


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
UpperCAmelCase__     			=							'\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
UpperCAmelCase__     			=							'\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
UpperCAmelCase__     			=							''

# TODO: Add the licence for the dataset here if you can find it
UpperCAmelCase__     			=							''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
UpperCAmelCase__     			=							{
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}







class    lowerCamelCase__     ( datasets.GeneratorBasedBuilder):
					SCREAMING_SNAKE_CASE__       =    datasets.Version('''1.1.0''')

					# This is an example of a dataset with multiple configurations.
					# If you don't want/need to define several sub-sets in your dataset,
					# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

					# If you need to make complex sub-parts in the datasets with configurable options
					# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
					# BUILDER_CONFIG_CLASS = MyBuilderConfig

					# You will be able to load one or the other configurations in the following list with
					# data = datasets.load_dataset('my_dataset', 'first_domain')
					# data = datasets.load_dataset('my_dataset', 'second_domain')
					SCREAMING_SNAKE_CASE__       =    [
					    datasets.BuilderConfig(name='''first_domain'''     ,							version=A__     ,							description='''This part of my dataset covers a first domain'''),
					    datasets.BuilderConfig(name='''second_domain'''     ,							version=A__     ,							description='''This part of my dataset covers a second domain'''),
					]

					SCREAMING_SNAKE_CASE__       =    '''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.





					def   __A      (self )  ->     Tuple:
						# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
						if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							_lowercase							   =datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string''' ),
							        '''option1''': datasets.Value('''string''' ),
							        '''answer''': datasets.Value('''string''' )
							        # These are the features of your dataset like images, labels ...
							    } )
						else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							_lowercase							   =datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string''' ),
							        '''option2''': datasets.Value('''string''' ),
							        '''second_domain_answer''': datasets.Value('''string''' )
							        # These are the features of your dataset like images, labels ...
							    } )
						return datasets.DatasetInfo(
						    # This is the description that will appear on the datasets page.
						    description=_DESCRIPTION	,  features=_lowerCAmelCase	,  homepage=_HOMEPAGE	,  license=_LICENSE	,  citation=_CITATION	,  )





					def   __A      (self	,  UpperCAmelCase )  ->     Union[str, Any]:
						# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
						# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

						# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
						# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
						# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
						_lowercase							   =_URLS[self.config.name]
						_lowercase							   =dl_manager.download_and_extract(_lowerCAmelCase )
						return [
						    datasets.SplitGenerator(
						        name=datasets.Split.TRAIN	,  gen_kwargs={
						            '''filepath''': os.path.join(_lowerCAmelCase	,  '''train.jsonl''' ),
						            '''split''': '''train''',
						        }	,  ),
						    datasets.SplitGenerator(
						        name=datasets.Split.VALIDATION	,  gen_kwargs={
						            '''filepath''': os.path.join(_lowerCAmelCase	,  '''dev.jsonl''' ),
						            '''split''': '''dev''',
						        }	,  ),
						    datasets.SplitGenerator(
						        name=datasets.Split.TEST	,  gen_kwargs={
						            '''filepath''': os.path.join(_lowerCAmelCase	,  '''test.jsonl''' ),
						            '''split''': '''test'''
						        }	,  ),
						]





					def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase )  ->     str:
						# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
						# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
						with open(_lowerCAmelCase	,  encoding='''utf-8''' ) as f:
							for key, row in enumerate(_lowerCAmelCase ):
								_lowercase							   =json.loads(_lowerCAmelCase )
								if self.config.name == "first_domain":
									# Yields examples as (key, example) tuples
									yield key, {
									    "sentence": data["sentence"],
									    "option1": data["option1"],
									    "answer": "" if split == "test" else data["answer"],
									}
								else:
									yield key, {
									    "sentence": data["sentence"],
									    "option2": data["option2"],
									    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
									}





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

