

'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			16
SCREAMING_SNAKE_CASE							:				str     	=			32




def 							_UpperCamelCase				(   lowerCAmelCase__:					Accelerator		,lowerCAmelCase__:					int = 16   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained('bert-base-cased'   )
							SCREAMING_SNAKE_CASE_				=				load_dataset('glue'		,'mrpc'   )

							def tokenize_function(lowerCAmelCase__:					List[Any]   ):
														# max_length=None => use the model max length (it's actually the default)
														SCREAMING_SNAKE_CASE_				=				tokenizer(examples['sentence1']		,examples['sentence2']		,truncation=lowerCAmelCase__		,max_length=lowerCAmelCase__   )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														SCREAMING_SNAKE_CASE_				=				datasets.map(
														    lowerCAmelCase__		,batched=lowerCAmelCase__		,remove_columns=['idx', 'sentence1', 'sentence2']		,)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							SCREAMING_SNAKE_CASE_				=				tokenized_datasets.rename_column('label'		,'labels'   )

							def collate_fn(lowerCAmelCase__:					List[str]   ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														SCREAMING_SNAKE_CASE_				=				128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					SCREAMING_SNAKE_CASE_				=				16
														elif accelerator.mixed_precision != "no":
																					SCREAMING_SNAKE_CASE_				=				8
														else:
																					SCREAMING_SNAKE_CASE_				=				None

														return tokenizer.pad(
														    lowerCAmelCase__		,padding='longest'		,max_length=lowerCAmelCase__		,pad_to_multiple_of=lowerCAmelCase__		,return_tensors='pt'		,)

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_				=				DataLoader(
							    tokenized_datasets['train']		,shuffle=lowerCAmelCase__		,collate_fn=lowerCAmelCase__		,batch_size=lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				DataLoader(
							    tokenized_datasets['validation']		,shuffle=lowerCAmelCase__		,collate_fn=lowerCAmelCase__		,batch_size=lowerCAmelCase__   )

							return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			mocked_dataloaders  # noqa: F811




def 							_UpperCamelCase				(   lowerCAmelCase__:					Any		,lowerCAmelCase__:					Any   )      -> Optional[int]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'		,lowerCAmelCase__   ) == "1":
														SCREAMING_SNAKE_CASE_				=				2
							# Initialize accelerator
							SCREAMING_SNAKE_CASE_				=				Accelerator(cpu=args.cpu		,mixed_precision=args.mixed_precision   )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_				=				config['lr']
							SCREAMING_SNAKE_CASE_				=				int(config['num_epochs']   )
							SCREAMING_SNAKE_CASE_				=				int(config['seed']   )
							SCREAMING_SNAKE_CASE_				=				int(config['batch_size']   )

							SCREAMING_SNAKE_CASE_				=				evaluate.load('glue'		,'mrpc'   )

							# If the batch size is too big we use gradient accumulation
							SCREAMING_SNAKE_CASE_				=				1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														SCREAMING_SNAKE_CASE_				=				batch_size // MAX_GPU_BATCH_SIZE
														SCREAMING_SNAKE_CASE_				=				MAX_GPU_BATCH_SIZE

							set_seed(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				get_dataloaders(lowerCAmelCase__		,lowerCAmelCase__   )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_				=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,return_dict=lowerCAmelCase__   )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							SCREAMING_SNAKE_CASE_				=				model.to(accelerator.device   )

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_				=				AdamW(params=model.parameters()		,lr=lowerCAmelCase__   )

							# Instantiate scheduler
							SCREAMING_SNAKE_CASE_				=				get_linear_schedule_with_warmup(
							    optimizer=lowerCAmelCase__		,num_warmup_steps=100		,num_training_steps=(len(lowerCAmelCase__   ) * num_epochs) // gradient_accumulation_steps		,)

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				accelerator.prepare(
							    lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )

							# Now we train the model
							for epoch in range(lowerCAmelCase__   ):
														model.train()
														for step, batch in enumerate(lowerCAmelCase__   ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device   )
																					SCREAMING_SNAKE_CASE_				=				model(**lowerCAmelCase__   )
																					SCREAMING_SNAKE_CASE_				=				outputs.loss
																					SCREAMING_SNAKE_CASE_				=				loss / gradient_accumulation_steps
																					accelerator.backward(lowerCAmelCase__   )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														SCREAMING_SNAKE_CASE_				=				0
														for step, batch in enumerate(lowerCAmelCase__   ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device   )
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE_				=				model(**lowerCAmelCase__   )
																					SCREAMING_SNAKE_CASE_				=				outputs.logits.argmax(dim=-1   )
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				accelerator.gather((predictions, batch['labels'])   )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(lowerCAmelCase__   ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			SCREAMING_SNAKE_CASE_				=				predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																																			SCREAMING_SNAKE_CASE_				=				references[: len(eval_dataloader.dataset   ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=lowerCAmelCase__		,references=lowerCAmelCase__		,)

														SCREAMING_SNAKE_CASE_				=				metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""		,lowerCAmelCase__   )




def 							_UpperCamelCase				(   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser(description='Simple example of training script.'   )
							parser.add_argument(
							    '--mixed_precision'		,type=lowerCAmelCase__		,default=lowerCAmelCase__		,choices=['no', 'fp16', 'bf16', 'fp8']		,help='Whether to use mixed precision. Choose'
							    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
							    'and an Nvidia Ampere GPU.'		,)
							parser.add_argument('--cpu'		,action='store_true'		,help='If passed, will train on the CPU.'   )
							SCREAMING_SNAKE_CASE_				=				parser.parse_args()
							SCREAMING_SNAKE_CASE_				=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(lowerCAmelCase__		,lowerCAmelCase__   )


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       )





