"""simple docstring"""



# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class SCREAMING_SNAKE_CASE     (    a   ,      a   ,      a   ,      unittest.TestCase	):







 """simple docstring"""


 a_       :	List[str]  			=StableDiffusionControlNetInpaintPipeline
 a_       :	Tuple  			=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 a_       :	Optional[int]  			=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 a_       :	Any  			=frozenset({"control_image"}	)  # skip `image` and `mask` for now, only test for control_image
 a_       :	Any  			=TEXT_TO_IMAGE_IMAGE_PARAMS


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Tuple:




    '''simple docstring'''






    torch.manual_seed(0				)
    a__     				=		UNetaDConditionModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=9					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
    torch.manual_seed(0				)
    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    torch.manual_seed(0				)
    a__     				=		DDIMScheduler(
        beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_snake_case					,   set_alpha_to_one=_snake_case					,   )
    torch.manual_seed(0				)
    a__     				=		AutoencoderKL(
        block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,   latent_channels=4					,   )
    torch.manual_seed(0				)
    a__     				=		CLIPTextConfig(
        bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
    a__     				=		CLIPTextModel(_snake_case				)
    a__     				=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

    a__     				=		{
        'unet': unet,
        'controlnet': controlnet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      Dict					,   _snake_case		:      Optional[Any]=0				)				->			List[str]:




    '''simple docstring'''






    if str(_snake_case				).startswith('mps'				):
       a__     				=		torch.manual_seed(_snake_case				)
    else:
       a__     				=		torch.Generator(device=_snake_case				).manual_seed(_snake_case				)

    a__     				=		2
    a__     				=		randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=_snake_case					,   device=torch.device(_snake_case				)					,   )
    a__     				=		floats_tensor((1, 3, 32, 32)					,   rng=random.Random(_snake_case				)				).to(_snake_case				)
    a__     				=		init_image.cpu().permute(0					,   2					,   3					,   1				)[0]

    a__     				=		Image.fromarray(np.uinta(_snake_case				)				).convert('RGB'				).resize((64, 64)				)
    a__     				=		Image.fromarray(np.uinta(init_image + 4				)				).convert('RGB'				).resize((64, 64)				)

    a__     				=		{
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
        'image': image,
        'mask_image': mask_image,
        'control_image': control_image,
    }

    return inputs


 def        _lowerCAmelCase				(   self		:      Any				)				->			str:




    '''simple docstring'''






    return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)


 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()					,   reason='XFormers attention is only available with CUDA and `xformers` installed'					,   )
 def        _lowerCAmelCase				(   self		:      List[str]				)				->			List[Any]:




    '''simple docstring'''






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)



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




    '''simple docstring'''






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	List[Any]  			=StableDiffusionControlNetInpaintPipeline
 a_       :	Union[str, Any]  			=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 a_       :	Any  			=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 a_       :	int  			=frozenset([]	)


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




    '''simple docstring'''






    torch.manual_seed(0				)
    a__     				=		UNetaDConditionModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=4					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
    torch.manual_seed(0				)
    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    torch.manual_seed(0				)
    a__     				=		DDIMScheduler(
        beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_snake_case					,   set_alpha_to_one=_snake_case					,   )
    torch.manual_seed(0				)
    a__     				=		AutoencoderKL(
        block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,   latent_channels=4					,   )
    torch.manual_seed(0				)
    a__     				=		CLIPTextConfig(
        bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
    a__     				=		CLIPTextModel(_snake_case				)
    a__     				=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

    a__     				=		{
        'unet': unet,
        'controlnet': controlnet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components

class SCREAMING_SNAKE_CASE     (    a   ,      a   ,      unittest.TestCase	):







 """simple docstring"""


 a_       :	List[Any]  			=StableDiffusionControlNetInpaintPipeline
 a_       :	Union[str, Any]  			=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 a_       :	Optional[Any]  			=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


 def        _lowerCAmelCase				(   self		:      int				)				->			str:




    '''simple docstring'''






    torch.manual_seed(0				)
    a__     				=		UNetaDConditionModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=9					,   out_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,   cross_attention_dim=32					,   )
    torch.manual_seed(0				)

    def init_weights(_snake_case		:      Any				):
       if isinstance(_snake_case					,   torch.nn.Convad				):
          torch.nn.init.normal(m.weight				)
          m.bias.data.fill_(1.0				)

    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    controlneta.controlnet_down_blocks.apply(_snake_case				)

    torch.manual_seed(0				)
    a__     				=		ControlNetModel(
        block_out_channels=(32, 64)					,   layers_per_block=2					,   in_channels=4					,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,   cross_attention_dim=32					,   conditioning_embedding_out_channels=(16, 32)					,   )
    controlneta.controlnet_down_blocks.apply(_snake_case				)

    torch.manual_seed(0				)
    a__     				=		DDIMScheduler(
        beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=_snake_case					,   set_alpha_to_one=_snake_case					,   )
    torch.manual_seed(0				)
    a__     				=		AutoencoderKL(
        block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,   latent_channels=4					,   )
    torch.manual_seed(0				)
    a__     				=		CLIPTextConfig(
        bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )
    a__     				=		CLIPTextModel(_snake_case				)
    a__     				=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

    a__     				=		MultiControlNetModel([controlneta, controlneta]				)

    a__     				=		{
        'unet': unet,
        'controlnet': controlnet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components


 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      Optional[Any]					,   _snake_case		:      int=0				)				->			str:




    '''simple docstring'''






    if str(_snake_case				).startswith('mps'				):
       a__     				=		torch.manual_seed(_snake_case				)
    else:
       a__     				=		torch.Generator(device=_snake_case				).manual_seed(_snake_case				)

    a__     				=		2

    a__     				=		[
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=_snake_case					,   device=torch.device(_snake_case				)					,   ),
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=_snake_case					,   device=torch.device(_snake_case				)					,   ),
    ]
    a__     				=		floats_tensor((1, 3, 32, 32)					,   rng=random.Random(_snake_case				)				).to(_snake_case				)
    a__     				=		init_image.cpu().permute(0					,   2					,   3					,   1				)[0]

    a__     				=		Image.fromarray(np.uinta(_snake_case				)				).convert('RGB'				).resize((64, 64)				)
    a__     				=		Image.fromarray(np.uinta(init_image + 4				)				).convert('RGB'				).resize((64, 64)				)

    a__     				=		{
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
        'image': image,
        'mask_image': mask_image,
        'control_image': control_image,
    }

    return inputs


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




    '''simple docstring'''






    a__     				=		self.get_dummy_components()
    a__     				=		self.pipeline_class(**_snake_case				)
    pipe.to(_snake_case				)

    a__     				=		10.0
    a__     				=		4

    a__     				=		self.get_dummy_inputs(_snake_case				)
    a__     				=		steps
    a__     				=		scale
    a__     				=		pipe(**_snake_case				)[0]

    a__     				=		self.get_dummy_inputs(_snake_case				)
    a__     				=		steps
    a__     				=		scale
    a__     				=		pipe(**_snake_case					,   control_guidance_start=0.1					,   control_guidance_end=0.2				)[0]

    a__     				=		self.get_dummy_inputs(_snake_case				)
    a__     				=		steps
    a__     				=		scale
    a__     				=		pipe(**_snake_case					,   control_guidance_start=[0.1, 0.3]					,   control_guidance_end=[0.2, 0.7]				)[0]

    a__     				=		self.get_dummy_inputs(_snake_case				)
    a__     				=		steps
    a__     				=		scale
    a__     				=		pipe(**_snake_case					,   control_guidance_start=0.4					,   control_guidance_end=[0.5, 0.8]				)[0]

    # make sure that all outputs are different
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
    assert np.sum(np.abs(output_a - output_a				)				) > 1E-3


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




    '''simple docstring'''






    return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)


 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()					,   reason='XFormers attention is only available with CUDA and `xformers` installed'					,   )
 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			Any:




    '''simple docstring'''






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)


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




    '''simple docstring'''






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



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




    '''simple docstring'''






    a__     				=		self.get_dummy_components()
    a__     				=		self.pipeline_class(**_snake_case				)
    pipe.to(_snake_case				)
    pipe.set_progress_bar_config(disable=_snake_case				)
    with tempfile.TemporaryDirectory() as tmpdir:
       try:
          # save_pretrained is not implemented for Multi-ControlNet
          pipe.save_pretrained(_snake_case				)
       except NotImplementedError:
          pass

@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      str				)				->			int:




    '''simple docstring'''






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


 def        _lowerCAmelCase				(   self		:      int				)				->			Any:




    '''simple docstring'''






    a__     				=		ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'				)

    a__     				=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        'runwayml/stable-diffusion-inpainting'					,   safety_checker=_snake_case					,   controlnet=_snake_case				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=_snake_case				)

    a__     				=		torch.Generator(device='cpu'				).manual_seed(0				)
    a__     				=		load_image(
        'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'				).resize((512, 512)				)

    a__     				=		load_image(
        'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
        '/stable_diffusion_inpaint/input_bench_mask.png'				).resize((512, 512)				)

    a__     				=		'pitch black hole'

    a__     				=		load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'				).resize((512, 512)				)

    a__     				=		pipe(
        _snake_case					,   image=_snake_case					,   mask_image=_snake_case					,   control_image=_snake_case					,   generator=_snake_case					,   output_type='np'					,   num_inference_steps=3					,   )

    a__     				=		output.images[0]

    assert image.shape == (512, 512, 3)

    a__     				=		load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'				)

    assert np.abs(expected_image - image				).max() < 9E-2



 def        _lowerCAmelCase				(   self		:      Dict				)				->			Any:




    '''simple docstring'''






    a__     				=		ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'				)

    a__     				=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        'runwayml/stable-diffusion-v1-5'					,   safety_checker=_snake_case					,   controlnet=_snake_case				)
    a__     				=		DDIMScheduler.from_config(pipe.scheduler.config				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=_snake_case				)

    a__     				=		torch.Generator(device='cpu'				).manual_seed(33				)

    a__     				=		load_image(
        'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'				)
    a__     				=		init_image.resize((512, 512)				)

    a__     				=		load_image(
        'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'				)
    a__     				=		mask_image.resize((512, 512)				)

    a__     				=		'a handsome man with ray-ban sunglasses'

    def make_inpaint_condition(_snake_case		:      Any					,   _snake_case		:      int				):
       a__     				=		np.array(image.convert('RGB'				)				).astype(np.floataa				) / 255.0
       a__     				=		np.array(image_mask.convert('L'				)				).astype(np.floataa				) / 255.0

       assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
       a__     				=		-1.0  # set as masked pixel
       a__     				=		np.expand_dims(_snake_case					,   0				).transpose(0					,   3					,   1					,   2				)
       a__     				=		torch.from_numpy(_snake_case				)
       return image

    a__     				=		make_inpaint_condition(_snake_case					,   _snake_case				)

    a__     				=		pipe(
        _snake_case					,   image=_snake_case					,   mask_image=_snake_case					,   control_image=_snake_case					,   guidance_scale=9.0					,   eta=1.0					,   generator=_snake_case					,   num_inference_steps=20					,   output_type='np'					,   )
    a__     				=		output.images[0]

    assert image.shape == (512, 512, 3)

    a__     				=		load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'				)

    assert np.abs(expected_image - image				).max() < 9E-2



"""simple docstring"""



from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__magic_name__    				=					{
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}





@pytest.mark.parametrize(
    'task_cls',[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],)
def   _lowerCamelCase ( UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   a__     				=		task_cls()
   a__     				=		asdict(UpperCAmelCase__					)
   a__     				=		task_template_from_dict(UpperCAmelCase__					)
   assert task == reloaded

class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		LanguageModeling(text_column='input_text'				)
    assert {"input_text": "text"} == task.column_mapping



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




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				)}				)
    a__     				=		{'text_column': 'input_text'}
    a__     				=		LanguageModeling.from_dict(_snake_case				)
    assert "language-modeling" == task.task
    assert input_schema == task.input_schema

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Dict				)				->			Tuple:




    '''simple docstring'''






    a__     				=		sorted(['pos', 'neg']				)


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




    '''simple docstring'''






    a__     				=		TextClassification(text_column='input_text'					,   label_column='input_label'				)
    self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}					,   task.column_mapping				)


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




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				)}				)
    # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
    a__     				=		Features({'labels': ClassLabel}				)
    a__     				=		{'text_column': 'input_text', 'label_column': 'input_labels'}
    a__     				=		TextClassification.from_dict(_snake_case				)
    self.assertEqual('text-classification'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)



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




    '''simple docstring'''






    a__     				=		TextClassification(text_column='input_text'					,   label_column='input_label'				)
    self.assertEqual(task.label_schema['labels']					,   _snake_case				)
    a__     				=		task.align_with_features(Features({'input_label': ClassLabel(names=self.labels				)}				)				)
    self.assertEqual(task.label_schema['labels']					,   ClassLabel(names=self.labels				)				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		QuestionAnsweringExtractive(
        context_column='input_context'					,   question_column='input_question'					,   answers_column='input_answers'				)
    self.assertDictEqual(
        {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}					,   task.column_mapping				)



 def        _lowerCAmelCase				(   self		:      Tuple				)				->			str:




    '''simple docstring'''






    a__     				=		Features({'question': Value('string'				), 'context': Value('string'				)}				)
    a__     				=		Features(
        {
            'answers': Sequence(
                {
                    'text': Value('string'				),
                    'answer_start': Value('int32'				),
                }				)
        }				)
    a__     				=		{
        'context_column': 'input_input_context',
        'question_column': 'input_question',
        'answers_column': 'input_answers',
    }
    a__     				=		QuestionAnsweringExtractive.from_dict(_snake_case				)
    self.assertEqual('question-answering-extractive'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		Summarization(text_column='input_text'					,   summary_column='input_summary'				)
    self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}					,   task.column_mapping				)



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




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				)}				)
    a__     				=		Features({'summary': Value('string'				)}				)
    a__     				=		{'text_column': 'input_text', 'summary_column': 'input_summary'}
    a__     				=		Summarization.from_dict(_snake_case				)
    self.assertEqual('summarization'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Tuple:




    '''simple docstring'''






    a__     				=		AutomaticSpeechRecognition(audio_column='input_audio'					,   transcription_column='input_transcription'				)
    self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}					,   task.column_mapping				)



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




    '''simple docstring'''






    a__     				=		Features({'audio': Audio()}				)
    a__     				=		Features({'transcription': Value('string'				)}				)
    a__     				=		{
        'audio_column': 'input_audio',
        'transcription_column': 'input_transcription',
    }
    a__     				=		AutomaticSpeechRecognition.from_dict(_snake_case				)
    self.assertEqual('automatic-speech-recognition'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		sorted(['pos', 'neg']				)


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




    '''simple docstring'''






    a__     				=		AudioClassification(audio_column='input_audio'					,   label_column='input_label'				)
    self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}					,   task.column_mapping				)


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




    '''simple docstring'''






    a__     				=		Features({'audio': Audio()}				)
    a__     				=		Features({'labels': ClassLabel}				)
    a__     				=		{
        'audio_column': 'input_image',
        'label_column': 'input_label',
    }
    a__     				=		AudioClassification.from_dict(_snake_case				)
    self.assertEqual('audio-classification'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)



 def        _lowerCAmelCase				(   self		:      str				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		AudioClassification(audio_column='input_audio'					,   label_column='input_label'				)
    self.assertEqual(task.label_schema['labels']					,   _snake_case				)
    a__     				=		task.align_with_features(Features({'input_label': ClassLabel(names=self.labels				)}				)				)
    self.assertEqual(task.label_schema['labels']					,   ClassLabel(names=self.labels				)				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Tuple:




    '''simple docstring'''






    a__     				=		sorted(['pos', 'neg']				)


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			str:




    '''simple docstring'''






    a__     				=		ImageClassification(image_column='input_image'					,   label_column='input_label'				)
    self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}					,   task.column_mapping				)


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




    '''simple docstring'''






    a__     				=		Features({'image': Image()}				)
    a__     				=		Features({'labels': ClassLabel}				)
    a__     				=		{
        'image_column': 'input_image',
        'label_column': 'input_label',
    }
    a__     				=		ImageClassification.from_dict(_snake_case				)
    self.assertEqual('image-classification'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)



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




    '''simple docstring'''






    a__     				=		ImageClassification(image_column='input_image'					,   label_column='input_label'				)
    self.assertEqual(task.label_schema['labels']					,   _snake_case				)
    a__     				=		task.align_with_features(Features({'input_label': ClassLabel(names=self.labels				)}				)				)
    self.assertEqual(task.label_schema['labels']					,   ClassLabel(names=self.labels				)				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
    a__     				=		Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}					,   info=_snake_case				)
    assert isinstance(dataset.info.task_templates					,   _snake_case				)
    assert len(dataset.info.task_templates				) == 1

    def keep_task(_snake_case		:      Tuple				):
       return x

    def dont_keep_task(_snake_case		:      Any				):
       a__     				=		deepcopy(_snake_case				)
       a__     				=		0
       return out

    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates
    # reload from cache
    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates

    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == []
    # reload from cache
    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == []



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




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				), 'label': ClassLabel(names=('pos', 'neg')				)}				)
    a__     				=		TextClassification(text_column='text'					,   label_column='label'				)
    a__     				=		DatasetInfo(features=_snake_case					,   task_templates=_snake_case				)
    a__     				=		Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}					,   info=_snake_case				)

    def process(_snake_case		:      List[Any]				):
       return example

    a__     				=		dataset.remove_columns('label'				)
    a__     				=		modified_dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == []


