




"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class      a__				(	__magic_name__   ):
			lowercase_    =	(UniPCMultistepScheduler,)
			lowercase_    =	(("num_inference_steps", 2_5),)


			def       a_     (      self      :    List[Any]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					{
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						    "solver_type": "bh2",
						}

						config.update(**UpperCamelCase_)
						return config


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Optional[int]=0      ,  **UpperCamelCase_      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					dict(self.forward_default_kwargs)
						__UpperCAmelCase	: Tuple		=					kwargs.pop("num_inference_steps"      ,  UpperCamelCase_)
						__UpperCAmelCase	: Optional[int]		=					self.dummy_sample
						__UpperCAmelCase	: Tuple		=					0.1 * sample
						__UpperCAmelCase	: str		=					[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase	: int		=					self.get_scheduler_config(**UpperCamelCase_)
									__UpperCAmelCase	: Union[str, Any]		=					scheduler_class(**UpperCamelCase_)
									scheduler.set_timesteps(UpperCamelCase_)
									# copy over dummy past residuals
									__UpperCAmelCase	: str		=					dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(UpperCamelCase_)
												__UpperCAmelCase	: Union[str, Any]		=					scheduler_class.from_pretrained(UpperCamelCase_)
												new_scheduler.set_timesteps(UpperCamelCase_)
												# copy over dummy past residuals
												__UpperCAmelCase	: Optional[Any]		=					dummy_past_residuals[: new_scheduler.config.solver_order]

									__UpperCAmelCase	,     __UpperCAmelCase	: Optional[int]		=					sample, sample
									for t in range(UpperCamelCase_      ,  time_step + scheduler.config.solver_order + 1):
												__UpperCAmelCase	: Optional[int]		=					scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_).prev_sample
												__UpperCAmelCase	: int		=					new_scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_).prev_sample

												assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[Any]=0      ,  **UpperCamelCase_      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					dict(self.forward_default_kwargs)
						__UpperCAmelCase	: Optional[Any]		=					kwargs.pop("num_inference_steps"      ,  UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					self.dummy_sample
						__UpperCAmelCase	: Optional[Any]		=					0.1 * sample
						__UpperCAmelCase	: Optional[Any]		=					[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase	: str		=					self.get_scheduler_config()
									__UpperCAmelCase	: Dict		=					scheduler_class(**UpperCamelCase_)
									scheduler.set_timesteps(UpperCamelCase_)

									# copy over dummy past residuals (must be after setting timesteps)
									__UpperCAmelCase	: List[str]		=					dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(UpperCamelCase_)
												__UpperCAmelCase	: int		=					scheduler_class.from_pretrained(UpperCamelCase_)
												# copy over dummy past residuals
												new_scheduler.set_timesteps(UpperCamelCase_)

												# copy over dummy past residual (must be after setting timesteps)
												__UpperCAmelCase	: Optional[Any]		=					dummy_past_residuals[: new_scheduler.config.solver_order]

									__UpperCAmelCase	: Optional[int]		=					scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_).prev_sample
									__UpperCAmelCase	: List[Any]		=					new_scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_).prev_sample

									assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Optional[Any]=None      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						if scheduler is None:
									__UpperCAmelCase	: int		=					self.scheduler_classes[0]
									__UpperCAmelCase	: Tuple		=					self.get_scheduler_config(**UpperCamelCase_)
									__UpperCAmelCase	: str		=					scheduler_class(**UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					self.scheduler_classes[0]
						__UpperCAmelCase	: str		=					self.get_scheduler_config(**UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					scheduler_class(**UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					10
						__UpperCAmelCase	: int		=					self.dummy_model()
						__UpperCAmelCase	: int		=					self.dummy_sample_deter
						scheduler.set_timesteps(UpperCamelCase_)

						for i, t in enumerate(scheduler.timesteps):
									__UpperCAmelCase	: List[Any]		=					model(UpperCamelCase_      ,  UpperCamelCase_)
									__UpperCAmelCase	: Dict		=					scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_).prev_sample

						return sample


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					dict(self.forward_default_kwargs)

						__UpperCAmelCase	: int		=					kwargs.pop("num_inference_steps"      ,  UpperCamelCase_)

						for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase	: int		=					self.get_scheduler_config()
									__UpperCAmelCase	: Union[str, Any]		=					scheduler_class(**UpperCamelCase_)

									__UpperCAmelCase	: Tuple		=					self.dummy_sample
									__UpperCAmelCase	: Union[str, Any]		=					0.1 * sample

									if num_inference_steps is not None and hasattr(UpperCamelCase_      ,  "set_timesteps"):
												scheduler.set_timesteps(UpperCamelCase_)
									elif num_inference_steps is not None and not hasattr(UpperCamelCase_      ,  "set_timesteps"):
												__UpperCAmelCase	: Tuple		=					num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									__UpperCAmelCase	: Optional[int]		=					[residual + 0.2, residual + 0.15, residual + 0.10]
									__UpperCAmelCase	: Optional[int]		=					dummy_past_residuals[: scheduler.config.solver_order]

									__UpperCAmelCase	: Optional[Any]		=					scheduler.timesteps[5]
									__UpperCAmelCase	: int		=					scheduler.timesteps[6]

									__UpperCAmelCase	: Optional[Any]		=					scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_).prev_sample
									__UpperCAmelCase	: List[Any]		=					scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  **UpperCamelCase_).prev_sample

									self.assertEqual(output_a.shape      ,  sample.shape)
									self.assertEqual(output_a.shape      ,  output_a.shape)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					UniPCMultistepScheduler(**self.get_scheduler_config())
						__UpperCAmelCase	: str		=					self.full_loop(scheduler=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					torch.mean(torch.abs(UpperCamelCase_))

						assert abs(result_mean.item() - 0.2464) < 1e-3

						__UpperCAmelCase	: Any		=					DPMSolverSinglestepScheduler.from_config(scheduler.config)
						__UpperCAmelCase	: int		=					DEISMultistepScheduler.from_config(scheduler.config)
						__UpperCAmelCase	: List[Any]		=					DPMSolverMultistepScheduler.from_config(scheduler.config)
						__UpperCAmelCase	: Optional[int]		=					UniPCMultistepScheduler.from_config(scheduler.config)

						__UpperCAmelCase	: str		=					self.full_loop(scheduler=UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					torch.mean(torch.abs(UpperCamelCase_))

						assert abs(result_mean.item() - 0.2464) < 1e-3


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						self.check_over_configs(thresholding=UpperCamelCase_)
						for order in [1, 2, 3]:
									for solver_type in ["bh1", "bh2"]:
												for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(
																		    thresholding=UpperCamelCase_      ,  prediction_type=UpperCamelCase_      ,  sample_max_value=UpperCamelCase_      ,  solver_order=UpperCamelCase_      ,  solver_type=UpperCamelCase_      ,  )


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=UpperCamelCase_)


			def       a_     (      self      :    int):


						"""simple docstring"""




						for solver_type in ["bh1", "bh2"]:
									for order in [1, 2, 3]:
												for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(
															    solver_order=UpperCamelCase_      ,  solver_type=UpperCamelCase_      ,  prediction_type=UpperCamelCase_      ,  )
															__UpperCAmelCase	: Any		=					self.full_loop(
															    solver_order=UpperCamelCase_      ,  solver_type=UpperCamelCase_      ,  prediction_type=UpperCamelCase_      ,  )
															assert not torch.isnan(UpperCamelCase_).any(), "Samples have nan numbers"


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						self.check_over_configs(lower_order_final=UpperCamelCase_)
						self.check_over_configs(lower_order_final=UpperCamelCase_)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=UpperCamelCase_      ,  time_step=0)


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self.full_loop()
						__UpperCAmelCase	: int		=					torch.mean(torch.abs(UpperCamelCase_))

						assert abs(result_mean.item() - 0.2464) < 1e-3


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.full_loop(prediction_type="v_prediction")
						__UpperCAmelCase	: Dict		=					torch.mean(torch.abs(UpperCamelCase_))

						assert abs(result_mean.item() - 0.1014) < 1e-3


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					self.scheduler_classes[0]
						__UpperCAmelCase	: str		=					self.get_scheduler_config(thresholding=UpperCamelCase_      ,  dynamic_thresholding_ratio=0)
						__UpperCAmelCase	: List[Any]		=					scheduler_class(**UpperCamelCase_)

						__UpperCAmelCase	: Any		=					10
						__UpperCAmelCase	: Union[str, Any]		=					self.dummy_model()
						__UpperCAmelCase	: Tuple		=					self.dummy_sample_deter.half()
						scheduler.set_timesteps(UpperCamelCase_)

						for i, t in enumerate(scheduler.timesteps):
									__UpperCAmelCase	: Any		=					model(UpperCamelCase_      ,  UpperCamelCase_)
									__UpperCAmelCase	: Tuple		=					scheduler.step(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_).prev_sample

						assert sample.dtype == torch.floataa


			def       a_     (      self      :    List[str]      ,  **UpperCamelCase_      :    str):


						"""simple docstring"""




						for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase	: Any		=					self.get_scheduler_config(**UpperCamelCase_)
									__UpperCAmelCase	: List[Any]		=					scheduler_class(**UpperCamelCase_)

									scheduler.set_timesteps(scheduler.config.num_train_timesteps)
									assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps








"""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):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""



class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir      ,  "models/bert/"))
						__UpperCAmelCase	: Tuple		=					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       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					"src/transformers"
						shutil.rmtree(self.transformer_dir)


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    List[str]=None):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					comment + F"\nclass {class_name}(nn.Module):\n" + class_code
						if overwrite_result is not None:
									__UpperCAmelCase	: str		=					comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
						__UpperCAmelCase	: Optional[int]		=					black.Mode(target_versions={black.TargetVersion.PYaa}      ,  line_length=119)
						__UpperCAmelCase	: Dict		=					black.format_str(UpperCamelCase_      ,  mode=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					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       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""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	: Optional[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"      ,  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       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

						__UpperCAmelCase	: 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."
						)
						__UpperCAmelCase	: 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"
						)
						__UpperCAmelCase	: 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。\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	: Any		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

						self.assertFalse(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)

						__UpperCAmelCase	,     __UpperCAmelCase	: int		=					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	: 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."
						)
						__UpperCAmelCase	: Optional[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"
						)
						__UpperCAmelCase	: int		=					(
						    "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	: List[str]		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

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


