



import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)
def        lowercase_							(A : Tuple		,					A : List[Any]		,					A : List[str]      ):
       if isinstance(SCREAMING_SNAKE_CASE__		,					torch.Tensor      ):
              return image
       elif isinstance(SCREAMING_SNAKE_CASE__		,					PIL.Image.Image      ):
              snake_case__							:	Tuple   	=	[image]

       if isinstance(image[0]		,					PIL.Image.Image      ):
              snake_case__							:	List[str]   	=	[np.array(i.resize((w, h)		,					resample=PIL_INTERPOLATION['lanczos']      )      )[None, :] for i in image]
              snake_case__							:	Optional[Any]   	=	np.concatenate(SCREAMING_SNAKE_CASE__		,					axis=0      )
              snake_case__							:	Optional[int]   	=	np.array(SCREAMING_SNAKE_CASE__      ).astype(np.floataa      ) / 255.0
              snake_case__							:	Tuple   	=	image.transpose(0		,					3		,					1		,					2      )
              snake_case__							:	Dict   	=	2.0 * image - 1.0
              snake_case__							:	Optional[Any]   	=	torch.from_numpy(SCREAMING_SNAKE_CASE__      )
       elif isinstance(image[0]		,					torch.Tensor      ):
              snake_case__							:	Any   	=	torch.cat(SCREAMING_SNAKE_CASE__		,					dim=0      )
       return image
def        lowercase_							(A : List[Any]		,					A : Optional[int]		,					A : Any		,					A : Optional[Any]=0.9995      ):
       if not isinstance(SCREAMING_SNAKE_CASE__		,					np.ndarray      ):
              snake_case__							:	Optional[Any]   	=	True
              snake_case__							:	Any   	=	va.device
              snake_case__							:	Tuple   	=	va.cpu().numpy()
              snake_case__							:	Dict   	=	va.cpu().numpy()

       snake_case__							:	Any   	=	np.sum(va * va / (np.linalg.norm(SCREAMING_SNAKE_CASE__      ) * np.linalg.norm(SCREAMING_SNAKE_CASE__      ))      )
       if np.abs(SCREAMING_SNAKE_CASE__      ) > DOT_THRESHOLD:
              snake_case__							:	Any   	=	(1 - t) * va + t * va
       else:
              snake_case__							:	int   	=	np.arccos(SCREAMING_SNAKE_CASE__      )
              snake_case__							:	List[str]   	=	np.sin(SCREAMING_SNAKE_CASE__      )
              snake_case__							:	str   	=	theta_a * t
              snake_case__							:	List[Any]   	=	np.sin(SCREAMING_SNAKE_CASE__      )
              snake_case__							:	int   	=	np.sin(theta_a - theta_t      ) / sin_theta_a
              snake_case__							:	int   	=	sin_theta_t / sin_theta_a
              snake_case__							:	Dict   	=	sa * va + sa * va

       if inputs_are_torch:
              snake_case__							:	Optional[int]   	=	torch.from_numpy(SCREAMING_SNAKE_CASE__      ).to(SCREAMING_SNAKE_CASE__      )

       return va
def        lowercase_							(A : Union[str, Any]		,					A : Union[str, Any]      ):
       snake_case__							:	Union[str, Any]   	=	F.normalize(SCREAMING_SNAKE_CASE__		,					dim=-1      )
       snake_case__							:	List[Any]   	=	F.normalize(SCREAMING_SNAKE_CASE__		,					dim=-1      )
       return (x - y).norm(dim=-1      ).div(2      ).arcsin().pow(2      ).mul(2      )
def        lowercase_							(A : str		,					A : Tuple      ):
       for param in model.parameters():
              snake_case__							:	List[str]   	=	value





class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  List[Any], _snake_case  :  Union[str, Any], _snake_case  :  Any, _snake_case  :  List[str], _snake_case  :  int, _snake_case  :  Any, _snake_case  :  Optional[int], _snake_case  :  int, _snake_case  :  str=None, _snake_case  :  Optional[Any]=None, _snake_case  :  Tuple=None, )   ->Optional[Any]:
            super().__init__()
            self.register_modules(
                vae=_snake_case, text_encoder=_snake_case, clip_model=_snake_case, tokenizer=_snake_case, unet=_snake_case, scheduler=_snake_case, feature_extractor=_snake_case, coca_model=_snake_case, coca_tokenizer=_snake_case, coca_transform=_snake_case, )
            snake_case__							:	Optional[int]   	=	(
                feature_extractor.size
                if isinstance(feature_extractor.size, _snake_case					)
                else feature_extractor.size["""shortest_edge"""]
            )
            snake_case__							:	Dict   	=	transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std					)
            set_requires_grad(self.text_encoder, _snake_case					)
            set_requires_grad(self.clip_model, _snake_case					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  Optional[int] = "auto"					)   ->Union[str, Any]:







            if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   snake_case__							:	List[Any]   	=	self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(_snake_case					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Any:
            self.enable_attention_slicing(_snake_case					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Union[str, Any]:
            set_requires_grad(self.vae, _snake_case					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Union[str, Any]:
            set_requires_grad(self.vae, _snake_case					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            set_requires_grad(self.unet, _snake_case					)






     def    lowercase_   (       self  :  str					)   ->Any:
            set_requires_grad(self.unet, _snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Optional[Any], _snake_case  :  Optional[int], _snake_case  :  Optional[int]					)   ->str:
            snake_case__							:	Optional[int]   	=	min(int(num_inference_steps * strength					), _snake_case					)

            snake_case__							:	List[Any]   	=	max(num_inference_steps - init_timestep, 0					)
            snake_case__							:	int   	=	self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  List[Any], _snake_case  :  List[str], _snake_case  :  List[Any], _snake_case  :  Any, _snake_case  :  Optional[Any], _snake_case  :  Union[str, Any]=None					)   ->Dict:







            if not isinstance(_snake_case, torch.Tensor					):
                   raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(_snake_case					)}'''					)

            snake_case__							:	List[str]   	=	image.to(device=_snake_case, dtype=_snake_case					)

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	Optional[int]   	=	[
                       self.vae.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(_snake_case					)
                   ]
                   snake_case__							:	Tuple   	=	torch.cat(_snake_case, dim=0					)
            else:
                   snake_case__							:	List[str]   	=	self.vae.encode(_snake_case					).latent_dist.sample(_snake_case					)

            # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            snake_case__							:	Any   	=	0.1_8_2_1_5 * init_latents
            snake_case__							:	Dict   	=	init_latents.repeat_interleave(_snake_case, dim=0					)

            snake_case__							:	List[str]   	=	randn_tensor(init_latents.shape, generator=_snake_case, device=_snake_case, dtype=_snake_case					)

            # get latents
            snake_case__							:	Optional[int]   	=	self.scheduler.add_noise(_snake_case, _snake_case, _snake_case					)
            snake_case__							:	Optional[Any]   	=	init_latents

            return latents






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  List[str]					)   ->Any:
            snake_case__							:	Dict   	=	self.coca_transform(_snake_case					).unsqueeze(0					)
            with torch.no_grad(), torch.cuda.amp.autocast():
                   snake_case__							:	List[Any]   	=	self.coca_model.generate(transformed_image.to(device=self.device, dtype=self.coca_model.dtype					)					)
            snake_case__							:	int   	=	self.coca_tokenizer.decode(generated[0].cpu().numpy()					)
            return generated.split('<end_of_text>'					)[0].replace('<start_of_text>', ''					).rstrip(' .,'					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  List[str], _snake_case  :  Optional[int]					)   ->int:
            snake_case__							:	Optional[int]   	=	self.feature_extractor.preprocess(_snake_case					)
            snake_case__							:	Optional[int]   	=	torch.from_numpy(clip_image_input['pixel_values'][0]					).unsqueeze(0					).to(self.device					).half()
            snake_case__							:	List[Any]   	=	self.clip_model.get_image_features(_snake_case					)
            snake_case__							:	List[str]   	=	image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=_snake_case					)
            snake_case__							:	Tuple   	=	image_embeddings_clip.repeat_interleave(_snake_case, dim=0					)
            return image_embeddings_clip






     @torch.enable_grad()
     def    lowercase_   (       self  :  List[Any], _snake_case  :  str, _snake_case  :  Tuple, _snake_case  :  Optional[Any], _snake_case  :  int, _snake_case  :  Any, _snake_case  :  List[Any], _snake_case  :  Dict, )   ->int:
            snake_case__							:	Optional[int]   	=	latents.detach().requires_grad_()

            snake_case__							:	Optional[int]   	=	self.scheduler.scale_model_input(_snake_case, _snake_case					)

            # predict the noise residual
            snake_case__							:	Optional[int]   	=	self.unet(_snake_case, _snake_case, encoder_hidden_states=_snake_case					).sample

            if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)					):
                   snake_case__							:	Optional[int]   	=	self.scheduler.alphas_cumprod[timestep]
                   snake_case__							:	Union[str, Any]   	=	1 - alpha_prod_t
                   # compute predicted original sample from predicted noise also called
                   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                   snake_case__							:	Tuple   	=	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                   snake_case__							:	int   	=	torch.sqrt(_snake_case					)
                   snake_case__							:	Any   	=	pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler, _snake_case					):
                   snake_case__							:	Dict   	=	self.scheduler.sigmas[index]
                   snake_case__							:	Tuple   	=	latents - sigma * noise_pred
            else:
                   raise ValueError(F'''scheduler type {type(self.scheduler					)} not supported'''					)

            # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            snake_case__							:	Optional[Any]   	=	1 / 0.1_8_2_1_5 * sample
            snake_case__							:	Union[str, Any]   	=	self.vae.decode(_snake_case					).sample
            snake_case__							:	str   	=	(image / 2 + 0.5).clamp(0, 1					)

            snake_case__							:	int   	=	transforms.Resize(self.feature_extractor_size					)(_snake_case					)
            snake_case__							:	Tuple   	=	self.normalize(_snake_case					).to(latents.dtype					)

            snake_case__							:	Tuple   	=	self.clip_model.get_image_features(_snake_case					)
            snake_case__							:	List[str]   	=	image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=_snake_case					)

            snake_case__							:	str   	=	spherical_dist_loss(_snake_case, _snake_case					).mean() * clip_guidance_scale

            snake_case__							:	List[Any]   	=	-torch.autograd.grad(_snake_case, _snake_case					)[0]

            if isinstance(self.scheduler, _snake_case					):
                   snake_case__							:	Any   	=	latents.detach() + grads * (sigma**2)
                   snake_case__							:	Optional[Any]   	=	noise_pred_original
            else:
                   snake_case__							:	int   	=	noise_pred_original - torch.sqrt(_snake_case					) * grads
            return noise_pred, latents






     @torch.no_grad()
     def __call__(       self  :  Any, _snake_case  :  List[Any], _snake_case  :  Dict, _snake_case  :  List[str] = None, _snake_case  :  Dict = None, _snake_case  :  Tuple = 5_1_2, _snake_case  :  Any = 5_1_2, _snake_case  :  Optional[Any] = 0.6, _snake_case  :  str = 5_0, _snake_case  :  Optional[Any] = 7.5, _snake_case  :  Any = 1, _snake_case  :  Union[str, Any] = 0.0, _snake_case  :  Any = 1_0_0, _snake_case  :  Optional[Any] = None, _snake_case  :  List[Any] = "pil", _snake_case  :  Union[str, Any] = True, _snake_case  :  Union[str, Any] = 0.8, _snake_case  :  Dict = 0.1, _snake_case  :  Tuple = 0.1, )   ->Optional[Any]:







            if isinstance(_snake_case, _snake_case					) and len(_snake_case					) != batch_size:
                   raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(_snake_case					)} generators.'''					)

            if height % 8 != 0 or width % 8 != 0:
                   raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''					)

            if isinstance(_snake_case, torch.Generator					) and batch_size > 1:
                   snake_case__							:	str   	=	[generator] + [None] * (batch_size - 1)

            snake_case__							:	Union[str, Any]   	=	[
                ("""model""", self.coca_model is None),
                ("""tokenizer""", self.coca_tokenizer is None),
                ("""transform""", self.coca_transform is None),
            ]
            snake_case__							:	Optional[int]   	=	[x[0] for x in coca_is_none if x[1]]
            snake_case__							:	int   	=	""", """.join(_snake_case					)
            # generate prompts with coca model if prompt is None
            if content_prompt is None:
                   if len(_snake_case					):
                          raise ValueError(
                              F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                              F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''					)
                   snake_case__							:	Optional[int]   	=	self.get_image_description(_snake_case					)
            if style_prompt is None:
                   if len(_snake_case					):
                          raise ValueError(
                              F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                              F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''					)
                   snake_case__							:	str   	=	self.get_image_description(_snake_case					)

            # get prompt text embeddings for content and style
            snake_case__							:	List[Any]   	=	self.tokenizer(
                _snake_case, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=_snake_case, return_tensors='pt', )
            snake_case__							:	List[Any]   	=	self.text_encoder(content_text_input.input_ids.to(self.device					)					)[0]

            snake_case__							:	Optional[int]   	=	self.tokenizer(
                _snake_case, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=_snake_case, return_tensors='pt', )
            snake_case__							:	Tuple   	=	self.text_encoder(style_text_input.input_ids.to(self.device					)					)[0]

            snake_case__							:	Optional[Any]   	=	slerp(_snake_case, _snake_case, _snake_case					)

            # duplicate text embeddings for each generation per prompt
            snake_case__							:	str   	=	text_embeddings.repeat_interleave(_snake_case, dim=0					)

            # set timesteps
            snake_case__							:	str   	=	"""offset""" in set(inspect.signature(self.scheduler.set_timesteps					).parameters.keys()					)
            snake_case__							:	Tuple   	=	{}
            if accepts_offset:
                   snake_case__							:	int   	=	1

            self.scheduler.set_timesteps(_snake_case, **_snake_case					)
            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            self.scheduler.timesteps.to(self.device					)

            snake_case__							:	Optional[int]   	=	self.get_timesteps(_snake_case, _snake_case, self.device					)
            snake_case__							:	Tuple   	=	timesteps[:1].repeat(_snake_case					)

            # Preprocess image
            snake_case__							:	str   	=	preprocess(_snake_case, _snake_case, _snake_case					)
            snake_case__							:	int   	=	self.prepare_latents(
                _snake_case, _snake_case, _snake_case, text_embeddings.dtype, self.device, _snake_case					)

            snake_case__							:	List[Any]   	=	preprocess(_snake_case, _snake_case, _snake_case					)
            snake_case__							:	Tuple   	=	self.prepare_latents(
                _snake_case, _snake_case, _snake_case, text_embeddings.dtype, self.device, _snake_case					)

            snake_case__							:	List[str]   	=	slerp(_snake_case, _snake_case, _snake_case					)

            if clip_guidance_scale > 0:
                   snake_case__							:	Union[str, Any]   	=	self.get_clip_image_embeddings(_snake_case, _snake_case					)
                   snake_case__							:	Optional[int]   	=	self.get_clip_image_embeddings(_snake_case, _snake_case					)
                   snake_case__							:	Optional[int]   	=	slerp(
                       _snake_case, _snake_case, _snake_case					)

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            snake_case__							:	str   	=	guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                   snake_case__							:	List[str]   	=	content_text_input.input_ids.shape[-1]
                   snake_case__							:	Optional[Any]   	=	self.tokenizer([''], padding='max_length', max_length=_snake_case, return_tensors='pt'					)
                   snake_case__							:	Any   	=	self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
                   # duplicate unconditional embeddings for each generation per prompt
                   snake_case__							:	Any   	=	uncond_embeddings.repeat_interleave(_snake_case, dim=0					)

                   # For classifier free guidance, we need to do two forward passes.
                   # Here we concatenate the unconditional and text embeddings into a single batch
                   # to avoid doing two forward passes
                   snake_case__							:	List[str]   	=	torch.cat([uncond_embeddings, text_embeddings]					)

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            snake_case__							:	Tuple   	=	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
            snake_case__							:	Tuple   	=	text_embeddings.dtype
            if latents is None:
                   if self.device.type == "mps":
                          # randn does not work reproducibly on mps
                          snake_case__							:	str   	=	torch.randn(_snake_case, generator=_snake_case, device='cpu', dtype=_snake_case					).to(
                              self.device					)
                   else:
                          snake_case__							:	Optional[int]   	=	torch.randn(_snake_case, generator=_snake_case, device=self.device, dtype=_snake_case					)
            else:
                   if latents.shape != latents_shape:
                          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''					)
                   snake_case__							:	Any   	=	latents.to(self.device					)

            # scale the initial noise by the standard deviation required by the scheduler
            snake_case__							:	Dict   	=	latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            snake_case__							:	Union[str, Any]   	=	"""eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
            snake_case__							:	str   	=	{}
            if accepts_eta:
                   snake_case__							:	str   	=	eta

            # check if the scheduler accepts generator
            snake_case__							:	Optional[Any]   	=	"""generator""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
            if accepts_generator:
                   snake_case__							:	Tuple   	=	generator

            with self.progress_bar(total=_snake_case					):
                   for i, t in enumerate(_snake_case					):
                          # expand the latents if we are doing classifier free guidance
                          snake_case__							:	Optional[Any]   	=	torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
                          snake_case__							:	Optional[int]   	=	self.scheduler.scale_model_input(_snake_case, _snake_case					)

                          # predict the noise residual
                          snake_case__							:	Dict   	=	self.unet(_snake_case, _snake_case, encoder_hidden_states=_snake_case					).sample

                          # perform classifier free guidance
                          if do_classifier_free_guidance:
                                 snake_case__							:	str   	=	noise_pred.chunk(2					)
                                 snake_case__							:	Optional[int]   	=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                          # perform clip guidance
                          if clip_guidance_scale > 0:
                                 snake_case__							:	int   	=	(
                                     text_embeddings.chunk(2					)[1] if do_classifier_free_guidance else text_embeddings
                                 )
                                 snake_case__							:	Union[str, Any]   	=	self.cond_fn(
                                     _snake_case, _snake_case, _snake_case, _snake_case, _snake_case, _snake_case, _snake_case, )

                          # compute the previous noisy sample x_t -> x_t-1
                          snake_case__							:	Any   	=	self.scheduler.step(_snake_case, _snake_case, _snake_case, **_snake_case					).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            snake_case__							:	Optional[Any]   	=	1 / 0.1_8_2_1_5 * latents
            snake_case__							:	Any   	=	self.vae.decode(_snake_case					).sample

            snake_case__							:	Optional[Any]   	=	(image / 2 + 0.5).clamp(0, 1					)
            snake_case__							:	Optional[Any]   	=	image.cpu().permute(0, 2, 3, 1					).numpy()

            if output_type == "pil":
                   snake_case__							:	List[str]   	=	self.numpy_to_pil(_snake_case					)

            if not return_dict:
                   return (image, None)

            return StableDiffusionPipelineOutput(images=_snake_case, nsfw_content_detected=_snake_case					)








from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase  :					str						    =							"""\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

lowercase  :					Dict						    =							"""\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

lowercase  :					int						    =							"""
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      float(spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.config_name not in [
               "sst2",
               "mnli",
               "mnli_mismatched",
               "mnli_matched",
               "cola",
               "stsb",
               "mrpc",
               "qqp",
               "qnli",
               "rte",
               "wnli",
               "hans",
           ]:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""      )
           return datasets.MetricInfo(
               description=_DESCRIPTION		,citation=_CITATION		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
                   {
                       """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""      ),
                       """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""      ),
                   }      )		,codebase_urls=[]		,reference_urls=[]		,format="""numpy"""		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(snake_case		,snake_case      )}
           elif self.config_name == "stsb":
                  return pearson_and_spearman(snake_case		,snake_case      )
           elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(snake_case		,snake_case      )
           elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(snake_case		,snake_case      )}
           else:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""      )
