


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						List[str]       )			->					Dict:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
														__snake_case	:    Any											=     1
														__snake_case	:    Optional[int]											=     3
														__snake_case	:    Tuple											=     (32, 32)

														__snake_case	:    Tuple											=     floats_tensor((batch_size, num_channels) + sizes					,			rng=random.Random(0       )       ).to(lowerCamelCase       )
														return image

							@property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[Any]:
														torch.manual_seed(0       )
														__snake_case	:    str											=     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					,			)
														return model

							@property
							def 					__snake_case      (							self :						int       )			->					Optional[int]:
														torch.manual_seed(0       )
														__snake_case	:    Dict											=     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					,			)
														return model

							@property
							def 					__snake_case      (							self :						Dict       )			->					Tuple:
														torch.manual_seed(0       )
														__snake_case	:    Optional[Any]											=     RobertaSeriesConfig(
														    hidden_size=32					,			project_dim=32					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=5006					,			)
														return RobertaSeriesModelWithTransformation(lowerCamelCase       )

							@property
							def 					__snake_case      (							self :						Any       )			->					Union[str, Any]:
														def extract(*lowerCamelCase :						int					,			**lowerCamelCase :						Union[str, Any]       ):

																					class        a	:




																												"""simple docstring"""


																												def __init__(							self :						List[Any]       )			->					int:
																																			__snake_case	:    Optional[Any]											=     torch.ones([0]       )






																												def 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						List[str]       )			->					Union[str, Any]:
																																			self.pixel_values.to(lowerCamelCase       )
																																			return self
																					return Out()

														return extract

							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    List[Any]											=     "cpu"  # ensure determinism for the device-dependent torch.Generator
														__snake_case	:    Optional[int]											=     self.dummy_cond_unet
														__snake_case	:    int											=     PNDMScheduler(skip_prk_steps=lowerCamelCase       )
														__snake_case	:    Optional[int]											=     self.dummy_vae
														__snake_case	:    Any											=     self.dummy_text_encoder
														__snake_case	:    Optional[Any]											=     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
														__snake_case	:    List[str]											=     77

														__snake_case	:    Optional[Any]											=     self.dummy_image.to(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     init_image / 2 + 0.5

														# make sure here that pndm scheduler skips prk
														__snake_case	:    Any											=     AltDiffusionImgaImgPipeline(
														    unet=lowerCamelCase					,			scheduler=lowerCamelCase					,			vae=lowerCamelCase					,			text_encoder=lowerCamelCase					,			tokenizer=lowerCamelCase					,			safety_checker=lowerCamelCase					,			feature_extractor=self.dummy_extractor					,			)
														__snake_case	:    Tuple											=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,			do_normalize=lowerCamelCase       )
														__snake_case	:    Dict											=     alt_pipe.to(lowerCamelCase       )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    List[str]											=     "A painting of a squirrel eating a burger"
														__snake_case	:    List[Any]											=     torch.Generator(device=lowerCamelCase       ).manual_seed(0       )
														__snake_case	:    Any											=     alt_pipe(
														    [prompt]					,			generator=lowerCamelCase					,			guidance_scale=6.0					,			num_inference_steps=2					,			output_type="np"					,			image=lowerCamelCase					,			)

														__snake_case	:    Optional[int]											=     output.images

														__snake_case	:    Optional[Any]											=     torch.Generator(device=lowerCamelCase       ).manual_seed(0       )
														__snake_case	:    List[str]											=     alt_pipe(
														    [prompt]					,			generator=lowerCamelCase					,			guidance_scale=6.0					,			num_inference_steps=2					,			output_type="np"					,			image=lowerCamelCase					,			return_dict=lowerCamelCase					,			)[0]

														__snake_case	:    Union[str, Any]											=     image[0, -3:, -3:, -1]
														__snake_case	:    Optional[int]											=     image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														__snake_case	:    Optional[int]											=     np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99]       )

														assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 5E-3

							@unittest.skipIf(torch_device != "cuda"					,			"This test requires a GPU"       )
							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.dummy_cond_unet
														__snake_case	:    List[Any]											=     PNDMScheduler(skip_prk_steps=lowerCamelCase       )
														__snake_case	:    int											=     self.dummy_vae
														__snake_case	:    str											=     self.dummy_text_encoder
														__snake_case	:    Optional[Any]											=     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
														__snake_case	:    Optional[int]											=     77

														__snake_case	:    Any											=     self.dummy_image.to(lowerCamelCase       )

														# put models in fp16
														__snake_case	:    Any											=     unet.half()
														__snake_case	:    List[Any]											=     vae.half()
														__snake_case	:    List[str]											=     bert.half()

														# make sure here that pndm scheduler skips prk
														__snake_case	:    int											=     AltDiffusionImgaImgPipeline(
														    unet=lowerCamelCase					,			scheduler=lowerCamelCase					,			vae=lowerCamelCase					,			text_encoder=lowerCamelCase					,			tokenizer=lowerCamelCase					,			safety_checker=lowerCamelCase					,			feature_extractor=self.dummy_extractor					,			)
														__snake_case	:    List[str]											=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,			do_normalize=lowerCamelCase       )
														__snake_case	:    List[str]											=     alt_pipe.to(lowerCamelCase       )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Dict											=     "A painting of a squirrel eating a burger"
														__snake_case	:    str											=     torch.manual_seed(0       )
														__snake_case	:    List[Any]											=     alt_pipe(
														    [prompt]					,			generator=lowerCamelCase					,			num_inference_steps=2					,			output_type="np"					,			image=lowerCamelCase					,			).images

														assert image.shape == (1, 32, 32, 3)






							@unittest.skipIf(torch_device != "cuda"					,			"This test requires a GPU"       )
							def 					__snake_case      (							self :						List[str]       )			->					List[Any]:
														__snake_case	:    Dict											=     load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg"       )
														# resize to resolution that is divisible by 8 but not 16 or 32
														__snake_case	:    Tuple											=     init_image.resize((760, 504)       )

														__snake_case	:    str											=     "BAAI/AltDiffusion"
														__snake_case	:    List[Any]											=     AltDiffusionImgaImgPipeline.from_pretrained(
														    lowerCamelCase					,			safety_checker=lowerCamelCase					,			)
														pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )
														pipe.enable_attention_slicing()

														__snake_case	:    Union[str, Any]											=     "A fantasy landscape, trending on artstation"

														__snake_case	:    List[str]											=     torch.manual_seed(0       )
														__snake_case	:    Tuple											=     pipe(
														    prompt=lowerCamelCase					,			image=lowerCamelCase					,			strength=0.75					,			guidance_scale=7.5					,			generator=lowerCamelCase					,			output_type="np"					,			)
														__snake_case	:    int											=     output.images[0]

														__snake_case	:    Tuple											=     image[255:258, 383:386, -1]

														assert image.shape == (504, 760, 3)
														__snake_case	:    List[str]											=     np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00]       )

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

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




							"""simple docstring"""


							def 					__snake_case      (							self :						List[str]       )			->					Dict:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					__snake_case      (							self :						Optional[int]       )			->					List[Any]:
														__snake_case	:    List[str]											=     load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg"       )
														__snake_case	:    int											=     init_image.resize((768, 512)       )
														__snake_case	:    Optional[Any]											=     load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"       )

														__snake_case	:    int											=     "BAAI/AltDiffusion"
														__snake_case	:    Any											=     AltDiffusionImgaImgPipeline.from_pretrained(
														    lowerCamelCase					,			safety_checker=lowerCamelCase					,			)
														pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )
														pipe.enable_attention_slicing()

														__snake_case	:    Optional[Any]											=     "A fantasy landscape, trending on artstation"

														__snake_case	:    Dict											=     torch.manual_seed(0       )
														__snake_case	:    List[Any]											=     pipe(
														    prompt=lowerCamelCase					,			image=lowerCamelCase					,			strength=0.75					,			guidance_scale=7.5					,			generator=lowerCamelCase					,			output_type="np"					,			)
														__snake_case	:    Optional[Any]											=     output.images[0]

														assert image.shape == (512, 768, 3)
														# img2img is flaky across GPUs even in fp32, so using MAE here
														assert np.abs(expected_image - image       ).max() < 1E-2





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_snake_case  :	List[Any]			      =       "\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

_snake_case  :	Any			      =       "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

_snake_case  :	str			      =       "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							return float((preds == labels).mean()			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase="binary"			):
							__snake_case	:    Union[str, Any]											=     simple_accuracy(__lowerCamelCase							,			__lowerCamelCase			)
							__snake_case	:    List[str]											=     float(fa_score(y_true=__lowerCamelCase							,			y_pred=__lowerCamelCase							,			average=__lowerCamelCase			)			)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    List[Any]											=     {}
							for id_pred, label in zip(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    int											=     F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
														__snake_case	:    str											=     id_pred["prediction"]
														if question_id in question_map:
																					question_map[question_id].append((pred, label)			)
														else:
																					__snake_case	:    Optional[Any]											=     [(pred, label)]
							__snake_case				,    __snake_case	:    Dict											=     [], []
							for question, preds_labels in question_map.items():
														__snake_case				,    __snake_case	:    int											=     zip(*__lowerCamelCase			)
														__snake_case	:    Optional[Any]											=     fa_score(y_true=__lowerCamelCase							,			y_pred=__lowerCamelCase							,			average="macro"			)
														fas.append(__lowerCamelCase			)
														__snake_case	:    Union[str, Any]											=     int(sum(pred == label for pred, label in preds_labels			) == len(__lowerCamelCase			)			)
														ems.append(__lowerCamelCase			)
							__snake_case	:    Tuple											=     float(sum(__lowerCamelCase			) / len(__lowerCamelCase			)			)
							__snake_case	:    Any											=     sum(__lowerCamelCase			) / len(__lowerCamelCase			)
							__snake_case	:    int											=     float(fa_score(y_true=__lowerCamelCase							,			y_pred=[id_pred["prediction"] for id_pred in ids_preds]			)			)
							return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,       _KWARGS_DESCRIPTION   )
class        a	(datasets.Metric   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						List[Any]       )			->					Union[str, Any]:
														if self.config_name not in [
														    "boolq",
														    "cb",
														    "copa",
														    "multirc",
														    "record",
														    "rte",
														    "wic",
														    "wsc",
														    "wsc.fixed",
														    "axb",
														    "axg",
														]:
																					raise KeyError(
																					    "You should supply a configuration name selected in "
																					    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )
														return datasets.MetricInfo(
														    description=_DESCRIPTION					,			citation=_CITATION					,			inputs_description=_KWARGS_DESCRIPTION					,			features=datasets.Features(self._get_feature_types()       )					,			codebase_urls=[]					,			reference_urls=[]					,			format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None					,			)

							def 					__snake_case      (							self :						Any       )			->					int:
														if self.config_name == "record":
																					return {
																					    "predictions": {
																					        "idx": {
																					            "passage": datasets.Value("int64"       ),
																					            "query": datasets.Value("int64"       ),
																					        },
																					        "prediction_text": datasets.Value("string"       ),
																					    },
																					    "references": {
																					        "idx": {
																					            "passage": datasets.Value("int64"       ),
																					            "query": datasets.Value("int64"       ),
																					        },
																					        "answers": datasets.Sequence(datasets.Value("string"       )       ),
																					    },
																					}
														elif self.config_name == "multirc":
																					return {
																					    "predictions": {
																					        "idx": {
																					            "answer": datasets.Value("int64"       ),
																					            "paragraph": datasets.Value("int64"       ),
																					            "question": datasets.Value("int64"       ),
																					        },
																					        "prediction": datasets.Value("int64"       ),
																					    },
																					    "references": datasets.Value("int64"       ),
																					}
														else:
																					return {
																					    "predictions": datasets.Value("int64"       ),
																					    "references": datasets.Value("int64"       ),
																					}






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						Optional[Any]       )			->					str:
														if self.config_name == "axb":
																					return {"matthews_correlation": matthews_corrcoef(lowerCamelCase					,			lowerCamelCase       )}
														elif self.config_name == "cb":
																					return acc_and_fa(lowerCamelCase					,			lowerCamelCase					,			fa_avg="macro"       )
														elif self.config_name == "record":
																					__snake_case	:    Tuple											=     [
																					    {
																					        "qas": [
																					            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
																					            for ref in references
																					        ]
																					    }
																					]
																					__snake_case	:    List[str]											=     {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
																					return evaluate_record(lowerCamelCase					,			lowerCamelCase       )[0]
														elif self.config_name == "multirc":
																					return evaluate_multirc(lowerCamelCase					,			lowerCamelCase       )
														elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
																					return {"accuracy": simple_accuracy(lowerCamelCase					,			lowerCamelCase       )}
														else:
																					raise KeyError(
																					    "You should supply a configuration name selected in "
																					    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )

