






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:
							for param, grad_param in zip(model_a.parameters()     ,     model_b.parameters()				):
														if not param.requires_grad:
																					continue
														if not did_step:
																					# Grads should not be in sync
																					assert (
																					    torch.allclose(param.grad     ,     grad_param.grad				) is False
																					), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
														else:
																					# Grads should be in sync
																					assert (
																					    torch.allclose(param.grad     ,     grad_param.grad				) is True
																					), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=True				)     ->    Tuple:
							model.train()
							_lowercase							:   List[str] 							=   model(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Union[str, Any] 							=   F.mse_loss(SCREAMING_SNAKE_CASE     ,     target.to(output.device				)				)
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=False				)     ->    List[Any]:
							set_seed(42				)
							_lowercase							:   List[str] 							=   RegressionModel()
							_lowercase							:   Any 							=   deepcopy(SCREAMING_SNAKE_CASE				)
							_lowercase							:   List[Any] 							=   RegressionDataset(length=80				)
							_lowercase							:   Any 							=   DataLoader(SCREAMING_SNAKE_CASE     ,     batch_size=16				)
							model.to(accelerator.device				)
							if sched:
														_lowercase							:   int 							=   AdamW(params=model.parameters()     ,     lr=1E-3				)
														_lowercase							:   List[str] 							=   AdamW(params=ddp_model.parameters()     ,     lr=1E-3				)
														_lowercase							:   List[Any] 							=   LambdaLR(SCREAMING_SNAKE_CASE     ,     lr_lambda=lambda SCREAMING_SNAKE_CASE				: epoch**0.65				)
														_lowercase							:   Any 							=   LambdaLR(SCREAMING_SNAKE_CASE     ,     lr_lambda=lambda SCREAMING_SNAKE_CASE				: epoch**0.65				)
							# Make a copy of `model`
							if sched:
														_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   accelerator.prepare(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							else:
														_lowercase							,							_lowercase							:   Any 							=   accelerator.prepare(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    int:
							# Test when on a single CPU or GPU that the context manager does nothing
							_lowercase							,							_lowercase							,							_lowercase							:   List[str] 							=   get_training_setup(SCREAMING_SNAKE_CASE				)
							# Use a single batch
							_lowercase							,							_lowercase							:   List[Any] 							=   next(iter(SCREAMING_SNAKE_CASE				)				).values()
							for iteration in range(3				):
														# Gather the distributed inputs and targs for the base model
														_lowercase							,							_lowercase							:   List[Any] 							=   accelerator.gather((ddp_input, ddp_target)				)
														_lowercase							,							_lowercase							:   Dict 							=   input.to(accelerator.device				), target.to(accelerator.device				)
														# Perform our initial ground truth step in non "DDP"
														step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(SCREAMING_SNAKE_CASE				):
																												step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														else:
																					# Sync grads
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														for param, ddp_param in zip(model.parameters()     ,     ddp_model.parameters()				):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad     ,     ddp_param.grad				), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration				)
														_lowercase							:   List[Any] 							=   ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE				)				)]






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Tuple:
							# Test on distributed setup that context manager behaves properly
							_lowercase							,							_lowercase							,							_lowercase							:   Dict 							=   get_training_setup(SCREAMING_SNAKE_CASE				)
							# Use a single batch
							_lowercase							,							_lowercase							:   str 							=   next(iter(SCREAMING_SNAKE_CASE				)				).values()
							for iteration in range(3				):
														# Gather the distributed inputs and targs for the base model
														_lowercase							,							_lowercase							:   str 							=   accelerator.gather((ddp_input, ddp_target)				)
														_lowercase							,							_lowercase							:   List[Any] 							=   input.to(accelerator.device				), target.to(accelerator.device				)
														# Perform our initial ground truth step in non "DDP"
														step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(SCREAMING_SNAKE_CASE				):
																												step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														else:
																					# Sync grads
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()     ,     ddp_model.parameters()				):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad     ,     ddp_param.grad				) is False
																												), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad     ,     ddp_param.grad				) is True
																												), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration				)
														_lowercase							:   int 							=   ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE				)				)]






def    __magic_name__    (	SCREAMING_SNAKE_CASE=False     ,     SCREAMING_SNAKE_CASE=False				)     ->    List[str]:
							_lowercase							:   List[str] 							=   Accelerator(
							    split_batches=SCREAMING_SNAKE_CASE     ,     dispatch_batches=SCREAMING_SNAKE_CASE     ,     gradient_accumulation_steps=2				)
							# Test that context manager behaves properly
							_lowercase							,							_lowercase							,							_lowercase							:   Any 							=   get_training_setup(SCREAMING_SNAKE_CASE				)
							for iteration, batch in enumerate(SCREAMING_SNAKE_CASE				):
														_lowercase							,							_lowercase							:   int 							=   batch.values()
														# Gather the distributed inputs and targs for the base model
														_lowercase							,							_lowercase							:   Tuple 							=   accelerator.gather((ddp_input, ddp_target)				)
														_lowercase							,							_lowercase							:   List[str] 							=   input.to(accelerator.device				), target.to(accelerator.device				)
														# Perform our initial ground truth step in non "DDP"
														step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(SCREAMING_SNAKE_CASE				):
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()     ,     ddp_model.parameters()				):
																					if not param.requires_grad:
																												continue
																					if ((iteration + 1) % 2 == 0) or (iteration == len(SCREAMING_SNAKE_CASE				) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad     ,     ddp_param.grad				) is True
																												), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad     ,     ddp_param.grad				) is False
																												), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration				)
														_lowercase							:   Optional[int] 							=   ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE				)				)]
							GradientState._reset_state()






def    __magic_name__    (	SCREAMING_SNAKE_CASE=False     ,     SCREAMING_SNAKE_CASE=False				)     ->    Union[str, Any]:
							_lowercase							:   Dict 							=   Accelerator(
							    split_batches=SCREAMING_SNAKE_CASE     ,     dispatch_batches=SCREAMING_SNAKE_CASE     ,     gradient_accumulation_steps=2				)
							# Test that context manager behaves properly
							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   int 							=   get_training_setup(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							for iteration, batch in enumerate(SCREAMING_SNAKE_CASE				):
														_lowercase							,							_lowercase							:   str 							=   batch.values()
														# Gather the distributed inputs and targs for the base model
														_lowercase							,							_lowercase							:   Dict 							=   accelerator.gather((ddp_input, ddp_target)				)
														_lowercase							,							_lowercase							:   Tuple 							=   input.to(accelerator.device				), target.to(accelerator.device				)
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE				)):
																					if split_batches:
																												sched.step()
																					else:
																												for _ in range(accelerator.num_processes				):
																																			sched.step()
														opt.zero_grad()
														# Perform gradient accumulation under wrapper
														with accelerator.accumulate(SCREAMING_SNAKE_CASE				):
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
														_lowercase							:   Optional[int] 							=   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE				))
														if accelerator.num_processes > 1:
																					check_model_parameters(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration				)
							GradientState._reset_state()






def    __magic_name__    (	)     ->    Any:
							_lowercase							:   Any 							=   Accelerator()

							_lowercase							:   List[Any] 							=   RegressionDataset(length=80				)
							_lowercase							:   List[str] 							=   DataLoader(SCREAMING_SNAKE_CASE     ,     batch_size=16				)
							_lowercase							:   List[str] 							=   RegressionDataset(length=96				)
							_lowercase							:   Union[str, Any] 							=   DataLoader(SCREAMING_SNAKE_CASE     ,     batch_size=16				)
							_lowercase							,							_lowercase							:   Tuple 							=   accelerator.prepare(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(SCREAMING_SNAKE_CASE				):
														assert id(accelerator.gradient_state.active_dataloader				) == id(SCREAMING_SNAKE_CASE				)
														if iteration < len(SCREAMING_SNAKE_CASE				) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE				):
																																			assert id(accelerator.gradient_state.active_dataloader				) == id(SCREAMING_SNAKE_CASE				)
																																			if batch_num < len(SCREAMING_SNAKE_CASE				) - 1:
																																										assert not accelerator.gradient_state.end_of_dataloader
																																			else:
																																										assert accelerator.gradient_state.end_of_dataloader
														else:
																					assert accelerator.gradient_state.end_of_dataloader
							assert accelerator.gradient_state.active_dataloader is None






def    __magic_name__    (	)     ->    Dict:
							_lowercase							:   List[str] 							=   Accelerator()
							_lowercase							:   List[Any] 							=   accelerator.state
							if state.local_process_index == 0:
														print('**Test `accumulate` gradient accumulation with dataloader break**'				)
							test_dataloader_break()
							if state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print('**Test NOOP `no_sync` context manager**'				)
														test_noop_sync(SCREAMING_SNAKE_CASE				)
							if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
														if state.local_process_index == 0:
																					print('**Test Distributed `no_sync` context manager**'				)
														test_distributed_sync(SCREAMING_SNAKE_CASE				)
							if state.distributed_type == DistributedType.MULTI_GPU:
														for split_batch in [True, False]:
																					for dispatch_batches in [True, False]:
																												if state.local_process_index == 0:
																																			print(
																																			    '**Test `accumulate` gradient accumulation, '     ,     F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,     )
																												test_gradient_accumulation(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

    # Currently will break on torch 2.0 +, need to investigate why
							if is_torch_version('<'     ,     '2.0'				) or state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print(
																					    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,     '`split_batches=False`, `dispatch_batches=False`**'     ,     )
														test_gradient_accumulation_with_opt_and_scheduler()
														if state.distributed_type == DistributedType.MULTI_GPU:
																					for split_batch in [True, False]:
																												for dispatch_batches in [True, False]:
																																			if not split_batch and not dispatch_batches:
																																										continue
																																			if state.local_process_index == 0:
																																										print(
																																										    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,     F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,     )
																																			test_gradient_accumulation_with_opt_and_scheduler(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()







'''simple docstring'''

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

import black


SCREAMING_SNAKE_CASE							:				List[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.
SCREAMING_SNAKE_CASE							:				List[str]     	=			"    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    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir,     'models/bert/'       )       )
											SCREAMING_SNAKE_CASE_				=				self.transformer_dir
											shutil.copy(
											    os.path.join(_lowercase,     'src/transformers/models/bert/modeling_bert.py'       ),     os.path.join(self.transformer_dir,     'models/bert/modeling_bert.py'       ),     )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				'src/transformers'
											shutil.rmtree(self.transformer_dir       )



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



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'       )
											self.assertEqual(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Union[str, 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',     _lowercase,     )

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

											# Copy consistency with a really long name
											SCREAMING_SNAKE_CASE_				=				'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',     _lowercase,     _lowercase       ),     )

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





				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

											SCREAMING_SNAKE_CASE_				=				(
											    '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.'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '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'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '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'
											)

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

											self.assertFalse(_lowercase       )
											self.assertEqual(_lowercase,     _lowercase       )

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

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

											SCREAMING_SNAKE_CASE_				=				(
											    '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.'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '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'
											)
											SCREAMING_SNAKE_CASE_				=				(
											    '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'
											)

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

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





