
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase					:							List[str]   =      get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase   (      UpperCamelCase_		,     unittest.TestCase		):
							__lowercase   	= ReformerTokenizer
							__lowercase   	= ReformerTokenizerFast
							__lowercase   	= True
							__lowercase   	= False
							__lowercase   	= True


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().setUp()

								_snake_case     		=	ReformerTokenizer(_a					,     keep_accents=_a						)
								tokenizer.save_pretrained(self.tmpdirname						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	"""<s>"""
								_snake_case     		=	1

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	list(self.get_tokenizer().get_vocab().keys()						)

								self.assertEqual(vocab_keys[0]					,     '<unk>'						)
								self.assertEqual(vocab_keys[1]					,     '<s>'						)
								self.assertEqual(vocab_keys[-1]					,     'j'						)
								self.assertEqual(len(_a						)					,     10_00						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.assertEqual(self.get_tokenizer().vocab_size					,     10_00						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								if not self.test_rust_tokenizer:
									return

								_snake_case     		=	self.get_tokenizer()
								_snake_case     		=	self.get_rust_tokenizer()

								_snake_case     		=	"""I was born in 92000, and this is falsé."""

								_snake_case     		=	tokenizer.tokenize(_a						)
								_snake_case     		=	rust_tokenizer.tokenize(_a						)
								self.assertListEqual(_a					,     _a						)

								_snake_case     		=	tokenizer.encode(_a					,     add_special_tokens=_a						)
								_snake_case     		=	rust_tokenizer.encode(_a					,     add_special_tokens=_a						)
								self.assertListEqual(_a					,     _a						)

								_snake_case     		=	self.get_rust_tokenizer()
								_snake_case     		=	tokenizer.encode(_a						)
								_snake_case     		=	rust_tokenizer.encode(_a						)
								self.assertListEqual(_a					,     _a						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=15						):





								"""simple docstring"""






								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
										_snake_case     		=	self.rust_tokenizer_class.from_pretrained(_a					,     **_a						)

										# Simple input
										_snake_case     		=	"""This is a simple input"""
										_snake_case     		=	["""This is a simple input 1""", """This is a simple input 2"""]
										_snake_case     		=	("""This is a simple input""", """This is a pair""")
										_snake_case     		=	[
										    ("""This is a simple input 1""", """This is a simple input 2"""),
										    ("""This is a simple pair 1""", """This is a simple pair 2"""),
										]

										# Simple input tests
										self.assertRaises(_a					,     tokenizer_r.encode					,     _a					,     max_length=_a					,     padding='max_length'						)

										# Simple input
										self.assertRaises(_a					,     tokenizer_r.encode_plus					,     _a					,     max_length=_a					,     padding='max_length'						)

										# Simple input
										self.assertRaises(
										    _a					,     tokenizer_r.batch_encode_plus					,     _a					,     max_length=_a					,     padding='max_length'					,     )

										# Pair input
										self.assertRaises(_a					,     tokenizer_r.encode					,     _a					,     max_length=_a					,     padding='max_length'						)

										# Pair input
										self.assertRaises(_a					,     tokenizer_r.encode_plus					,     _a					,     max_length=_a					,     padding='max_length'						)

										# Pair input
										self.assertRaises(
										    _a					,     tokenizer_r.batch_encode_plus					,     _a					,     max_length=_a					,     padding='max_length'					,     )


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	ReformerTokenizer(_a					,     keep_accents=_a						)

								_snake_case     		=	tokenizer.tokenize('This is a test'						)
								self.assertListEqual(_a					,     ['▁This', '▁is', '▁a', '▁t', 'est']						)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(_a						)					,     [2_85, 46, 10, 1_70, 3_82]					,     )

								_snake_case     		=	tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
								self.assertListEqual(
								    _a					,     [
								        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',
								        '.',
								    ]					,     )
								_snake_case     		=	tokenizer.convert_tokens_to_ids(_a						)
								self.assertListEqual(
								    _a					,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]					,     )

								_snake_case     		=	tokenizer.convert_ids_to_tokens(_a						)
								self.assertListEqual(
								    _a					,     [
								        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>',
								        '.',
								    ]					,     )


							@cached_property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'						)


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	"""Hello World!"""
								_snake_case     		=	[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

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


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_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"""
								)
								_snake_case     		=	[
								    1_08,
								    2_65,
								    24,
								    1_11,
								    4,
								    2_58,
								    1_56,
								    35,
								    28,
								    2_75,
								    3,
								    2_59,
								    2_97,
								    2_60,
								    84,
								    4,
								    35,
								    1_10,
								    44,
								    8,
								    2_59,
								    91,
								    2_68,
								    21,
								    11,
								    2_09,
								    2_74,
								    1_09,
								    2_66,
								    2_77,
								    1_17,
								    86,
								    93,
								    3_15,
								    2_58,
								    2_78,
								    2_58,
								    2_77,
								    2_58,
								    0,
								    2_58,
								    2_88,
								    2_58,
								    3_19,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    2_87,
								    2_58,
								    3_15,
								    2_58,
								    2_89,
								    2_58,
								    2_78,
								    99,
								    2_69,
								    2_66,
								    2_62,
								    8,
								    2_59,
								    2_41,
								    4,
								    2_17,
								    2_30,
								    2_68,
								    2_66,
								    55,
								    1_68,
								    1_06,
								    75,
								    1_93,
								    2_66,
								    2_23,
								    27,
								    49,
								    26,
								    2_82,
								    25,
								    2_64,
								    2_99,
								    19,
								    26,
								    0,
								    2_58,
								    2_77,
								    1_17,
								    86,
								    93,
								    1_76,
								    1_83,
								    2_70,
								    11,
								    2_62,
								    42,
								    61,
								    2_65,
								]

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


							@require_torch
							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								_snake_case     		=	list(self.big_tokenizer.get_vocab().keys()						)[:10]
								_snake_case     		=	""" """.join(_a						)
								_snake_case     		=	self.big_tokenizer.encode_plus(_a					,     return_tensors='pt'						)
								_snake_case     		=	self.big_tokenizer.batch_encode_plus([sequence, sequence]					,     return_tensors='pt'						)

								_snake_case     		=	ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								_snake_case     		=	encoded_sequence["""input_ids"""].shape
								_snake_case     		=	ReformerModel(_a						)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
									model(**_a						)
									model(**_a						)




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	{"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """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]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								_snake_case     		=	[
								    """This is a very simple sentence.""",
								    """The quick brown fox jumps over the lazy dog.""",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=_a					,     model_name='google/reformer-crime-and-punishment'					,     revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'					,     padding=_a					,     sequences=_a					,     )



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A=5			) ->			Dict:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'			) == 1
	_snake_case     		=	torch.tensor(tokenizer.encode(__A    ,       add_special_tokens=__A			)			).unsqueeze(0			)  # Batch size 1
	_snake_case     		=	model(__A			)[0]  # The last hidden-state is the first element of the output tuple
	_snake_case     		=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	_snake_case     		=	logits[0, masked_index, :]
	_snake_case     		=	logits.softmax(dim=0			)
	_snake_case		,				_snake_case     		=	prob.topk(k=__A    ,       dim=0			)
	_snake_case     		=	' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()			) for i in range(len(__A			)			)]			)
	_snake_case     		=	tokenizer.mask_token
	_snake_case     		=	[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '			)			):
		_snake_case     		=	predicted_token_bpe.replace('\u2581'    ,       ' '			)
		if " {0}".format(__A			) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(__A			)    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__A    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
	return topk_filled_outputs


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





print(fill_mask(masked_input, model, tokenizer, topk=3))

