



'''simple docstring'''



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=dataavec_model.args.encoder_embed_dim   ,				num_hidden_layers=dataavec_model.args.encoder_layers   ,				num_attention_heads=dataavec_model.args.encoder_attention_heads   ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    torch.allclose(UpperCamelCase__   ,				UpperCamelCase__   ,				atol=1e-3			)
				print('''Do both models output the same tensors?'''   ,				'''🔥''' if success else '''💩'''			)
				if not success:
								raise Exception('''Something went wRoNg'''			)

				pathlib.Path(UpperCamelCase__			).mkdir(parents=UpperCamelCase__   ,				exist_ok=UpperCamelCase__			)
				print(F'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--classification_head", action="store_true", help="Whether to convert a final classification head."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)








import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class      __snake_case				(   _UpperCAmelCase	,      unittest.TestCase):
					SCREAMING_SNAKE_CASE_		:       str								=			ElectraTokenizer
					SCREAMING_SNAKE_CASE_		:       Any								=			ElectraTokenizerFast
					SCREAMING_SNAKE_CASE_		:       Tuple								=			True
					SCREAMING_SNAKE_CASE_		:       str								=			True
					SCREAMING_SNAKE_CASE_		:       str								=			filter_non_english
					def 	a			(						self       :						int  )							->					Optional[int]:



											'''simple docstring'''





											super().setUp()

											__magic_name__					=      [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    'want',
											    '##want',
											    '##ed',
											    'wa',
											    'un',
											    'runn',
											    '##ing',
											    ',',
											    'low',
											    'lowest',
											]
											__magic_name__					=      os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['vocab_file']  )
											with open(self.vocab_file	,      'w'	,      encoding='utf-8'  ) as vocab_writer:
																	vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]  )  )
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						Dict  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__					=      'UNwant\u00E9d,running'
											__magic_name__					=      'unwanted, running'
											return input_text, output_text
					def 	a			(						self       :						Union[str, Any]  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      self.tokenizer_class(self.vocab_file  )

											__magic_name__					=      tokenizer.tokenize('UNwant\u00E9d,running'  )
											self.assertListEqual(_lowerCAmelCase	,      ['un', '##want', '##ed', ',', 'runn', '##ing']  )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase  )	,      [9, 6, 7, 1_2, 1_0, 1_1]  )
					def 	a			(						self       :						Optional[int]  )							->					Dict:



											'''simple docstring'''





											if not self.test_rust_tokenizer:
																	return

											__magic_name__					=      self.get_tokenizer()
											__magic_name__					=      self.get_rust_tokenizer()

											__magic_name__					=      'UNwant\u00E9d,running'

											__magic_name__					=      tokenizer.tokenize(_lowerCAmelCase  )
											__magic_name__					=      rust_tokenizer.tokenize(_lowerCAmelCase  )
											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

											__magic_name__					=      tokenizer.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )
											__magic_name__					=      rust_tokenizer.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )
											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

											__magic_name__					=      self.get_rust_tokenizer()
											__magic_name__					=      tokenizer.encode(_lowerCAmelCase  )
											__magic_name__					=      rust_tokenizer.encode(_lowerCAmelCase  )
											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

											# With lower casing
											__magic_name__					=      self.get_tokenizer(do_lower_case=_lowerCAmelCase  )
											__magic_name__					=      self.get_rust_tokenizer(do_lower_case=_lowerCAmelCase  )

											__magic_name__					=      'UNwant\u00E9d,running'

											__magic_name__					=      tokenizer.tokenize(_lowerCAmelCase  )
											__magic_name__					=      rust_tokenizer.tokenize(_lowerCAmelCase  )
											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

											__magic_name__					=      tokenizer.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )
											__magic_name__					=      rust_tokenizer.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )
											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

											__magic_name__					=      self.get_rust_tokenizer()
											__magic_name__					=      tokenizer.encode(_lowerCAmelCase  )
											__magic_name__					=      rust_tokenizer.encode(_lowerCAmelCase  )
											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )
					def 	a			(						self       :						str  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'  )	,      ['ah', '\u535A', '\u63A8', 'zz']  )
					def 	a			(						self       :						Optional[int]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '  )	,      ['hello', '!', 'how', 'are', 'you', '?']  )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'  )	,      ['hello']  )
					def 	a			(						self       :						int  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase	,      strip_accents=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '  )	,      ['hällo', '!', 'how', 'are', 'you', '?']  )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'  )	,      ['h\u00E9llo']  )
					def 	a			(						self       :						Any  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase	,      strip_accents=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '  )	,      ['hallo', '!', 'how', 'are', 'you', '?']  )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'  )	,      ['hello']  )
					def 	a			(						self       :						List[str]  )							->					str:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '  )	,      ['hallo', '!', 'how', 'are', 'you', '?']  )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'  )	,      ['hello']  )
					def 	a			(						self       :						Union[str, Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '  )	,      ['HeLLo', '!', 'how', 'Are', 'yoU', '?']  )
					def 	a			(						self       :						int  )							->					int:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase	,      strip_accents=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '  )	,      ['HäLLo', '!', 'how', 'Are', 'yoU', '?']  )
					def 	a			(						self       :						Dict  )							->					int:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase	,      strip_accents=_lowerCAmelCase  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '  )	,      ['HaLLo', '!', 'how', 'Are', 'yoU', '?']  )
					def 	a			(						self       :						Tuple  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      BasicTokenizer(do_lower_case=_lowerCAmelCase	,      never_split=['[UNK]']  )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'  )	,      ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']  )
					def 	a			(						self       :						Dict  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

											__magic_name__					=      {}
											for i, token in enumerate(_lowerCAmelCase  ):
																	__magic_name__					=      i
											__magic_name__					=      WordpieceTokenizer(vocab=_lowerCAmelCase	,      unk_token='[UNK]'  )

											self.assertListEqual(tokenizer.tokenize(''  )	,      []  )

											self.assertListEqual(tokenizer.tokenize('unwanted running'  )	,      ['un', '##want', '##ed', 'runn', '##ing']  )

											self.assertListEqual(tokenizer.tokenize('unwantedX running'  )	,      ['[UNK]', 'runn', '##ing']  )
					def 	a			(						self       :						List[str]  )							->					Union[str, Any]:



											'''simple docstring'''





											self.assertTrue(_is_whitespace(' '  )  )
											self.assertTrue(_is_whitespace('\t'  )  )
											self.assertTrue(_is_whitespace('\r'  )  )
											self.assertTrue(_is_whitespace('\n'  )  )
											self.assertTrue(_is_whitespace('\u00A0'  )  )

											self.assertFalse(_is_whitespace('A'  )  )
											self.assertFalse(_is_whitespace('-'  )  )
					def 	a			(						self       :						Optional[int]  )							->					Dict:



											'''simple docstring'''





											self.assertTrue(_is_control('\u0005'  )  )

											self.assertFalse(_is_control('A'  )  )
											self.assertFalse(_is_control(' '  )  )
											self.assertFalse(_is_control('\t'  )  )
											self.assertFalse(_is_control('\r'  )  )
					def 	a			(						self       :						List[Any]  )							->					List[str]:



											'''simple docstring'''





											self.assertTrue(_is_punctuation('-'  )  )
											self.assertTrue(_is_punctuation('$'  )  )
											self.assertTrue(_is_punctuation('`'  )  )
											self.assertTrue(_is_punctuation('.'  )  )

											self.assertFalse(_is_punctuation('A'  )  )
											self.assertFalse(_is_punctuation(' '  )  )
					def 	a			(						self       :						Optional[Any]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      self.get_tokenizer()
											__magic_name__					=      self.get_rust_tokenizer()

											# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
											self.assertListEqual([tokenizer.tokenize(_lowerCAmelCase  ) for t in ['Test', '\xad', 'test']]	,      [['[UNK]'], [], ['[UNK]']]  )

											self.assertListEqual(
											    [rust_tokenizer.tokenize(_lowerCAmelCase  ) for t in ['Test', '\xad', 'test']]	,      [['[UNK]'], [], ['[UNK]']]  )
					@slow
					def 	a			(						self       :						str  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.tokenizer_class.from_pretrained('google/electra-base-discriminator'  )

											__magic_name__					=      tokenizer.encode('sequence builders'	,      add_special_tokens=_lowerCAmelCase  )
											__magic_name__					=      tokenizer.encode('multi-sequence build'	,      add_special_tokens=_lowerCAmelCase  )

											__magic_name__					=      tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase  )
											__magic_name__					=      tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	,      _lowerCAmelCase  )

											assert encoded_sentence == [1_0_1] + text + [1_0_2]
											assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
					def 	a			(						self       :						int  )							->					Union[str, Any]:



											'''simple docstring'''





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

																							__magic_name__					=      F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
																							__magic_name__					=      tokenizer_r.encode_plus(
																							    _lowerCAmelCase	,      return_attention_mask=_lowerCAmelCase	,      return_token_type_ids=_lowerCAmelCase	,      return_offsets_mapping=_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase	,      )

																							__magic_name__					=      tokenizer_r.do_lower_case if hasattr(_lowerCAmelCase	,      'do_lower_case'  ) else False
																							__magic_name__					=      (
																							    [
																							        ((0, 0), tokenizer_r.cls_token),
																							        ((0, 1), 'A'),
																							        ((1, 2), ','),
																							        ((3, 5), 'na'),
																							        ((5, 6), '##ï'),
																							        ((6, 8), '##ve'),
																							        ((9, 1_5), tokenizer_r.mask_token),
																							        ((1_6, 2_1), 'Allen'),
																							        ((2_1, 2_3), '##NL'),
																							        ((2_3, 2_4), '##P'),
																							        ((2_5, 3_3), 'sentence'),
																							        ((3_3, 3_4), '.'),
																							        ((0, 0), tokenizer_r.sep_token),
																							    ]
																							    if not do_lower_case
																							    else [
																							        ((0, 0), tokenizer_r.cls_token),
																							        ((0, 1), 'a'),
																							        ((1, 2), ','),
																							        ((3, 8), 'naive'),
																							        ((9, 1_5), tokenizer_r.mask_token),
																							        ((1_6, 2_1), 'allen'),
																							        ((2_1, 2_3), '##nl'),
																							        ((2_3, 2_4), '##p'),
																							        ((2_5, 3_3), 'sentence'),
																							        ((3_3, 3_4), '.'),
																							        ((0, 0), tokenizer_r.sep_token),
																							    ]
																							)

																							self.assertEqual(
																							    [e[1] for e in expected_results]	,      tokenizer_r.convert_ids_to_tokens(tokens['input_ids']  )  )
																							self.assertEqual([e[0] for e in expected_results]	,      tokens['offset_mapping']  )







					def 	a			(						self       :						Any  )							->					int:



											'''simple docstring'''





											__magic_name__					=      ['的', '人', '有']
											__magic_name__					=      ''.join(_lowerCAmelCase  )
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																							__magic_name__					=      True
																							__magic_name__					=      self.tokenizer_class.from_pretrained(_lowerCAmelCase	,      **_lowerCAmelCase  )
																							__magic_name__					=      self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	,      **_lowerCAmelCase  )

																							__magic_name__					=      tokenizer_p.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )
																							__magic_name__					=      tokenizer_r.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )

																							__magic_name__					=      tokenizer_r.convert_ids_to_tokens(_lowerCAmelCase  )
																							__magic_name__					=      tokenizer_p.convert_ids_to_tokens(_lowerCAmelCase  )

																							# it is expected that each Chinese character is not preceded by "##"
																							self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )
																							self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

																							__magic_name__					=      False
																							__magic_name__					=      self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	,      **_lowerCAmelCase  )
																							__magic_name__					=      self.tokenizer_class.from_pretrained(_lowerCAmelCase	,      **_lowerCAmelCase  )

																							__magic_name__					=      tokenizer_r.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )
																							__magic_name__					=      tokenizer_p.encode(_lowerCAmelCase	,      add_special_tokens=_lowerCAmelCase  )

																							__magic_name__					=      tokenizer_r.convert_ids_to_tokens(_lowerCAmelCase  )
																							__magic_name__					=      tokenizer_p.convert_ids_to_tokens(_lowerCAmelCase  )

																							# it is expected that only the first Chinese character is not preceded by "##".
																							__magic_name__					=      [
																							    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(_lowerCAmelCase  )
																							]
																							self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )
																							self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )

