




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












import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase						:							Optional[int]			     =						'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__lowerCAmelCase						:							str			     =						'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

__lowerCAmelCase						:							Optional[Any]			     =						'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,			_KWARGS_DESCRIPTION  )
class        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.Value("string"				),
							                "no_answer_probability": datasets.Value("float32"				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"				), "answer_start": datasets.Value("int32"				)}				),
							            },
							        }				)   , codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , )





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

							a       =     make_qid_to_has_ans(__lowerCamelCase				)  # maps qid to True/False
							a       =     [k for k, v in qid_to_has_ans.items() if v]
							a       =     [k for k, v in qid_to_has_ans.items() if not v]

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

							if has_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "HasAns"				)
							if no_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "NoAns"				)
							find_all_best_thresh(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							return dict(__lowerCamelCase				)






