



import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       List[Any]          =GPTaTokenizer
		lowercase      :       Tuple          =GPTaTokenizerFast
		lowercase      :       Tuple          =True
		lowercase      :       Dict          ={'''add_prefix_space''': True}
		lowercase      :       List[Any]          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase         =     [
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "\u0120",
								    "\u0120l",
								    "\u0120n",
								    "\u0120lo",
								    "\u0120low",
								    "er",
								    "\u0120lowest",
								    "\u0120newer",
								    "\u0120wider",
								    "<unk>",
								    "<|endoftext|>",
								]
								UpperCAmelCase         =     dict(zip(UpperCamelCase__				,		range(len(UpperCamelCase__      )      )      )      )
								UpperCAmelCase         =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
								UpperCAmelCase         =     {"unk_token": "<unk>"}

								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								UpperCAmelCase         =     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(UpperCamelCase__      ) + "\n"      )
								with open(self.merges_file				,		"w"				,		encoding="utf-8"      ) as fp:
														fp.write("\n".join(UpperCamelCase__      )      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		**UpperCamelCase__      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								kwargs.update(self.special_tokens_map      )
								return GPTaTokenizer.from_pretrained(self.tmpdirname				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		**UpperCamelCase__      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								kwargs.update(self.special_tokens_map      )
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     Dict      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     "lower newer"
								UpperCAmelCase         =     "lower newer"
								return input_text, output_text






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     GPTaTokenizer(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map      )
								UpperCAmelCase         =     "lower newer"
								UpperCAmelCase         =     ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokens + [tokenizer.unk_token]
								UpperCAmelCase         =     [14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					int:
								'''simple docstring'''







								if not self.test_rust_tokenizer:
														return

								UpperCAmelCase         =     self.get_tokenizer()
								UpperCAmelCase         =     self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__      )

								UpperCAmelCase         =     "lower newer"

								# Testing tokenization
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								# Testing conversion to ids without special tokens
								UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								# Testing conversion to ids with special tokens
								UpperCAmelCase         =     self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__      )
								UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.encode(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								# Testing the unknown token
								UpperCAmelCase         =     tokens + [rust_tokenizer.unk_token]
								UpperCAmelCase         =     [14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		*UpperCamelCase__      :     Optional[int]				,		**UpperCamelCase__      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Union[str, Any]=15      )				->					Optional[int]:
								'''simple docstring'''







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

																				# Simple input
																				UpperCAmelCase         =     "This is a simple input"
																				UpperCAmelCase         =     ["This is a simple input 1", "This is a simple input 2"]
																				UpperCAmelCase         =     ("This is a simple input", "This is a pair")
																				UpperCAmelCase         =     [
																				    ("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(UpperCamelCase__				,		tokenizer_r.encode				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Simple input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Simple input
																				self.assertRaises(
																				    UpperCamelCase__				,		tokenizer_r.batch_encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"				,		)

																				# Pair input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Pair input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Pair input
																				self.assertRaises(
																				    UpperCamelCase__				,		tokenizer_r.batch_encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     GPTaTokenizer.from_pretrained(self.tmpdirname				,		pad_token="<pad>"      )

								# Simple input
								UpperCAmelCase         =     "This is a simple input"
								UpperCAmelCase         =     ["This is a simple input looooooooong", "This is a simple input"]
								UpperCAmelCase         =     ("This is a simple input", "This is a pair")
								UpperCAmelCase         =     [
								    ("This is a simple input loooooong", "This is a simple input"),
								    ("This is a simple pair loooooong", "This is a simple pair"),
								]

								UpperCAmelCase         =     tokenizer.pad_token_id

								UpperCAmelCase         =     tokenizer(UpperCamelCase__				,		padding="max_length"				,		max_length=30				,		return_tensors="np"      )
								UpperCAmelCase         =     tokenizer(UpperCamelCase__				,		padding=UpperCamelCase__				,		truncate=UpperCamelCase__				,		return_tensors="np"      )
								UpperCAmelCase         =     tokenizer(*UpperCamelCase__				,		padding="max_length"				,		max_length=60				,		return_tensors="np"      )
								UpperCAmelCase         =     tokenizer(UpperCamelCase__				,		padding=UpperCamelCase__				,		truncate=UpperCamelCase__				,		return_tensors="np"      )

								# s
								# test single string max_length padding
								self.assertEqual(out_s["input_ids"].shape[-1]				,		30      )
								self.assertTrue(pad_token_id in out_s["input_ids"]      )
								self.assertTrue(0 in out_s["attention_mask"]      )

								# s2
								# test automatic padding
								self.assertEqual(out_sa["input_ids"].shape[-1]				,		33      )
								# long slice doesn't have padding
								self.assertFalse(pad_token_id in out_sa["input_ids"][0]      )
								self.assertFalse(0 in out_sa["attention_mask"][0]      )
								# short slice does have padding
								self.assertTrue(pad_token_id in out_sa["input_ids"][1]      )
								self.assertTrue(0 in out_sa["attention_mask"][1]      )

								# p
								# test single pair max_length padding
								self.assertEqual(out_p["input_ids"].shape[-1]				,		60      )
								self.assertTrue(pad_token_id in out_p["input_ids"]      )
								self.assertTrue(0 in out_p["attention_mask"]      )

								# p2
								# test automatic padding pair
								self.assertEqual(out_pa["input_ids"].shape[-1]				,		52      )
								# long slice pair doesn't have padding
								self.assertFalse(pad_token_id in out_pa["input_ids"][0]      )
								self.assertFalse(0 in out_pa["attention_mask"][0]      )
								# short slice pair does have padding
								self.assertTrue(pad_token_id in out_pa["input_ids"][1]      )
								self.assertTrue(0 in out_pa["attention_mask"][1]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "$$$"
								UpperCAmelCase         =     GPTaTokenizer.from_pretrained(self.tmpdirname				,		bos_token=UpperCamelCase__				,		add_bos_token=UpperCamelCase__      )

								UpperCAmelCase         =     "This is a simple input"
								UpperCAmelCase         =     ["This is a simple input 1", "This is a simple input 2"]

								UpperCAmelCase         =     tokenizer.bos_token_id

								UpperCAmelCase         =     tokenizer(UpperCamelCase__      )
								UpperCAmelCase         =     tokenizer(UpperCamelCase__      )

								self.assertEqual(out_s.input_ids[0]				,		UpperCamelCase__      )
								self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

								UpperCAmelCase         =     tokenizer.decode(out_s.input_ids      )
								UpperCAmelCase         =     tokenizer.batch_decode(out_sa.input_ids      )

								self.assertEqual(decode_s.split()[0]				,		UpperCamelCase__      )
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					List[str]:
								'''simple docstring'''







								pass







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     [self.get_tokenizer(do_lower_case=UpperCamelCase__				,		add_bos_token=UpperCamelCase__      )]
								for tokenizer in tokenizers:
														with self.subTest(F'{tokenizer.__class__.__name__}'      ):
																				UpperCAmelCase         =     "Encode this."
																				UpperCAmelCase         =     "This one too please."
																				UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
																				encoded_sequence += tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
																				UpperCAmelCase         =     tokenizer.encode_plus(
																				    UpperCamelCase__				,		UpperCamelCase__				,		add_special_tokens=UpperCamelCase__				,		return_special_tokens_mask=UpperCamelCase__				,		)
																				UpperCAmelCase         =     encoded_sequence_dict["input_ids"]
																				UpperCAmelCase         =     encoded_sequence_dict["special_tokens_mask"]
																				self.assertEqual(len(UpperCamelCase__      )				,		len(UpperCamelCase__      )      )

																				UpperCAmelCase         =     [
																				    (x if not special_tokens_mask[i] else None) for i, x in enumerate(UpperCamelCase__      )
																				]
																				UpperCAmelCase         =     [x for x in filtered_sequence if x is not None]
																				self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )





@require_tokenizers
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     AutoTokenizer.from_pretrained("facebook/opt-350m"				,		from_slow=UpperCamelCase__      )
								UpperCAmelCase         =     "A photo of a cat"

								UpperCAmelCase         =     tokenizer.encode(
								    UpperCamelCase__				,		)
								self.assertEqual(UpperCamelCase__				,		[2, 2_50, 13_45, 9, 10, 47_58]      )
								tokenizer.save_pretrained("test_opt"      )

								UpperCAmelCase         =     AutoTokenizer.from_pretrained("./test_opt"      )
								UpperCAmelCase         =     tokenizer.encode(
								    UpperCamelCase__				,		)
								self.assertEqual(UpperCamelCase__				,		[2, 2_50, 13_45, 9, 10, 47_58]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     AutoTokenizer.from_pretrained("facebook/opt-350m"				,		use_slow=UpperCamelCase__      )
								UpperCAmelCase         =     "A photo of a cat"

								UpperCAmelCase         =     tokenizer.encode(
								    UpperCamelCase__				,		)
								# Same as above
								self.assertEqual(UpperCamelCase__				,		[2, 2_50, 13_45, 9, 10, 47_58]      )







		@unittest.skip("This test is failing because of a bug in the fast tokenizer"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     AutoTokenizer.from_pretrained("facebook/opt-350m"				,		from_slow=UpperCamelCase__      )

								UpperCAmelCase         =     "bos"
								UpperCAmelCase         =     tokenizer.get_vocab()["bos"]

								UpperCAmelCase         =     "A photo of a cat"
								UpperCAmelCase         =     tokenizer.encode(
								    UpperCamelCase__				,		)
								# We changed the bos token
								self.assertEqual(UpperCamelCase__				,		[3_19_57, 2_50, 13_45, 9, 10, 47_58]      )
								tokenizer.save_pretrained("./tok"      )
								UpperCAmelCase         =     AutoTokenizer.from_pretrained("./tok"      )
								self.assertTrue(tokenizer.is_fast      )
								UpperCAmelCase         =     tokenizer.encode(
								    UpperCamelCase__				,		)
								self.assertEqual(UpperCamelCase__				,		[3_19_57, 2_50, 13_45, 9, 10, 47_58]      )








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

import black


__lowerCamelCase     :							str  							=				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.
__lowerCamelCase     :							Optional[Any]  							=				"    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  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,		"models/bert/"      )      )
								UpperCAmelCase         =     self.transformer_dir
								shutil.copy(
								    os.path.join(UpperCamelCase__				,		"src/transformers/models/bert/modeling_bert.py"      )				,		os.path.join(self.transformer_dir				,		"models/bert/modeling_bert.py"      )				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "src/transformers"
								shutil.rmtree(self.transformer_dir      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=None      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								UpperCAmelCase         =     black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19      )
								UpperCAmelCase         =     black.format_str(UpperCamelCase__				,		mode=UpperCamelCase__      )
								UpperCAmelCase         =     os.path.join(self.transformer_dir				,		"new_code.py"      )
								with open(UpperCamelCase__				,		"w"				,		newline="\n"      ) as f:
														f.write(UpperCamelCase__      )
								if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__      )      ) == 0      )
								else:
														check_copies.is_copy_consistent(f.name				,		overwrite=UpperCamelCase__      )
														with open(UpperCamelCase__				,		"r"      ) as f:
																				self.assertTrue(f.read()				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								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"				,		UpperCamelCase__				,		)

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

								# Copy consistency with a really long name
								UpperCAmelCase         =     "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"				,		UpperCamelCase__				,		UpperCamelCase__      )				,		)

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







		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								UpperCAmelCase         =     (
								    "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."
								)
								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     (
								    "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"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								self.assertFalse(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

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

								UpperCAmelCase         =     (
								    "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."
								)
								UpperCAmelCase         =     (
								    "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"
								)
								UpperCAmelCase         =     (
								    "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"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

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



