



"""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
A_	:						Optional[Any]				=			'\\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
A_	:						str				=			'\\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
A_	:						Dict				=			''

# TODO: Add the licence for the dataset here if you can find it
A_	:						List[Any]				=			''

# 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)
A_	:						Any				=			{
    '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   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   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  :		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'''   ),
	]

	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   '''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.





	def  _lowerCAmelCase    (			self							:					int						)	->  Dict:





					"""simple docstring"""


					if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
									SCREAMING_SNAKE_CASE	:       Tuple 							=    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"
									SCREAMING_SNAKE_CASE	:       str 							=    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=_SCREAMING_SNAKE_CASE    ,					homepage=_HOMEPAGE    ,					license=_LICENSE    ,					citation=_CITATION    ,					)





	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    _URLS[self.config.name]
					SCREAMING_SNAKE_CASE	:       str 							=    dl_manager.download_and_extract(_SCREAMING_SNAKE_CASE						)
					return [
					    datasets.SplitGenerator(
					        name=datasets.Split.TRAIN    ,					gen_kwargs={
					            'filepath': os.path.join(_SCREAMING_SNAKE_CASE    ,					'train.jsonl'						),
					            'split': 'train',
					        }    ,					),
					    datasets.SplitGenerator(
					        name=datasets.Split.VALIDATION    ,					gen_kwargs={
					            'filepath': os.path.join(_SCREAMING_SNAKE_CASE    ,					'dev.jsonl'						),
					            'split': 'dev',
					        }    ,					),
					    datasets.SplitGenerator(
					        name=datasets.Split.TEST    ,					gen_kwargs={
					            'filepath': os.path.join(_SCREAMING_SNAKE_CASE    ,					'test.jsonl'						),
					            'split': 'test'
					        }    ,					),
					]







	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  str:





					"""simple docstring"""


					with open(_SCREAMING_SNAKE_CASE    ,					encoding='utf-8'						) as f:
									for key, row in enumerate(_SCREAMING_SNAKE_CASE						):
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    json.loads(_SCREAMING_SNAKE_CASE						)
													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"],
																	}










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					def model(_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Any 							=    sample.shape
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
									SCREAMING_SNAKE_CASE	:       List[str] 							=    F.log_softmax(logits.double()    ,					dim=1						).float()
									return return_value

					return model





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Dict:





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





