





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 				SCREAMING_SNAKE_CASE   (lowerCAmelCase__      ):
   _UpperCamelCase    :		Optional[int] 					=       (EulerAncestralDiscreteScheduler,)
   _UpperCamelCase    :		List[Any] 					=       10


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict			,     **a			:				str       )->   int:





     """simple docstring"""







     lowercase__				=  {
         'num_train_timesteps': 1_100,
         'beta_start': 0.0001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
     }

     config.update(**_lowerCamelCase       )
     return config


   def        SCREAMING_SNAKE_CASE_							(    self			:				Tuple       )->   Union[str, Any]:





     """simple docstring"""







     for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   Union[str, Any]:





     """simple docstring"""







     for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			,     [0.0002, 0.002, 0.02]       ):
       self.check_over_configs(beta_start=_lowerCamelCase			,     beta_end=_lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				str       )->   Optional[int]:





     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=_lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				int       )->   str:





     """simple docstring"""







     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=_lowerCamelCase       )


   def        SCREAMING_SNAKE_CASE_							(    self			:				Any       )->   List[Any]:





     """simple docstring"""







     lowercase__				=  self.scheduler_classes[0]
     lowercase__				=  self.get_scheduler_config()
     lowercase__				=  scheduler_class(**_lowerCamelCase       )

     scheduler.set_timesteps(self.num_inference_steps       )

     lowercase__				=  torch.manual_seed(0       )

     lowercase__				=  self.dummy_model()
     lowercase__				=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     lowercase__				=  sample.to(_lowerCamelCase       )

     for i, t in enumerate(scheduler.timesteps       ):
       lowercase__				=  scheduler.scale_model_input(_lowerCamelCase			,     _lowerCamelCase       )

       lowercase__				=  model(_lowerCamelCase			,     _lowerCamelCase       )

       lowercase__				=  scheduler.step(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     generator=_lowerCamelCase       )
       lowercase__				=  output.prev_sample

     lowercase__				=  torch.sum(torch.abs(_lowerCamelCase       )       )
     lowercase__				=  torch.mean(torch.abs(_lowerCamelCase       )       )

     assert abs(result_sum.item() - 152.3_192       ) < 1E-2
     assert abs(result_mean.item() - 0.1983       ) < 1E-3


   def        SCREAMING_SNAKE_CASE_							(    self			:				List[Any]       )->   List[Any]:





     """simple docstring"""







     lowercase__				=  self.scheduler_classes[0]
     lowercase__				=  self.get_scheduler_config(prediction_type='v_prediction'       )
     lowercase__				=  scheduler_class(**_lowerCamelCase       )

     scheduler.set_timesteps(self.num_inference_steps       )

     lowercase__				=  torch.manual_seed(0       )

     lowercase__				=  self.dummy_model()
     lowercase__				=  self.dummy_sample_deter * scheduler.init_noise_sigma
     lowercase__				=  sample.to(_lowerCamelCase       )

     for i, t in enumerate(scheduler.timesteps       ):
       lowercase__				=  scheduler.scale_model_input(_lowerCamelCase			,     _lowerCamelCase       )

       lowercase__				=  model(_lowerCamelCase			,     _lowerCamelCase       )

       lowercase__				=  scheduler.step(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     generator=_lowerCamelCase       )
       lowercase__				=  output.prev_sample

     lowercase__				=  torch.sum(torch.abs(_lowerCamelCase       )       )
     lowercase__				=  torch.mean(torch.abs(_lowerCamelCase       )       )

     assert abs(result_sum.item() - 108.4_439       ) < 1E-2
     assert abs(result_mean.item() - 0.1412       ) < 1E-3


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[int]       )->   Any:





     """simple docstring"""







     lowercase__				=  self.scheduler_classes[0]
     lowercase__				=  self.get_scheduler_config()
     lowercase__				=  scheduler_class(**_lowerCamelCase       )

     scheduler.set_timesteps(self.num_inference_steps			,     device=_lowerCamelCase       )
     lowercase__				=  torch.manual_seed(0       )

     lowercase__				=  self.dummy_model()
     lowercase__				=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     lowercase__				=  sample.to(_lowerCamelCase       )

     for t in scheduler.timesteps:
       lowercase__				=  scheduler.scale_model_input(_lowerCamelCase			,     _lowerCamelCase       )

       lowercase__				=  model(_lowerCamelCase			,     _lowerCamelCase       )

       lowercase__				=  scheduler.step(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     generator=_lowerCamelCase       )
       lowercase__				=  output.prev_sample

     lowercase__				=  torch.sum(torch.abs(_lowerCamelCase       )       )
     lowercase__				=  torch.mean(torch.abs(_lowerCamelCase       )       )

     assert abs(result_sum.item() - 152.3_192       ) < 1E-2
     assert abs(result_mean.item() - 0.1983       ) < 1E-3









'''simple docstring'''



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
_SCREAMING_SNAKE_CASE    =			'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

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

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

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

# 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)
_SCREAMING_SNAKE_CASE    =			{
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				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')
	a			: List[Any]      =				[
	    datasets.BuilderConfig(name="first_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a first domain"  ),
	    datasets.BuilderConfig(name="second_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a second domain"  ),
	]

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


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				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   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__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   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				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"],
													}





