

'''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


UpperCamelCase__ :			Any												=				get_tests_dir('''fixtures/test_sentencepiece.model''')






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


			'''simple docstring'''

			_A   :	Any    					=	XLMRobertaTokenizer
			_A   :	Optional[Any]    					=	XLMRobertaTokenizerFast
			_A   :	int    					=	True
			_A   :	Optional[int]    					=	True

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					super().setUp()

					# We have a SentencePiece fixture for testing
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	XLMRobertaTokenizer(_UpperCAmelCase							,  keep_accents=_UpperCAmelCase		)
					tokenizer.save_pretrained(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	'''<pad>'''
					__SCREAMING_SNAKE_CASE :	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 UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	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		)							,  1_0_0_2		)

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_0_2		)

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	XLMRobertaTokenizer(_UpperCAmelCase							,  keep_accents=_UpperCAmelCase		)

					__SCREAMING_SNAKE_CASE :	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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,  )

					__SCREAMING_SNAKE_CASE :	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""",
					        """.""",
					    ]							,  )
					__SCREAMING_SNAKE_CASE :	Dict					 =	tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)
					self.assertListEqual(
					    _UpperCAmelCase							,  [
					        value + tokenizer.fairseq_offset
					        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]							,  )

					__SCREAMING_SNAKE_CASE :	Dict					 =	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 UpperCamelCase__      (							self  :				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

					__SCREAMING_SNAKE_CASE :	Tuple					 =	(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 :	Any					 =	self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase							,  **_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.tokenizer_class.from_pretrained(_UpperCAmelCase							,  **_UpperCAmelCase		)

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

									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	tokenizer_r.save_pretrained(_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	Tuple					 =	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		)		)
									__SCREAMING_SNAKE_CASE :	Tuple					 =	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
									__SCREAMING_SNAKE_CASE :	Tuple					 =	tokenizer_r.from_pretrained(_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	Union[str, 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
									__SCREAMING_SNAKE_CASE :	Tuple					 =	tempfile.mkdtemp()

									__SCREAMING_SNAKE_CASE :	Dict					 =	tokenizer_r.save_pretrained(_UpperCAmelCase							,  legacy_format=_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	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
									__SCREAMING_SNAKE_CASE :	int					 =	tokenizer_r.from_pretrained(_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	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		)

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

									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tokenizer_r.save_pretrained(_UpperCAmelCase							,  legacy_format=_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	int					 =	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
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	tokenizer_r.from_pretrained(_UpperCAmelCase		)
									__SCREAMING_SNAKE_CASE :	List[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		)

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(_UpperCAmelCase							,  f.name		)
							__SCREAMING_SNAKE_CASE :	int					 =	XLMRobertaTokenizer(f.name							,  keep_accents=_UpperCAmelCase		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	pickle.dumps(_UpperCAmelCase		)
					pickle.loads(_UpperCAmelCase		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					if not self.test_rust_tokenizer:
							return

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

					__SCREAMING_SNAKE_CASE :	int					 =	'''I was born in 92000, and this is falsé.'''

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

					__SCREAMING_SNAKE_CASE :	str					 =	tokenizer.encode(_UpperCAmelCase							,  add_special_tokens=_UpperCAmelCase		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	rust_tokenizer.encode(_UpperCAmelCase							,  add_special_tokens=_UpperCAmelCase		)
					self.assertListEqual(_UpperCAmelCase							,  _UpperCAmelCase		)

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

			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	'''Hello World!'''
					__SCREAMING_SNAKE_CASE :	str					 =	[0, 3_5_3_7_8, 6_6_6_1, 3_8, 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 UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(
					    '''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 :	Optional[Any]					 =	[
					    0,
					    3_2_9_3,
					    8_3,
					    1_0,
					    4_5_5_2,
					    4_9_8_9,
					    7_9_8_6,
					    6_7_8,
					    1_0,
					    5_9_1_5,
					    1_1_1,
					    1_7_9_4_5_9,
					    1_2_4_8_5_0,
					    4,
					    6_0_4_4,
					    2_3_7,
					    1_2,
					    6,
					    5,
					    6,
					    4,
					    6_7_8_0,
					    7_0_5,
					    1_5,
					    1_3_8_8,
					    4_4,
					    3_7_8,
					    1_0_1_1_4,
					    7_1_1,
					    1_5_2,
					    2_0,
					    6,
					    5,
					    2_2_3_7_6,
					    6_4_2,
					    1_2_2_1,
					    1_5_1_9_0,
					    3_4_1_5_3,
					    4_5_0,
					    5_6_0_8,
					    9_5_9,
					    1_1_1_9,
					    5_7_7_0_2,
					    1_3_6,
					    1_8_6,
					    4_7,
					    1_0_9_8,
					    2_9_3_6_7,
					    4_7,
					    # 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,
					    6_0_4_4,
					    2_3_7,
					    6_2_8_4,
					    5_0_9_0_1,
					    5_2_8,
					    3_1,
					    9_0,
					    3_4,
					    9_2_7,
					    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 UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	{'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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'''




# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCamelCase__ :			Tuple												=				'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
						import torch

						print('''Torch version:''', torch.__version__)
						print('''Cuda available:''', torch.cuda.is_available())
						print('''Cuda version:''', torch.version.cuda)
						print('''CuDNN version:''', torch.backends.cudnn.version())
						print('''Number of GPUs available:''', torch.cuda.device_count())
						print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
						print('''Torch version:''', None)

try:
						import deepspeed

						print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
						print('''DeepSpeed version:''', None)

try:
						import tensorflow as tf

						print('''TensorFlow version:''', tf.__version__)
						print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
						print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
						print('''TensorFlow version:''', None)