
import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType



class    _snake_case (						__SCREAMING_SNAKE_CASE				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   data



 def __iter__(       self):


   '''simple docstring'''




   for element in self.data:
     yield element

def 		UpperCamelCase						(   lowercase_=True )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   Accelerator(even_batches=lowerCAmelCase_ )
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = False )		->						Optional[Any]:


  '''simple docstring'''






  if iterable:
    lowercase__	: Dict     		=   DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_ ) ) )
  else:
    lowercase__	: Dict     		=   TensorDataset(torch.as_tensor(range(lowerCAmelCase_ ) ) )

  lowercase__	: Optional[int]     		=   DataLoader(lowerCAmelCase_			,			batch_size=lowerCAmelCase_ )
  lowercase__	: str     		=   accelerator.prepare(lowerCAmelCase_ )

  return dl

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   create_dataloader(accelerator=lowerCAmelCase_			,			dataset_size=lowerCAmelCase_			,			batch_size=lowerCAmelCase_ )

  lowercase__	: str     		=   [len(batch[0] ) for batch in dl]

  if accelerator.process_index == 0:
    assert batch_sizes == process_0_expected_batch_sizes
  elif accelerator.process_index == 1:
    assert batch_sizes == process_1_expected_batch_sizes

def 		UpperCamelCase						(   )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Dict     		=   create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      lowerCAmelCase_			,			dataset_size=3			,			batch_size=1			,			process_0_expected_batch_sizes=[1, 1]			,			process_1_expected_batch_sizes=[1, 1]			,			)

  # without padding, we would expect the same number of batches, but different sizes
  verify_dataloader_batch_sizes(
      lowerCAmelCase_			,			dataset_size=7			,			batch_size=2			,			process_0_expected_batch_sizes=[2, 2]			,			process_1_expected_batch_sizes=[2, 2]			,			)

def 		UpperCamelCase						(   )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   create_accelerator(even_batches=lowerCAmelCase_ )

  verify_dataloader_batch_sizes(
      lowerCAmelCase_			,			dataset_size=3			,			batch_size=1			,			process_0_expected_batch_sizes=[1, 1]			,			process_1_expected_batch_sizes=[1]			,			)

  verify_dataloader_batch_sizes(
      lowerCAmelCase_			,			dataset_size=7			,			batch_size=2			,			process_0_expected_batch_sizes=[2, 2]			,			process_1_expected_batch_sizes=[2, 1]			,			)

def 		UpperCamelCase						(   )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   create_accelerator(even_batches=lowerCAmelCase_ )

  lowercase__	: Optional[int]     		=   torch.nn.Linear(1			,			1 )
  lowercase__	: Union[str, Any]     		=   accelerator.prepare(lowerCAmelCase_ )

  lowercase__	: Optional[Any]     		=   create_dataloader(lowerCAmelCase_			,			dataset_size=3			,			batch_size=1 )

  lowercase__	: Tuple     		=   []
  with accelerator.join_uneven_inputs([ddp_model] ):
    for batch_idx, batch in enumerate(lowerCAmelCase_ ):
      lowercase__	: Dict     		=   ddp_model(batch[0].float() )
      lowercase__	: Dict     		=   output.sum()
      loss.backward()
      batch_idxs.append(lowerCAmelCase_ )

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  with warnings.catch_warnings(record=lowerCAmelCase_ ) as w:
    with accelerator.join_uneven_inputs([Mock()] ):
      pass

    assert issubclass(w[-1].category			,			lowerCAmelCase_ )
    assert "only supported for multi-GPU" in str(w[-1].message )

def 		UpperCamelCase						(   )		->						int:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   True
  lowercase__	: Dict     		=   False
  lowercase__	: List[Any]     		=   create_accelerator(even_batches=lowerCAmelCase_ )
  lowercase__	: Optional[int]     		=   torch.nn.Linear(1			,			1 )
  lowercase__	: int     		=   accelerator.prepare(lowerCAmelCase_ )
  lowercase__	: str     		=   create_dataloader(lowerCAmelCase_			,			dataset_size=3			,			batch_size=1 )
  lowercase__	: Tuple     		=   create_dataloader(lowerCAmelCase_			,			dataset_size=3			,			batch_size=1 )

  with accelerator.join_uneven_inputs([ddp_model]			,			even_batches=lowerCAmelCase_ ):
    lowercase__	: List[str]     		=   train_dl.batch_sampler.even_batches
    lowercase__	: Tuple     		=   valid_dl.batch_sampler.even_batches

  assert train_dl_overridden_value == overridden_even_batches
  assert valid_dl_overridden_value == overridden_even_batches
  assert train_dl.batch_sampler.even_batches == default_even_batches
  assert valid_dl.batch_sampler.even_batches == default_even_batches

def 		UpperCamelCase						(   )		->						Dict:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   True
  lowercase__	: Any     		=   False
  lowercase__	: Dict     		=   create_accelerator(even_batches=lowerCAmelCase_ )
  lowercase__	: str     		=   torch.nn.Linear(1			,			1 )
  lowercase__	: Union[str, Any]     		=   accelerator.prepare(lowerCAmelCase_ )
  create_dataloader(lowerCAmelCase_			,			dataset_size=3			,			batch_size=1			,			iterable=lowerCAmelCase_ )
  lowercase__	: Any     		=   create_dataloader(lowerCAmelCase_			,			dataset_size=3			,			batch_size=1 )

  with warnings.catch_warnings():
    warnings.filterwarnings("""ignore""" )
    try:
      with accelerator.join_uneven_inputs([ddp_model]			,			even_batches=lowerCAmelCase_ ):
        lowercase__	: List[str]     		=   batch_dl.batch_sampler.even_batches
    except AttributeError:
      # ensure attribute error is not raised when processing iterable dl
      raise AssertionError

  assert batch_dl_overridden_value == overridden_even_batches
  assert batch_dl.batch_sampler.even_batches == default_even_batches

def 		UpperCamelCase						(   )		->						Tuple:


  '''simple docstring'''






  lowercase__	: List[str]     		=   create_accelerator()
  lowercase__	: Optional[Any]     		=   torch.nn.Linear(1			,			1 )
  lowercase__	: Tuple     		=   accelerator.prepare(lowerCAmelCase_ )
  create_dataloader(lowerCAmelCase_			,			dataset_size=3			,			batch_size=1			,			iterable=lowerCAmelCase_ )

  with warnings.catch_warnings(record=lowerCAmelCase_ ) as w:
    with accelerator.join_uneven_inputs([ddp_model]			,			even_batches=lowerCAmelCase_ ):
      pass

    assert issubclass(w[-1].category			,			lowerCAmelCase_ )
    assert "only supported for map-style datasets" in str(w[-1].message )

def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   create_accelerator()

  accelerator.print("""Test that even_batches variable ensures uniform batches across processes""" )
  test_default_ensures_even_batch_sizes()

  accelerator.print("""Run tests with even_batches disabled""" )
  test_can_disable_even_batches()

  accelerator.print("""Test joining uneven inputs""" )
  test_can_join_uneven_inputs()

  accelerator.print("""Test overriding even_batches when joining uneven inputs""" )
  test_join_can_override_even_batches()

  accelerator.print("""Test overriding even_batches for mixed dataloader types""" )
  test_join_can_override_for_mixed_type_dataloaders()

  accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders""" )
  test_join_raises_warning_for_iterable_when_overriding_even_batches()

  accelerator.print("""Test join with non DDP distributed raises warning""" )
  lowercase__	: Dict     		=   accelerator.state.distributed_type
  lowercase__	: str     		=   DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase_ )
  lowercase__	: Dict     		=   original_state


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    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      [self.constructed_objects[key_node] for key_node, _ in node.value]
     _snake_case   	=      [tuple(__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else key for key in keys]
     _snake_case   	=      Counter(__lowerCamelCase				)
     _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    :	str							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Optional[Any]=False				):



     """simple docstring"""
     _snake_case   	=      super().construct_mapping(__lowerCamelCase							,       deep=__lowerCamelCase				)
     self._check_no_duplicates_on_constructed_node(__lowerCamelCase				)
     return mapping




def 			snake_case		(		lowerCAmelCase_	)						->				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(lowerCAmelCase_	)



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   # class attributes
   A__		:	int           =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata



   @classmethod
   def    __UpperCAmelCase    (       cls    :	str							,       __lowerCamelCase    :	Path				):



     """simple docstring"""
     with open(__lowerCamelCase							,       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(__lowerCamelCase				)
     else:
       return cls()



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Path				):



     """simple docstring"""
     if path.exists():
       with open(__lowerCamelCase							,       encoding='''utf-8'''				) as readme_file:
         _snake_case   	=      readme_file.read()
     else:
       _snake_case   	=      None
     _snake_case   	=      self._to_readme(__lowerCamelCase				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as readme_file:
       readme_file.write(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if readme_content is not None:
       _snake_case	,     _snake_case   	=      _split_yaml_from_readme(__lowerCamelCase				)
       _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    :	Tuple							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      yaml.load(__lowerCamelCase							,       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(**__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Dict				):



     """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=__lowerCamelCase							,       allow_unicode=__lowerCamelCase							,       encoding='''utf-8'''							,       ).decode('''utf-8'''				)

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

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

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





