"""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	:		int   =   '''\
@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	:		Optional[Any]   =   '''\
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	:		Optional[Any]   =   ''''''

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

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






class      _SCREAMING_SNAKE_CASE							(			datasets.GeneratorBasedBuilder				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    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')
   __UpperCAmelCase	:							Union[str, 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"				),
   ]

   __UpperCAmelCase	:							Optional[int]    =    "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.







   def    _A				(	self	):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      with open(__UpperCamelCase ,				encoding='utf-8'	) as f:
         for key, row in enumerate(__UpperCamelCase	):
            __lowerCamelCase    		=					json.loads(__UpperCamelCase	)
            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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__	:  Union[str, Any]						=  False





@nightly
@require_torch_gpu
class 					__snake_case    (     unittest.TestCase      ):




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


