"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE__					  =     16
SCREAMING_SNAKE_CASE__					  =     32





def       lowerCAmelCase__			(	_UpperCamelCase				: Accelerator       ,	_UpperCamelCase				: int = 1_6       ,	_UpperCamelCase				: str = "bert-base-cased"    )    -> Any:





						"""simple docstring"""






						snake_case	    =	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
						snake_case	    =	load_dataset('glue'       ,	'mrpc'    )

						def tokenize_function(_UpperCamelCase				: int    ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case	    =	tokenizer(examples['sentence1']       ,	examples['sentence2']       ,	truncation=__SCREAMING_SNAKE_CASE       ,	max_length=__SCREAMING_SNAKE_CASE    )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						snake_case	    =	datasets.map(
						    __SCREAMING_SNAKE_CASE       ,	batched=__SCREAMING_SNAKE_CASE       ,	remove_columns=['idx', 'sentence1', 'sentence2']       ,	load_from_cache_file=__SCREAMING_SNAKE_CASE    )

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

						def collate_fn(_UpperCamelCase				: int    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(__SCREAMING_SNAKE_CASE       ,	padding='max_length'       ,	max_length=1_2_8       ,	return_tensors='pt'    )
												return tokenizer.pad(__SCREAMING_SNAKE_CASE       ,	padding='longest'       ,	return_tensors='pt'    )

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

						return train_dataloader, eval_dataloader





def       lowerCAmelCase__			(	_UpperCamelCase				: Optional[int]       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: int    )    -> Tuple:





						"""simple docstring"""






						model.eval()
						snake_case	    =	0
						for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												with torch.no_grad():
																		snake_case	    =	model(**__SCREAMING_SNAKE_CASE    )
												snake_case	    =	outputs.logits.argmax(dim=-1    )
												# It is slightly faster to call this once, than multiple times
												snake_case	    =	accelerator.gather(
												    (predictions, batch['labels'])    )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(__SCREAMING_SNAKE_CASE    ) - 1:
																								snake_case	    =	predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																								snake_case	    =	references[: len(eval_dataloader.dataset    ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=__SCREAMING_SNAKE_CASE       ,	references=__SCREAMING_SNAKE_CASE       ,	)

						snake_case	    =	metric.compute()
						return eval_metric["accuracy"]





def       lowerCAmelCase__			(	_UpperCamelCase				: Optional[int]       ,	_UpperCamelCase				: Optional[Any]    )    -> Dict:





						"""simple docstring"""






						snake_case	    =	Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case	    =	config['lr']
						snake_case	    =	int(config['num_epochs']    )
						snake_case	    =	int(config['seed']    )
						snake_case	    =	int(config['batch_size']    )
						snake_case	    =	args.model_name_or_path

						set_seed(__SCREAMING_SNAKE_CASE    )
						snake_case	    =	get_dataloaders(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case	    =	AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE       ,	return_dict=__SCREAMING_SNAKE_CASE    )

						# Instantiate optimizer
						snake_case	    =	(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						snake_case	    =	optimizer_cls(params=model.parameters()       ,	lr=__SCREAMING_SNAKE_CASE    )

						if accelerator.state.deepspeed_plugin is not None:
												snake_case	    =	accelerator.state.deepspeed_plugin.deepspeed_config[
												    'gradient_accumulation_steps'
												]
						else:
												snake_case	    =	1
						snake_case	    =	(len(__SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												snake_case	    =	get_linear_schedule_with_warmup(
												    optimizer=__SCREAMING_SNAKE_CASE       ,	num_warmup_steps=0       ,	num_training_steps=__SCREAMING_SNAKE_CASE       ,	)
						else:
												snake_case	    =	DummyScheduler(__SCREAMING_SNAKE_CASE       ,	total_num_steps=__SCREAMING_SNAKE_CASE       ,	warmup_num_steps=0    )

						# 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.
						snake_case	    =	accelerator.prepare(
						    __SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )

						# We need to keep track of how many total steps we have iterated over
						snake_case	    =	0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case	    =	0
						snake_case	    =	evaluate.load('glue'       ,	'mrpc'    )
						snake_case	    =	num_epochs

						if args.partial_train_epoch is not None:
												snake_case	    =	args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint    )
												snake_case	    =	args.resume_from_checkpoint.split('epoch_'    )[1]
												snake_case	    =	''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												snake_case	    =	int(__SCREAMING_SNAKE_CASE    ) + 1
												snake_case	    =	evaluation_loop(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )
												accelerator.print('resumed checkpoint performance:'       ,	__SCREAMING_SNAKE_CASE    )
												accelerator.print('resumed checkpoint\'s scheduler\'s lr:'       ,	lr_scheduler.get_lr()[0]    )
												accelerator.print('resumed optimizers\'s lr:'       ,	optimizer.param_groups[0]['lr']    )
												with open(os.path.join(args.output_dir       ,	f"""state_{starting_epoch-1}.json"""    )       ,	'r'    ) as f:
																		snake_case	    =	json.load(__SCREAMING_SNAKE_CASE    )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						snake_case	    =	{}
						for epoch in range(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    ):
												model.train()
												for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
																		snake_case	    =	model(**__SCREAMING_SNAKE_CASE    )
																		snake_case	    =	outputs.loss
																		snake_case	    =	loss / gradient_accumulation_steps
																		accelerator.backward(__SCREAMING_SNAKE_CASE    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												snake_case	    =	f"""epoch_{epoch}"""
												snake_case	    =	os.path.join(args.output_dir       ,	__SCREAMING_SNAKE_CASE    )
												accelerator.save_state(__SCREAMING_SNAKE_CASE    )
												snake_case	    =	evaluation_loop(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )
												snake_case	    =	accuracy
												snake_case	    =	lr_scheduler.get_lr()[0]
												snake_case	    =	optimizer.param_groups[0]['lr']
												snake_case	    =	epoch
												snake_case	    =	overall_step
												accelerator.print(f"""epoch {epoch}:"""       ,	__SCREAMING_SNAKE_CASE    )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir       ,	f"""state_{epoch}.json"""    )       ,	'w'    ) as f:
																								json.dump(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )





def       lowerCAmelCase__			(	)    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
						parser.add_argument(
						    '--model_name_or_path'       ,	type=__SCREAMING_SNAKE_CASE       ,	default='bert-base-cased'       ,	help='Path to pretrained model or model identifier from huggingface.co/models.'       ,	required=__SCREAMING_SNAKE_CASE       ,	)
						parser.add_argument(
						    '--output_dir'       ,	type=__SCREAMING_SNAKE_CASE       ,	default='.'       ,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'       ,	)
						parser.add_argument(
						    '--resume_from_checkpoint'       ,	type=__SCREAMING_SNAKE_CASE       ,	default=__SCREAMING_SNAKE_CASE       ,	help='If the training should continue from a checkpoint folder.'       ,	)
						parser.add_argument(
						    '--partial_train_epoch'       ,	type=__SCREAMING_SNAKE_CASE       ,	default=__SCREAMING_SNAKE_CASE       ,	help='If passed, the training will stop after this number of epochs.'       ,	)
						parser.add_argument(
						    '--num_epochs'       ,	type=__SCREAMING_SNAKE_CASE       ,	default=2       ,	help='Number of train epochs.'       ,	)
						snake_case	    =	parser.parse_args()
						snake_case	    =	{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

						training_function(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
					main()



"""simple docstring"""







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml







class        UpperCamelCase		(			yaml.SafeLoader  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				str													=	[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowercase_	:				str													=	[tuple(__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else key for key in keys]
        lowercase_	:				List[Any]													=	Counter(__UpperCamelCase			)
        lowercase_	:				str													=	[key for key in counter if counter[key] > 1]
        if duplicate_keys:
             raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().construct_mapping(__UpperCamelCase     ,deep=__UpperCamelCase			)
        self._check_no_duplicates_on_constructed_node(__UpperCamelCase			)
        return mapping






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	list(readme_content.splitlines()      )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          lowercase_	:				Dict													=	full_content[1:].index('---'      ) + 1
          lowercase_	:				Optional[int]													=	'\n'.join(full_content[1:sep_idx]      )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

     return None, "\n".join(__SCREAMING_SNAKE_CASE      )







class        UpperCamelCase		(			lowercase_  ):
   # class attributes
   lowercase							=  {'train_eval_index'}  # train-eval-index in the YAML metadata
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
             lowercase_							,  lowercase_	:				Optional[int]													=	_split_yaml_from_readme(readme_file.read()			)
        if yaml_string is not None:
             return cls.from_yaml_string(__UpperCamelCase			)
        else:
             return cls()
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if path.exists():
             with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
                  lowercase_	:				Dict													=	readme_file.read()
        else:
             lowercase_	:				int													=	None
        lowercase_	:				Any													=	self._to_readme(__UpperCamelCase			)
        with open(__UpperCamelCase     ,'w'     ,encoding='utf-8'			) as readme_file:
             readme_file.write(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      str:


        '''simple docstring'''


        if readme_content is not None:
             lowercase_							,  lowercase_	:				Optional[Any]													=	_split_yaml_from_readme(__UpperCamelCase			)
             lowercase_	:				Optional[Any]													=	'---\n' + self.to_yaml_string() + '---\n' + content
        else:
             lowercase_	:				Tuple													=	'---\n' + self.to_yaml_string() + '---\n'
        return full_content
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        lowercase_	:				List[str]													=	yaml.load(__UpperCamelCase     ,Loader=_NoDuplicateSafeLoader			) or {}

        # Convert the YAML keys to DatasetMetadata fields
        lowercase_	:				Dict													=	{
            (key.replace('-'     ,'_'			) if key.replace('-'     ,'_'			) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return yaml.safe_dump(
            {
                (key.replace('_'     ,'-'			) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }     ,sort_keys=__UpperCamelCase     ,allow_unicode=__UpperCamelCase     ,encoding='utf-8'     ,).decode('utf-8'			)






__SCREAMING_SNAKE_CASE    	={
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
       from argparse import ArgumentParser

       __SCREAMING_SNAKE_CASE    	=ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
       ap.add_argument("readme_filepath")
       __SCREAMING_SNAKE_CASE    	=ap.parse_args()

       __SCREAMING_SNAKE_CASE    	=Path(args.readme_filepath)
       __SCREAMING_SNAKE_CASE    	=DatasetMetadata.from_readme(readme_filepath)
       print(dataset_metadata)
       dataset_metadata.to_readme(readme_filepath)

