
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_  :	List[Any] 				=					get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class     _UpperCamelCase			(   _UpperCamelCase       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	XLMRobertaTokenizer
			__UpperCamelCase :  Optional[int]    =	XLMRobertaTokenizerFast
			__UpperCamelCase :  Union[str, Any]    =	True
			__UpperCamelCase :  int    =	True

			def  lowerCAmelCase__		(    self	:    int ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							UpperCamelCase_:							Any		=  XLMRobertaTokenizer(_UpperCAmelCase  ,		keep_accents=_UpperCAmelCase )
							tokenizer.save_pretrained(self.tmpdirname )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Any		=  '<pad>'
							UpperCamelCase_:							Optional[int]		=  1

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

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[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]  ,		"""<mask>""" )
							self.assertEqual(len(_UpperCAmelCase )  ,		1002 )

			def  lowerCAmelCase__		(    self	:    Tuple ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,		1002 )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							int		=  XLMRobertaTokenizer(_UpperCAmelCase  ,		keep_accents=_UpperCAmelCase )

							UpperCamelCase_:							Any		=  tokenizer.tokenize("""This is a test""" )
							self.assertListEqual(_UpperCAmelCase  ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

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

							UpperCamelCase_:							Dict		=  tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
							self.assertListEqual(
							    _UpperCAmelCase  ,		[
							        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""",
							        """.""",
							    ]  ,		)
							UpperCamelCase_:							Optional[Any]		=  tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
							self.assertListEqual(
							    _UpperCAmelCase  ,		[
							        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 ^
							    ]  ,		)

							UpperCamelCase_:							Any		=  tokenizer.convert_ids_to_tokens(_UpperCAmelCase )
							self.assertListEqual(
							    _UpperCAmelCase  ,		[
							        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	:    Dict ):
							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

							UpperCamelCase_:							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})''' ):
															UpperCamelCase_:							int		=  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase  ,		**_UpperCAmelCase )
															UpperCamelCase_:							Union[str, Any]		=  self.tokenizer_class.from_pretrained(_UpperCAmelCase  ,		**_UpperCAmelCase )

															UpperCamelCase_:							Tuple		=  tempfile.mkdtemp()

															UpperCamelCase_:							Optional[int]		=  tokenizer_r.save_pretrained(_UpperCAmelCase )
															UpperCamelCase_:							Dict		=  tokenizer_p.save_pretrained(_UpperCAmelCase )

															# 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 ) )
															UpperCamelCase_:							Optional[int]		=  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
															self.assertSequenceEqual(_UpperCAmelCase  ,		_UpperCAmelCase )

															# Checks everything loads correctly in the same way
															UpperCamelCase_:							Any		=  tokenizer_r.from_pretrained(_UpperCAmelCase )
															UpperCamelCase_:							Optional[Any]		=  tokenizer_p.from_pretrained(_UpperCAmelCase )

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

															shutil.rmtree(_UpperCAmelCase )

															# Save tokenizer rust, legacy_format=True
															UpperCamelCase_:							Any		=  tempfile.mkdtemp()

															UpperCamelCase_:							Dict		=  tokenizer_r.save_pretrained(_UpperCAmelCase  ,		legacy_format=_UpperCAmelCase )
															UpperCamelCase_:							Optional[Any]		=  tokenizer_p.save_pretrained(_UpperCAmelCase )

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

															# Checks everything loads correctly in the same way
															UpperCamelCase_:							str		=  tokenizer_r.from_pretrained(_UpperCAmelCase )
															UpperCamelCase_:							str		=  tokenizer_p.from_pretrained(_UpperCAmelCase )

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

															shutil.rmtree(_UpperCAmelCase )

															# Save tokenizer rust, legacy_format=False
															UpperCamelCase_:							Tuple		=  tempfile.mkdtemp()

															UpperCamelCase_:							str		=  tokenizer_r.save_pretrained(_UpperCAmelCase  ,		legacy_format=_UpperCAmelCase )
															UpperCamelCase_:							Tuple		=  tokenizer_p.save_pretrained(_UpperCAmelCase )

															# 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
															UpperCamelCase_:							List[str]		=  tokenizer_r.from_pretrained(_UpperCAmelCase )
															UpperCamelCase_:							Optional[int]		=  tokenizer_p.from_pretrained(_UpperCAmelCase )

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

															shutil.rmtree(_UpperCAmelCase )

			@cached_property
			def  lowerCAmelCase__		(    self	:    int ):
							return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )

			def  lowerCAmelCase__		(    self	:    Dict ):
							with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(_UpperCAmelCase  ,		f.name )
											UpperCamelCase_:							Any		=  XLMRobertaTokenizer(f.name  ,		keep_accents=_UpperCAmelCase )
											UpperCamelCase_:							Tuple		=  pickle.dumps(_UpperCAmelCase )
							pickle.loads(_UpperCAmelCase )

			def  lowerCAmelCase__		(    self	:    Any ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Tuple		=  self.get_tokenizer()
							UpperCamelCase_:							Any		=  self.get_rust_tokenizer()

							UpperCamelCase_:							List[Any]		=  'I was born in 92000, and this is falsé.'

							UpperCamelCase_:							Dict		=  tokenizer.tokenize(_UpperCAmelCase )
							UpperCamelCase_:							Union[str, Any]		=  rust_tokenizer.tokenize(_UpperCAmelCase )
							self.assertListEqual(_UpperCAmelCase  ,		_UpperCAmelCase )

							UpperCamelCase_:							Any		=  tokenizer.encode(_UpperCAmelCase  ,		add_special_tokens=_UpperCAmelCase )
							UpperCamelCase_:							Dict		=  rust_tokenizer.encode(_UpperCAmelCase  ,		add_special_tokens=_UpperCAmelCase )
							self.assertListEqual(_UpperCAmelCase  ,		_UpperCAmelCase )

							UpperCamelCase_:							Optional[int]		=  self.get_rust_tokenizer()
							UpperCamelCase_:							str		=  tokenizer.encode(_UpperCAmelCase )
							UpperCamelCase_:							Dict		=  rust_tokenizer.encode(_UpperCAmelCase )
							self.assertListEqual(_UpperCAmelCase  ,		_UpperCAmelCase )

			@slow
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							str		=  'Hello World!'
							UpperCamelCase_:							Optional[Any]		=  [0, 3_5378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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

			@slow
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							Union[str, 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'
							)
							UpperCamelCase_:							str		=  [
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    17_9459,
							    12_4850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    1_0114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    2_2376,
							    642,
							    1221,
							    1_5190,
							    3_4153,
							    450,
							    5608,
							    959,
							    1119,
							    5_7702,
							    136,
							    186,
							    47,
							    1098,
							    2_9367,
							    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,
							    5_0901,
							    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(_UpperCAmelCase  ,		self.big_tokenizer.encode(_UpperCAmelCase ) )




			@slow
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							# fmt: off
							UpperCamelCase_:							Tuple		=  {'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, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 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, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 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], [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]], '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=_UpperCAmelCase  ,		model_name="""xlm-roberta-base"""  ,		revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""  ,		)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Optional[int] 				=					{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	"""ctrl"""
			__UpperCamelCase :  Dict    =	["""past_key_values"""]
			__UpperCamelCase :  List[str]    =	{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}

			def __init__(    self	:    Dict  ,		snake_case_	:    Any=24_6534  ,		snake_case_	:    Dict=256  ,		snake_case_	:    str=1280  ,		snake_case_	:    Optional[int]=8192  ,		snake_case_	:    Union[str, Any]=48  ,		snake_case_	:    Any=16  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    Any=0.1  ,		snake_case_	:    Any=1e-6  ,		snake_case_	:    Optional[Any]=0.02  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  vocab_size
							UpperCamelCase_:							Union[str, Any]		=  n_positions
							UpperCamelCase_:							Optional[int]		=  n_embd
							UpperCamelCase_:							int		=  n_layer
							UpperCamelCase_:							str		=  n_head
							UpperCamelCase_:							Optional[int]		=  dff
							UpperCamelCase_:							Optional[Any]		=  resid_pdrop
							UpperCamelCase_:							Union[str, Any]		=  embd_pdrop
							UpperCamelCase_:							List[str]		=  layer_norm_epsilon
							UpperCamelCase_:							Optional[Any]		=  initializer_range

							UpperCamelCase_:							Optional[Any]		=  use_cache

							super().__init__(**snake_case_ )






