





'''simple docstring'''







import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_lowercase       						=			16
_lowercase       						=			32






def    __UpperCamelCase	(	a    : Accelerator					,   a    : int = 16     ) ->str:
 snake_case				=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 snake_case				=			load_dataset('''glue'''					,   '''mrpc'''     )

 def tokenize_function(a    : Union[str, Any]     ):
  # max_length=None => use the model max length (it's actually the default)
  snake_case				=			tokenizer(examples['''sentence1''']					,   examples['''sentence2''']					,   truncation=a					,   max_length=a     )
  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():
  snake_case				=			datasets.map(
      a					,   batched=a					,   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
 snake_case				=			tokenized_datasets.rename_column('''label'''					,   '''labels'''     )

 def collate_fn(a    : int     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  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":
   snake_case				=			16
  elif accelerator.mixed_precision != "no":
   snake_case				=			8
  else:
   snake_case				=			None

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

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

 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

   _lowercase       						=			mocked_dataloaders  # noqa: F811






def    __UpperCamelCase	(	a    : List[str]					,   a    : int     ) ->int:
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,   a     ) == "1":
  snake_case				=			2
 # Initialize accelerator
 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
 snake_case				=			config['''lr''']
 snake_case				=			int(config['''num_epochs''']     )
 snake_case				=			int(config['''seed''']     )
 snake_case				=			int(config['''batch_size''']     )

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

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=a     )
 def inner_training_loop(a    : Tuple     ):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(a     )

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

  # 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).
  snake_case				=			model.to(accelerator.device     )

  # Instantiate optimizer
  snake_case				=			AdamW(params=model.parameters()					,   lr=a     )
  snake_case				,		snake_case				=			get_dataloaders(a					,   a     )

  # Instantiate scheduler
  snake_case				=			get_linear_schedule_with_warmup(
      optimizer=a					,   num_warmup_steps=100					,   num_training_steps=(len(a     ) * 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.
  snake_case				,		snake_case				,		snake_case				,		snake_case				,		snake_case				=			accelerator.prepare(
      a					,   a					,   a					,   a					,   a     )

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

   model.eval()
   for step, batch in enumerate(a     ):
    # 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(**a     )
    snake_case				=			outputs.logits.argmax(dim=-1     )
    snake_case				,		snake_case				=			accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
    metric.add_batch(
        predictions=a					,   references=a					,   )

   snake_case				=			metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"""epoch {epoch}:"""					,   a     )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()






def    __UpperCamelCase	(	) ->Any:
 snake_case				=			argparse.ArgumentParser(description='''Simple example of training script.'''     )
 parser.add_argument(
     '''--mixed_precision'''					,   type=a					,   default=a					,   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.'''     )
 snake_case				=			parser.parse_args()
 snake_case				=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(a					,   a     )


if __name__ == "__main__":
   main()








'''simple docstring'''







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

import yaml
class 			_lowercase						(       yaml.SafeLoader					):







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      snake_case				=			[self.constructed_objects[key_node] for key_node, _ in node.value]
      snake_case				=			[tuple(A__    ) if isinstance(A__    ,   A__    ) else key for key in keys]
      snake_case				=			Counter(A__    )
      snake_case				=			[key for key in counter if counter[key] > 1]
      if duplicate_keys:
       raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=False    ) ->	List[Any]:
      snake_case				=			super().construct_mapping(A__    ,   deep=A__    )
      self._check_no_duplicates_on_constructed_node(A__    )
      return mapping




def    __UpperCamelCase	(	a    : str     ) ->Tuple[Optional[str], str]:
 snake_case				=			list(readme_content.splitlines()     )
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  snake_case				=			full_content[1:].index('''---'''     ) + 1
  snake_case				=			'''\n'''.join(full_content[1:sep_idx]     )
  return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

 return None, "\n".join(a     )
class 			_lowercase						(       __a					):
     # class attributes
     _UpperCAmelCase										=					{'''train_eval_index'''}  # train-eval-index in the YAML metadata







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
       snake_case				,		snake_case				=			_split_yaml_from_readme(readme_file.read()    )
      if yaml_string is not None:
       return cls.from_yaml_string(A__    )
      else:
       return cls()







     def     UpperCamelCase			( self    ,   A__    ) ->	str:
      if path.exists():
       with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
        snake_case				=			readme_file.read()
      else:
       snake_case				=			None
      snake_case				=			self._to_readme(A__    )
      with open(A__    ,   '''w'''    ,   encoding='''utf-8'''    ) as readme_file:
       readme_file.write(A__    )







     def     UpperCamelCase			( self    ,   A__ = None    ) ->	str:
      if readme_content is not None:
       snake_case				,		snake_case				=			_split_yaml_from_readme(A__    )
       snake_case				=			'''---\n''' + self.to_yaml_string() + '''---\n''' + content
      else:
       snake_case				=			'''---\n''' + self.to_yaml_string() + '''---\n'''
      return full_content







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      snake_case				=			yaml.load(A__    ,   Loader=_NoDuplicateSafeLoader    ) or {}

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







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




_lowercase       						=			{
    '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

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

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

