






'''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
_UpperCamelCase		:		int								=			"\\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		:		Dict								=			"\\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		:		Optional[int]								=			""

# TODO: Add the licence for the dataset here if you can find it
_UpperCamelCase		:		int								=			""

# 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		:		Union[str, Any]								=			{
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}


class 				snake_case__     (			datasets.GeneratorBasedBuilder):
					a_       =			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_       =			[
					    datasets.BuilderConfig(name="first_domain"      ,							version=SCREAMING_SNAKE_CASE__      ,							description="This part of my dataset covers a first domain"),
					    datasets.BuilderConfig(name="second_domain"      ,							version=SCREAMING_SNAKE_CASE__      ,							description="This part of my dataset covers a second domain"),
					]

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




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

								if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
											UpperCAmelCase_     :   List[str]       =   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"
											UpperCAmelCase_     :   Dict       =   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=_A							,				homepage=_HOMEPAGE							,				license=_LICENSE							,				citation=_CITATION							,				)




					def 						A     (       self       :						str							,				_A       :						int     )  ->    Dict:
								UpperCAmelCase_     :   Any       =   _URLS[self.config.name]
								UpperCAmelCase_     :   Tuple       =   dl_manager.download_and_extract(_A     )
								return [
								    datasets.SplitGenerator(
								        name=datasets.Split.TRAIN							,				gen_kwargs={
								            '''filepath''': os.path.join(_A							,				'''train.jsonl'''     ),
								            '''split''': '''train''',
								        }							,				),
								    datasets.SplitGenerator(
								        name=datasets.Split.VALIDATION							,				gen_kwargs={
								            '''filepath''': os.path.join(_A							,				'''dev.jsonl'''     ),
								            '''split''': '''dev''',
								        }							,				),
								    datasets.SplitGenerator(
								        name=datasets.Split.TEST							,				gen_kwargs={
								            '''filepath''': os.path.join(_A							,				'''test.jsonl'''     ),
								            '''split''': '''test'''
								        }							,				),
								]





					def 						A     (       self       :						List[Any]							,				_A       :						str							,				_A       :						Dict     )  ->    List[str]:

								with open(_A							,				encoding='''utf-8'''     ) as f:
											for key, row in enumerate(_A     ):
														UpperCAmelCase_     :   List[Any]       =   json.loads(_A     )
														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 unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								if isinstance(image[0]							,				PIL.Image.Image     ):
											return np.stack([np.array(_A     ) for i in image]							,				axis=0     )
								elif isinstance(_A							,				torch.Tensor     ):
											return image.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								return image




					def 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								UpperCAmelCase_     :   List[Any]       =   image_processor.numpy_to_pil(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6





					def 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6


