"""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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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    	=16
__SCREAMING_SNAKE_CASE    	=32



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Accelerator     ,			__SCREAMING_SNAKE_CASE							:     int = 16      ):
     lowercase_	:				List[Any]													=	AutoTokenizer.from_pretrained('bert-base-cased'      )
     lowercase_	:				str													=	load_dataset('glue'     ,			'mrpc'      )

     def tokenize_function(__SCREAMING_SNAKE_CASE							:     Dict      ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_	:				Dict													=	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
     # starting with the main process first:
     with accelerator.main_process_first():
          lowercase_	:				int													=	datasets.map(
              __SCREAMING_SNAKE_CASE     ,			batched=__SCREAMING_SNAKE_CASE     ,			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
     lowercase_	:				int													=	tokenized_datasets.rename_column('label'     ,			'labels'      )

     def collate_fn(__SCREAMING_SNAKE_CASE							:     Dict      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowercase_	:				List[str]													=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               lowercase_	:				List[Any]													=	16
          elif accelerator.mixed_precision != "no":
               lowercase_	:				int													=	8
          else:
               lowercase_	:				Tuple													=	None

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

     # Instantiate dataloaders.
     lowercase_	:				Any													=	DataLoader(
         tokenized_datasets['train']     ,			shuffle=__SCREAMING_SNAKE_CASE     ,			collate_fn=__SCREAMING_SNAKE_CASE     ,			batch_size=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	DataLoader(
         tokenized_datasets['validation']     ,			shuffle=__SCREAMING_SNAKE_CASE     ,			collate_fn=__SCREAMING_SNAKE_CASE     ,			batch_size=__SCREAMING_SNAKE_CASE      )

     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    	=mocked_dataloaders  # noqa: F811



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Dict      ):
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,			__SCREAMING_SNAKE_CASE      ) == "1":
          lowercase_	:				Dict													=	2
     # New Code #
     lowercase_	:				Optional[Any]													=	int(args.gradient_accumulation_steps      )
     lowercase_	:				Dict													=	int(args.local_sgd_steps      )
     # Initialize accelerator
     lowercase_	:				Tuple													=	Accelerator(
         cpu=args.cpu     ,			mixed_precision=args.mixed_precision     ,			gradient_accumulation_steps=__SCREAMING_SNAKE_CASE      )
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'      )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_	:				int													=	config['lr']
     lowercase_	:				Optional[Any]													=	int(config['num_epochs']      )
     lowercase_	:				Any													=	int(config['seed']      )
     lowercase_	:				Optional[int]													=	int(config['batch_size']      )

     lowercase_	:				List[str]													=	evaluate.load('glue'     ,			'mrpc'      )

     set_seed(__SCREAMING_SNAKE_CASE      )
     lowercase_							,  lowercase_	:				Tuple													=	get_dataloaders(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_	:				List[Any]													=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,			return_dict=__SCREAMING_SNAKE_CASE      )

     # 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).
     lowercase_	:				List[str]													=	model.to(accelerator.device      )

     # Instantiate optimizer
     lowercase_	:				List[Any]													=	AdamW(params=model.parameters()     ,			lr=__SCREAMING_SNAKE_CASE      )

     # Instantiate scheduler
     lowercase_	:				Union[str, Any]													=	get_linear_schedule_with_warmup(
         optimizer=__SCREAMING_SNAKE_CASE     ,			num_warmup_steps=1_00     ,			num_training_steps=(len(__SCREAMING_SNAKE_CASE      ) * num_epochs)     ,			)

     # 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.
     lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				List[Any]													=	accelerator.prepare(
         __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # Now we train the model
     for epoch in range(__SCREAMING_SNAKE_CASE      ):
          model.train()
          with LocalSGD(
              accelerator=__SCREAMING_SNAKE_CASE     ,			model=__SCREAMING_SNAKE_CASE     ,			local_sgd_steps=__SCREAMING_SNAKE_CASE     ,			enabled=local_sgd_steps is not None      ) as local_sgd:
               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      )
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(__SCREAMING_SNAKE_CASE      ):
                         lowercase_	:				List[str]													=	model(**__SCREAMING_SNAKE_CASE      )
                         lowercase_	:				Any													=	output.loss
                         accelerator.backward(__SCREAMING_SNAKE_CASE      )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          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():
                    lowercase_	:				Dict													=	model(**__SCREAMING_SNAKE_CASE      )
               lowercase_	:				Union[str, Any]													=	outputs.logits.argmax(dim=-1      )
               lowercase_							,  lowercase_	:				str													=	accelerator.gather_for_metrics((predictions, batch['labels'])      )
               metric.add_batch(
                   predictions=__SCREAMING_SNAKE_CASE     ,			references=__SCREAMING_SNAKE_CASE     ,			)

          lowercase_	:				int													=	metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''     ,			__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       ):
     lowercase_	:				Any													=	argparse.ArgumentParser(description='Simple example of training script.'      )
     parser.add_argument(
         '--mixed_precision'     ,			type=__SCREAMING_SNAKE_CASE     ,			default=__SCREAMING_SNAKE_CASE     ,			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.'     ,			)
     # New Code #
     parser.add_argument(
         '--gradient_accumulation_steps'     ,			type=__SCREAMING_SNAKE_CASE     ,			default=1     ,			help='The number of minibatches to be ran before gradients are accumulated.'     ,			)
     parser.add_argument(
         '--local_sgd_steps'     ,			type=__SCREAMING_SNAKE_CASE     ,			default=8     ,			help='Number of local SGD steps or None to disable local SGD'      )
     parser.add_argument('--cpu'     ,			action='store_true'     ,			help='If passed, will train on the CPU.'      )
     lowercase_	:				List[Any]													=	parser.parse_args()
     lowercase_	:				List[Any]													=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     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)

