




'''simple docstring'''






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        lowercase_      (						lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			LongformerTokenizer
     A_											=			True
     A_											=			LongformerTokenizerFast
     A_											=			True





     def 			_lowerCAmelCase      (     self     :					int			):
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           snake_case__       :   Optional[int]	      =    [
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           snake_case__       :   List[str]	      =    dict(zip(__lowerCamelCase     ,							range(len(__lowerCamelCase			)			)			)			)
           snake_case__       :   str	      =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           snake_case__       :   Any	      =    {'unk_token': '<unk>'}

           snake_case__       :   List[Any]	      =    os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['vocab_file']			)
           snake_case__       :   List[Any]	      =    os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['merges_file']			)
           with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'			) as fp:
                 fp.write(json.dumps(__lowerCamelCase			) + '\n'			)
           with open(self.merges_file     ,							'w'     ,							encoding='utf-8'			) as fp:
                 fp.write('\n'.join(__lowerCamelCase			)			)





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							**__lowerCamelCase     :					int			):
           kwargs.update(self.special_tokens_map			)
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							**__lowerCamelCase     :					int			):
           kwargs.update(self.special_tokens_map			)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							__lowerCamelCase     :					List[Any]			):
           snake_case__       :   Optional[Any]	      =    'lower newer'
           snake_case__       :   Optional[int]	      =    'lower newer'
           return input_text, output_text





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[Any]	      =    self.tokenizer_class(self.vocab_file     ,							self.merges_file     ,							**self.special_tokens_map			)
           snake_case__       :   Dict	      =    'lower newer'
           snake_case__       :   List[str]	      =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
           snake_case__       :   Any	      =    tokenizer.tokenize(__lowerCamelCase			)  # , add_prefix_space=True)
           self.assertListEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   str	      =    tokens + [tokenizer.unk_token]
           snake_case__       :   Any	      =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase			)     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Tuple	      =    self.get_tokenizer()

           self.assertListEqual(tokenizer.encode('Hello world!'     ,							add_special_tokens=__lowerCamelCase			)     ,							[0, 31414, 232, 328, 2]			)
           self.assertListEqual(
               tokenizer.encode('Hello world! cécé herlolip 418'     ,							add_special_tokens=__lowerCamelCase			)     ,							[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]     ,							)





     @slow
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Tuple	      =    self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'			)

           snake_case__       :   str	      =    tokenizer.encode('sequence builders'     ,							add_special_tokens=__lowerCamelCase			)
           snake_case__       :   int	      =    tokenizer.encode('multi-sequence build'     ,							add_special_tokens=__lowerCamelCase			)

           snake_case__       :   List[str]	      =    tokenizer.encode(
               'sequence builders'     ,							add_special_tokens=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase			)
           snake_case__       :   List[Any]	      =    tokenizer.encode(
               'sequence builders'     ,							'multi-sequence build'     ,							add_special_tokens=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase			)

           snake_case__       :   Dict	      =    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase			)
           snake_case__       :   str	      =    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase     ,							__lowerCamelCase			)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   str	      =    self.get_tokenizer()

           snake_case__       :   int	      =    'Encode this sequence.'
           snake_case__       :   Union[str, Any]	      =    tokenizer.byte_encoder[' '.encode('utf-8'			)[0]]

           # Testing encoder arguments
           snake_case__       :   Union[str, Any]	      =    tokenizer.encode(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase			)
           snake_case__       :   List[str]	      =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
           self.assertNotEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   int	      =    tokenizer.encode(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           tokenizer.add_special_tokens({'bos_token': '<s>'}			)
           snake_case__       :   str	      =    tokenizer.encode(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
           self.assertNotEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           # Testing spaces after special tokens
           snake_case__       :   Dict	      =    '<mask>'
           tokenizer.add_special_tokens(
               {'mask_token': AddedToken(__lowerCamelCase     ,							lstrip=__lowerCamelCase     ,							rstrip=__lowerCamelCase			)}			)  # mask token has a left space
           snake_case__       :   List[Any]	      =    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)

           snake_case__       :   Any	      =    'Encode <mask> sequence'
           snake_case__       :   Optional[int]	      =    'Encode <mask>sequence'

           snake_case__       :   Union[str, Any]	      =    tokenizer.encode(__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    encoded.index(__lowerCamelCase			)
           snake_case__       :   Any	      =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   List[str]	      =    tokenizer.encode(__lowerCamelCase			)
           snake_case__       :   Any	      =    encoded.index(__lowerCamelCase			)
           snake_case__       :   int	      =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
           self.assertNotEqual(__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           pass





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                       snake_case__       :   Union[str, Any]	      =    self.rust_tokenizer_class.from_pretrained(__lowerCamelCase     ,							**__lowerCamelCase			)
                       snake_case__       :   List[str]	      =    self.tokenizer_class.from_pretrained(__lowerCamelCase     ,							**__lowerCamelCase			)
                       snake_case__       :   Dict	      =    'A, <mask> AllenNLP sentence.'
                       snake_case__       :   Tuple	      =    tokenizer_r.encode_plus(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							return_token_type_ids=__lowerCamelCase			)
                       snake_case__       :   Optional[Any]	      =    tokenizer_p.encode_plus(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							return_token_type_ids=__lowerCamelCase			)

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r['token_type_ids']			)     ,							sum(tokens_p['token_type_ids']			)			)

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r['attention_mask']			) / len(tokens_r['attention_mask']			)     ,							sum(tokens_p['attention_mask']			) / len(tokens_p['attention_mask']			)     ,							)

                       snake_case__       :   Any	      =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                       snake_case__       :   Union[str, Any]	      =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p['input_ids']     ,							[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)
                       self.assertSequenceEqual(tokens_r['input_ids']     ,							[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

                       self.assertSequenceEqual(
                           __lowerCamelCase     ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                       self.assertSequenceEqual(
                           __lowerCamelCase     ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           for trim_offsets, add_prefix_space in itertools.product([True, False]     ,							repeat=2			):
                 snake_case__       :   str	      =    self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)

                 snake_case__       :   Optional[int]	      =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
                 snake_case__       :   Dict	      =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

                 self.assertEqual(pre_tokenizer_state['add_prefix_space']     ,							__lowerCamelCase			)

                 self.assertEqual(post_processor_state['add_prefix_space']     ,							__lowerCamelCase			)
                 self.assertEqual(post_processor_state['trim_offsets']     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
           # `trim_offsets`
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                       snake_case__       :   str	      =    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                       snake_case__       :   Tuple	      =    F"{text_of_1_token} {text_of_1_token}"

                       snake_case__       :   List[Any]	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   Tuple	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(len(__lowerCamelCase			) + 1, len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)

                       snake_case__       :   Union[str, Any]	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   str	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(len(__lowerCamelCase			) + 1, len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)

                       snake_case__       :   str	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   Union[str, Any]	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(len(__lowerCamelCase			), len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)

                       snake_case__       :   Optional[Any]	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   int	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(len(__lowerCamelCase			), len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)

                       snake_case__       :   int	      =    F" {text}"

                       # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                       #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                       # )
                       # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                       # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                       # self.assertEqual(
                       #     encoding.offset_mapping[1],
                       #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                       # )

                       snake_case__       :   List[Any]	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   str	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(1, 1 + len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(1 + len(__lowerCamelCase			) + 1, 1 + len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)

                       snake_case__       :   int	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   str	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(0, 1 + len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(1 + len(__lowerCamelCase			), 1 + len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)

                       snake_case__       :   Optional[int]	      =    self.rust_tokenizer_class.from_pretrained(
                           __lowerCamelCase     ,							use_fast=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase			)
                       snake_case__       :   Optional[int]	      =    tokenizer_r(__lowerCamelCase     ,							return_offsets_mapping=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
                       self.assertEqual(encoding.offset_mapping[0]     ,							(0, 1 + len(__lowerCamelCase			))			)
                       self.assertEqual(
                           encoding.offset_mapping[1]     ,							(1 + len(__lowerCamelCase			), 1 + len(__lowerCamelCase			) + 1 + len(__lowerCamelCase			))     ,							)






'''simple docstring'''






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_			      =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_			      =		"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   str	      =    tempfile.mkdtemp()
           os.makedirs(os.path.join(self.transformer_dir     ,							'models/bert/'			)			)
           snake_case__       :   Optional[Any]	      =    self.transformer_dir
           shutil.copy(
               os.path.join(__lowerCamelCase     ,							'src/transformers/models/bert/modeling_bert.py'			)     ,							os.path.join(self.transformer_dir     ,							'models/bert/modeling_bert.py'			)     ,							)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Union[str, Any]	      =    'src/transformers'
           shutil.rmtree(self.transformer_dir			)





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]=None			):
           snake_case__       :   Optional[int]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + class_code
           if overwrite_result is not None:
                 snake_case__       :   List[Any]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
           snake_case__       :   Optional[int]	      =    black.Mode(target_versions={black.TargetVersion.PYaa}     ,							line_length=119			)
           snake_case__       :   Tuple	      =    black.format_str(__lowerCamelCase     ,							mode=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    os.path.join(self.transformer_dir     ,							'new_code.py'			)
           with open(__lowerCamelCase     ,							'w'     ,							newline='\n'			) as f:
                 f.write(__lowerCamelCase			)
           if overwrite_result is None:
                 self.assertTrue(len(check_copies.is_copy_consistent(__lowerCamelCase			)			) == 0			)
           else:
                 check_copies.is_copy_consistent(f.name     ,							overwrite=__lowerCamelCase			)
                 with open(__lowerCamelCase     ,							'r'			) as f:
                       self.assertTrue(f.read()     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[str]	      =    check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           # Base copy consistency
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'     ,							'BertLMPredictionHead'     ,							REFERENCE_CODE + '\n'     ,							)

           # With no empty line at the end
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'     ,							'BertLMPredictionHead'     ,							__lowerCamelCase     ,							)

           # Copy consistency with rename
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'     ,							'TestModelLMPredictionHead'     ,							re.sub('Bert'     ,							'TestModel'     ,							__lowerCamelCase			)     ,							)

           # Copy consistency with a really long name
           snake_case__       :   Union[str, Any]	      =    'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
           self.check_copy_consistency(
               F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"     ,							F"{long_class_name}LMPredictionHead"     ,							re.sub('Bert'     ,							__lowerCamelCase     ,							__lowerCamelCase			)     ,							)

           # Copy consistency with overwrite
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'     ,							'TestModelLMPredictionHead'     ,							__lowerCamelCase     ,							overwrite_result=re.sub('Bert'     ,							'TestModel'     ,							__lowerCamelCase			)     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    check_copies.LOCALIZED_READMES['README_zh-hans.md']

           snake_case__       :   List[str]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
               ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
               ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
               ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
               ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
               ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
               ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
               ' method has been applied to compress GPT2 into'
               ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
               ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
               ' Multilingual BERT into'
               ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
               ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
               ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
               ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
               ' Luong, Quoc V. Le, Christopher D. Manning.'
           )
           snake_case__       :   Union[str, Any]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )
           snake_case__       :   Tuple	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
               ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
               ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
               ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
               ' method has been applied to compress GPT2 into'
               ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
               ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
               ' Multilingual BERT into'
               ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
               ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
               ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
               ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
               ' Christopher D. Manning 发布。\n'
           )

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           self.assertFalse(__lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           # Check whether the number of models is equal to README.md after conversion.
           self.assertTrue(__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
               ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
               ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
               ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
           )
           snake_case__       :   int	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
               ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )
           snake_case__       :   List[str]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           # Check if the model link is synchronized.
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)
