



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







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




		'''simple docstring'''



		snake_case_			=XLMRobertaTokenizer
		snake_case_			=XLMRobertaTokenizerFast
		snake_case_			=True
		snake_case_			=True



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						super().setUp()

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  '''<pad>'''
						lowerCAmelCase__  :       Union[str, Any]				=  1

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



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       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(__lowerCamelCase				)	,10_02				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size	,10_02				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  XLMRobertaTokenizer(__lowerCamelCase	,keep_accents=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  tokenizer.tokenize('''This is a test'''				)
						self.assertListEqual(__lowerCamelCase	,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowerCamelCase				)	,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]	,)

						lowerCAmelCase__  :       Optional[Any]				=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
						self.assertListEqual(
						    __lowerCamelCase	,[
						        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''',
						        '''.''',
						    ]	,)
						lowerCAmelCase__  :       Any				=  tokenizer.convert_tokens_to_ids(__lowerCamelCase				)
						self.assertListEqual(
						    __lowerCamelCase	,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
						    ]	,)

						lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.convert_ids_to_tokens(__lowerCamelCase				)
						self.assertListEqual(
						    __lowerCamelCase	,[
						        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				)  ->    Any:

						"""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

						lowerCAmelCase__  :       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})"""				):
														lowerCAmelCase__  :       List[str]				=  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)
														lowerCAmelCase__  :       Union[str, Any]				=  self.tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)

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

														lowerCAmelCase__  :       Any				=  tokenizer_r.save_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       List[Any]				=  tokenizer_p.save_pretrained(__lowerCamelCase				)

														# 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				)				)
														lowerCAmelCase__  :       Any				=  tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
														self.assertSequenceEqual(__lowerCamelCase	,__lowerCamelCase				)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__  :       int				=  tokenizer_r.from_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       List[Any]				=  tokenizer_p.from_pretrained(__lowerCamelCase				)

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

														shutil.rmtree(__lowerCamelCase				)

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

														lowerCAmelCase__  :       str				=  tokenizer_r.save_pretrained(__lowerCamelCase	,legacy_format=__lowerCamelCase				)
														lowerCAmelCase__  :       Any				=  tokenizer_p.save_pretrained(__lowerCamelCase				)

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

														# Checks everything loads correctly in the same way
														lowerCAmelCase__  :       List[Any]				=  tokenizer_r.from_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[int]				=  tokenizer_p.from_pretrained(__lowerCamelCase				)

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

														shutil.rmtree(__lowerCamelCase				)

														# Save tokenizer rust, legacy_format=False
														lowerCAmelCase__  :       str				=  tempfile.mkdtemp()

														lowerCAmelCase__  :       Dict				=  tokenizer_r.save_pretrained(__lowerCamelCase	,legacy_format=__lowerCamelCase				)
														lowerCAmelCase__  :       List[str]				=  tokenizer_p.save_pretrained(__lowerCamelCase				)

														# 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
														lowerCAmelCase__  :       Tuple				=  tokenizer_r.from_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       Tuple				=  tokenizer_p.from_pretrained(__lowerCamelCase				)

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

														shutil.rmtree(__lowerCamelCase				)



		@cached_property
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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



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

						"""simple docstring"""






						if not self.test_rust_tokenizer:
										return

						lowerCAmelCase__  :       Optional[Any]				=  self.get_tokenizer()
						lowerCAmelCase__  :       List[str]				=  self.get_rust_tokenizer()

						lowerCAmelCase__  :       Tuple				=  '''I was born in 92000, and this is falsé.'''

						lowerCAmelCase__  :       Tuple				=  tokenizer.tokenize(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  rust_tokenizer.tokenize(__lowerCamelCase				)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

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

						lowerCAmelCase__  :       Tuple				=  self.get_rust_tokenizer()
						lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.encode(__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  rust_tokenizer.encode(__lowerCamelCase				)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  '''Hello World!'''
						lowerCAmelCase__  :       Optional[int]				=  [0, 3_53_78, 66_61, 38, 2]
						# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
						# xlmr.eval()
						# xlmr.encode(symbols)

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



		@slow
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[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'''
						)
						lowerCAmelCase__  :       Optional[Any]				=  [
						    0,
						    32_93,
						    83,
						    10,
						    45_52,
						    49_89,
						    79_86,
						    6_78,
						    10,
						    59_15,
						    1_11,
						    17_94_59,
						    12_48_50,
						    4,
						    60_44,
						    2_37,
						    12,
						    6,
						    5,
						    6,
						    4,
						    67_80,
						    7_05,
						    15,
						    13_88,
						    44,
						    3_78,
						    1_01_14,
						    7_11,
						    1_52,
						    20,
						    6,
						    5,
						    2_23_76,
						    6_42,
						    12_21,
						    1_51_90,
						    3_41_53,
						    4_50,
						    56_08,
						    9_59,
						    11_19,
						    5_77_02,
						    1_36,
						    1_86,
						    47,
						    10_98,
						    2_93_67,
						    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,
						    60_44,
						    2_37,
						    62_84,
						    5_09_01,
						    5_28,
						    31,
						    90,
						    34,
						    9_27,
						    2,
						]
						# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
						# xlmr.eval()
						# xlmr.encode(symbols)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=__lowerCamelCase	,model_name='''xlm-roberta-base'''	,revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''	,)






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

