10,
												    4552,
												    4989,
												    7986,
												    678,
												    10,
												    5915,
												    111,
												    179459,
												    124850,
												    4,
												    6044,
												    237,
												    12,
												    6,
												    5,
												    6,
												    4,
												    6780,
												    705,
												    15,
												    1388,
												    44,
												    378,
												    10114,
												    711,
												    152,
												    20,
												    6,
												    5,
												    22376,
												    642,
												    1221,
												    15190,
												    34153,
												    450,
												    5608,
												    959,
												    1119,
												    57702,
												    136,
												    186,
												    47,
												    1098,
												    29367,
												    47,
												    # 4426, # What fairseq tokenizes from "<unk>": "_<"
												    # 3678, # What fairseq tokenizes from "<unk>": "unk"
												    # 2740, # What fairseq tokenizes from "<unk>": ">"
												    3,  # What we tokenize from "<unk>": "<unk>"
												    6,  # Residue from the tokenization: an extra sentencepiece underline
												    4,
												    6044,
												    237,
												    6284,
												    50901,
												    528,
												    31,
												    90,
												    34,
												    927,
												    2,
												]
												# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
												# xlmr.eval()
												# xlmr.encode(symbols)

												self.assertListEqual(snake_case_		,     self.big_tokenizer.encode(snake_case_							)							)



					@slow
					def       lowerCAmelCase				(  self				:	Tuple							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     {"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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]], """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, 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], [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]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=snake_case_		,     model_name="""xlm-roberta-base"""		,     revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""		,     )













from torch import nn

class 		lowerCAmelCase     ( nn.Module			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     class_size
												__lowercase      :							int				=     embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												__lowercase      :							str				=     nn.Linear(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     self.mlp(__a							)
												return logits