


'''simple docstring'''

from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_pt_objects import *  # noqa F403
else:
       from .consistency_models import ConsistencyModelPipeline
       from .dance_diffusion import DanceDiffusionPipeline
       from .ddim import DDIMPipeline
       from .ddpm import DDPMPipeline
       from .dit import DiTPipeline
       from .latent_diffusion import LDMSuperResolutionPipeline
       from .latent_diffusion_uncond import LDMPipeline
       from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
       from .pndm import PNDMPipeline
       from .repaint import RePaintPipeline
       from .score_sde_ve import ScoreSdeVePipeline
       from .stochastic_karras_ve import KarrasVePipeline

try:
       if not (is_torch_available() and is_librosa_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
       from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
       if not (is_torch_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
       from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
       from .audioldm import AudioLDMPipeline
       from .controlnet import (
           StableDiffusionControlNetImgaImgPipeline,
           StableDiffusionControlNetInpaintPipeline,
           StableDiffusionControlNetPipeline,
       )
       from .deepfloyd_if import (
           IFImgaImgPipeline,
           IFImgaImgSuperResolutionPipeline,
           IFInpaintingPipeline,
           IFInpaintingSuperResolutionPipeline,
           IFPipeline,
           IFSuperResolutionPipeline,
       )
       from .kandinsky import (
           KandinskyImgaImgPipeline,
           KandinskyInpaintPipeline,
           KandinskyPipeline,
           KandinskyPriorPipeline,
       )
       from .kandinskya_a import (
           KandinskyVaaControlnetImgaImgPipeline,
           KandinskyVaaControlnetPipeline,
           KandinskyVaaImgaImgPipeline,
           KandinskyVaaInpaintPipeline,
           KandinskyVaaPipeline,
           KandinskyVaaPriorEmbaEmbPipeline,
           KandinskyVaaPriorPipeline,
       )
       from .latent_diffusion import LDMTextToImagePipeline
       from .paint_by_example import PaintByExamplePipeline
       from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
       from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
       from .stable_diffusion import (
           CycleDiffusionPipeline,
           StableDiffusionAttendAndExcitePipeline,
           StableDiffusionDepthaImgPipeline,
           StableDiffusionDiffEditPipeline,
           StableDiffusionImageVariationPipeline,
           StableDiffusionImgaImgPipeline,
           StableDiffusionInpaintPipeline,
           StableDiffusionInpaintPipelineLegacy,
           StableDiffusionInstructPixaPixPipeline,
           StableDiffusionLatentUpscalePipeline,
           StableDiffusionLDMaDPipeline,
           StableDiffusionModelEditingPipeline,
           StableDiffusionPanoramaPipeline,
           StableDiffusionParadigmsPipeline,
           StableDiffusionPipeline,
           StableDiffusionPixaPixZeroPipeline,
           StableDiffusionSAGPipeline,
           StableDiffusionUpscalePipeline,
           StableUnCLIPImgaImgPipeline,
           StableUnCLIPPipeline,
       )
       from .stable_diffusion_safe import StableDiffusionPipelineSafe
       from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
       from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
       from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
       from .versatile_diffusion import (
           VersatileDiffusionDualGuidedPipeline,
           VersatileDiffusionImageVariationPipeline,
           VersatileDiffusionPipeline,
           VersatileDiffusionTextToImagePipeline,
       )
       from .vq_diffusion import VQDiffusionPipeline


try:
       if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
       from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
       if not is_onnx_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_onnx_objects import *  # noqa F403
else:
       from .onnx_utils import OnnxRuntimeModel

try:
       if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
       from .stable_diffusion import (
           OnnxStableDiffusionImgaImgPipeline,
           OnnxStableDiffusionInpaintPipeline,
           OnnxStableDiffusionInpaintPipelineLegacy,
           OnnxStableDiffusionPipeline,
           OnnxStableDiffusionUpscalePipeline,
           StableDiffusionOnnxPipeline,
       )

try:
       if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
       from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_flax_objects import *  # noqa F403
else:
       from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
       if not (is_flax_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
       from .controlnet import FlaxStableDiffusionControlNetPipeline
       from .stable_diffusion import (
           FlaxStableDiffusionImgaImgPipeline,
           FlaxStableDiffusionInpaintPipeline,
           FlaxStableDiffusionPipeline,
       )
try:
       if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
       from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline



'''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
__lowercase:		Tuple    						=  "\\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
__lowercase:		Optional[int]    						=  "\\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
__lowercase:		List[str]    						=  ""

# TODO: Add the licence for the dataset here if you can find it
__lowercase:		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)
__lowercase:		List[str]    						=  {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}







class 			UpperCAmelCase			(							datasets.GeneratorBasedBuilder):
 _lowerCamelCase			:       List[str]            =       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')
 _lowerCamelCase			:       Dict            =       [
     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'),
 ]

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

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
       UpperCamelCase__			    =    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__			    =    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 		lowercase_    (			self			:						Any,       a_			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    _URLS[self.config.name]
    UpperCamelCase__			    =    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 		lowercase_    (			self			:						str,       a_			:						Optional[Any],       a_			:						Tuple       ):





    """simple docstring"""



    with open(a_,       encoding="utf-8"       ) as f:
       for key, row in enumerate(a_       ):
          UpperCamelCase__			    =    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"],
             }