
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


__snake_case				      =    get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class 		lowercase__    (		lowerCAmelCase__	,			unittest.TestCase    ):
	A__       :  List[str]										=XLMRobertaTokenizer
	A__       :  List[Any]										=XLMRobertaTokenizerFast
	A__       :  Dict										=True
	A__       :  Optional[Any]										=True
	def 	A_							(     self				:  Tuple				):
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE__						=					XLMRobertaTokenizer(snake_case_			,  keep_accents=snake_case_				)
						tokenizer.save_pretrained(self.tmpdirname				)
	def 	A_							(     self				:  List[str]				):
						SCREAMING_SNAKE_CASE__						=					'<pad>'
						SCREAMING_SNAKE_CASE__						=					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 	A_							(     self				:  Union[str, Any]				):
						SCREAMING_SNAKE_CASE__						=					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 	A_							(     self				:  Any				):
						self.assertEqual(self.get_tokenizer().vocab_size			,  1002				)
	def 	A_							(     self				:  str				):
						SCREAMING_SNAKE_CASE__						=					XLMRobertaTokenizer(snake_case_			,  keep_accents=snake_case_				)

						SCREAMING_SNAKE_CASE__						=					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]]			,  )

						SCREAMING_SNAKE_CASE__						=					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',
						        '.',
						    ]			,  )
						SCREAMING_SNAKE_CASE__						=					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 ^
						    ]			,  )

						SCREAMING_SNAKE_CASE__						=					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 	A_							(     self				:  int				):

						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

						SCREAMING_SNAKE_CASE__						=					(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})'				):
																SCREAMING_SNAKE_CASE__						=					self.rust_tokenizer_class.from_pretrained(snake_case_			,  **snake_case_				)
																SCREAMING_SNAKE_CASE__						=					self.tokenizer_class.from_pretrained(snake_case_			,  **snake_case_				)

																SCREAMING_SNAKE_CASE__						=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__						=					tokenizer_r.save_pretrained(snake_case_				)
																SCREAMING_SNAKE_CASE__						=					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				)				)
																SCREAMING_SNAKE_CASE__						=					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
																SCREAMING_SNAKE_CASE__						=					tokenizer_r.from_pretrained(snake_case_				)
																SCREAMING_SNAKE_CASE__						=					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
																SCREAMING_SNAKE_CASE__						=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__						=					tokenizer_r.save_pretrained(snake_case_			,  legacy_format=snake_case_				)
																SCREAMING_SNAKE_CASE__						=					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
																SCREAMING_SNAKE_CASE__						=					tokenizer_r.from_pretrained(snake_case_				)
																SCREAMING_SNAKE_CASE__						=					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
																SCREAMING_SNAKE_CASE__						=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__						=					tokenizer_r.save_pretrained(snake_case_			,  legacy_format=snake_case_				)
																SCREAMING_SNAKE_CASE__						=					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
																SCREAMING_SNAKE_CASE__						=					tokenizer_r.from_pretrained(snake_case_				)
																SCREAMING_SNAKE_CASE__						=					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 	A_							(     self				:  Any				):
						return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'				)
	def 	A_							(     self				:  Any				):

						with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(snake_case_			,  f.name				)
											SCREAMING_SNAKE_CASE__						=					XLMRobertaTokenizer(f.name			,  keep_accents=snake_case_				)
											SCREAMING_SNAKE_CASE__						=					pickle.dumps(snake_case_				)
						pickle.loads(snake_case_				)
	def 	A_							(     self				:  str				):

						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__						=					self.get_tokenizer()
						SCREAMING_SNAKE_CASE__						=					self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__						=					'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__						=					tokenizer.tokenize(snake_case_				)
						SCREAMING_SNAKE_CASE__						=					rust_tokenizer.tokenize(snake_case_				)
						self.assertListEqual(snake_case_			,  snake_case_				)

						SCREAMING_SNAKE_CASE__						=					tokenizer.encode(snake_case_			,  add_special_tokens=snake_case_				)
						SCREAMING_SNAKE_CASE__						=					rust_tokenizer.encode(snake_case_			,  add_special_tokens=snake_case_				)
						self.assertListEqual(snake_case_			,  snake_case_				)

						SCREAMING_SNAKE_CASE__						=					self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__						=					tokenizer.encode(snake_case_				)
						SCREAMING_SNAKE_CASE__						=					rust_tokenizer.encode(snake_case_				)
						self.assertListEqual(snake_case_			,  snake_case_				)
	@slow
	def 	A_							(     self				:  Any				):
						SCREAMING_SNAKE_CASE__						=					'Hello World!'
						SCREAMING_SNAKE_CASE__						=					[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 	A_							(     self				:  int				):
						SCREAMING_SNAKE_CASE__						=					(
						    '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'
						)
						SCREAMING_SNAKE_CASE__						=					[
						    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 	A_							(     self				:  Optional[int]				):
						SCREAMING_SNAKE_CASE__						=					{'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 typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


