



'''simple docstring'''

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						      =       get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
@require_tokenizers
class      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   List[str]           = XLMRobertaTokenizer
							lowerCAmelCase	:   Optional[int]           = XLMRobertaTokenizerFast
							lowerCAmelCase	:   Union[str, Any]           = True
							lowerCAmelCase	:   int           = True







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

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







							def 							__lowercase      (							self :	Any		):
									_a					:				Any				=							'<pad>'
									_a					:				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 							__lowercase      (							self :	List[str]		):
									_a					:				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 							__lowercase      (							self :	Tuple		):
									self.assertEqual(self.get_tokenizer().vocab_size     ,1002		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				int				=							XLMRobertaTokenizer(_UpperCAmelCase     ,keep_accents=_UpperCAmelCase		)

									_a					:				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]]     ,)

									_a					:				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',
									        '.',
									    ]     ,)
									_a					:				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 ^
									    ]     ,)

									_a					:				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 							__lowercase      (							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

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

													_a					:				Tuple				=							tempfile.mkdtemp()

													_a					:				Optional[int]				=							tokenizer_r.save_pretrained(_UpperCAmelCase		)
													_a					:				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		)		)
													_a					:				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
													_a					:				Any				=							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a					:				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
													_a					:				Any				=							tempfile.mkdtemp()

													_a					:				Dict				=							tokenizer_r.save_pretrained(_UpperCAmelCase     ,legacy_format=_UpperCAmelCase		)
													_a					:				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
													_a					:				str				=							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a					:				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
													_a					:				Tuple				=							tempfile.mkdtemp()

													_a					:				str				=							tokenizer_r.save_pretrained(_UpperCAmelCase     ,legacy_format=_UpperCAmelCase		)
													_a					:				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
													_a					:				List[str]				=							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a					:				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 							__lowercase      (							self :	int		):
									return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'		)







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







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

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

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

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

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

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







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				str				=							'Hello World!'
									_a					:				Optional[Any]				=							[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(_UpperCAmelCase     ,self.big_tokenizer.encode(_UpperCAmelCase		)		)







							@slow
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				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'
									)
									_a					:				str				=							[
									    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(_UpperCAmelCase     ,self.big_tokenizer.encode(_UpperCAmelCase		)		)







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									# fmt: off
									_a					:				Tuple				=							{'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=_UpperCAmelCase     ,model_name='xlm-roberta-base'     ,revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'     ,)









'''simple docstring'''

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer

@require_tf
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]		):
									self.assertEqual(len(_UpperCAmelCase		)     ,len(_UpperCAmelCase		)		)
									for a, b in zip(_UpperCAmelCase     ,_UpperCAmelCase		):
											self.assertAlmostEqual(_UpperCAmelCase     ,_UpperCAmelCase     ,delta=_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							GradientAccumulator()
									accumulator([tf.constant([1.0, 2.0]		)]		)
									accumulator([tf.constant([-2.0, 1.0]		)]		)
									accumulator([tf.constant([-1.0, 2.0]		)]		)
									with self.assertRaises(_UpperCAmelCase		):
											accumulator([tf.constant([1.0, 1.0]		), tf.constant([2.0, 2.0]		)]		)
									self.assertEqual(accumulator.step     ,3		)
									self.assertEqual(len(accumulator.gradients		)     ,1		)
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,[-2.0, 5.0]     ,tol=1E-2		)
									accumulator.reset()
									self.assertEqual(accumulator.step     ,0		)
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,[0.0, 0.0]     ,tol=1E-2		)







							def 							__lowercase      (							self :	Any		):
									_a					:				int				=							None
									ops.enable_eager_execution_internal()
									_a					:				Optional[int]				=							tf.config.list_physical_devices('CPU'		)
									if len(_UpperCAmelCase		) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0]     ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
									_a					:				Tuple				=							tf.config.list_logical_devices(device_type='CPU'		)
									_a					:				List[str]				=							tf.distribute.MirroredStrategy(devices=devices[:2]		)

									with strategy.scope():
											_a					:				Tuple				=							GradientAccumulator()
											_a					:				List[Any]				=							tf.Variable([4.0, 3.0]		)
											_a	, _a					:				Dict				=							create_optimizer(5E-5     ,10     ,5		)
											_a					:				Tuple				=							tf.Variable([0.0, 0.0]     ,trainable=_UpperCAmelCase		)

									def accumulate_on_replica(_UpperCAmelCase :	str		):
											accumulator([gradient]		)

									def apply_on_replica():
											optimizer.apply_gradients(list(zip(accumulator.gradients     ,[variable]		)		)		)

									@tf.function
									def accumulate(_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]		):
											with strategy.scope():
													_a					:				Union[str, Any]				=							strategy.experimental_local_results(_UpperCAmelCase		)
													local_variables[0].assign(_UpperCAmelCase		)
													local_variables[1].assign(_UpperCAmelCase		)
													strategy.run(_UpperCAmelCase     ,args=(gradient_placeholder,)		)

									@tf.function
									def apply_grad():
											with strategy.scope():
													strategy.run(_UpperCAmelCase		)

									def _check_local_values(_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]		):
											_a					:				List[Any]				=							strategy.experimental_local_results(accumulator._gradients[0]		)
											self.assertListAlmostEqual(values[0].value()     ,_UpperCAmelCase     ,tol=1E-2		)
											self.assertListAlmostEqual(values[1].value()     ,_UpperCAmelCase     ,tol=1E-2		)

									accumulate([1.0, 2.0]     ,[-1.0, 1.0]		)
									accumulate([3.0, -1.0]     ,[-1.0, -1.0]		)
									accumulate([-2.0, 2.0]     ,[3.0, -2.0]		)
									self.assertEqual(accumulator.step     ,3		)
									_check_local_values([2.0, 3.0]     ,[1.0, -2.0]		)
									apply_grad()
									self.assertListAlmostEqual(variable.value()     ,[4.0, 3.0]     ,tol=1E-2		)
									accumulator.reset()
									self.assertEqual(accumulator.step     ,0		)
									_check_local_values([0.0, 0.0]     ,[0.0, 0.0]		)




