

'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							A__   (	A__  ):
		A__					=    42
		A__					=    42





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





							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=_SCREAMING_SNAKE_CASE							, scheduler=_SCREAMING_SNAKE_CASE			)





		@torch.no_grad()
		def __call__(		self				:     Tuple							, _a				:     List[str] = 1							, _a				:     str = 50							, _a				:     List[str] = None							, _a				:     Union[str, Any] = "pil"							, _a				:     Dict = True							, **_a				:     List[str]							, )     ->   Union[Tuple, ImagePipelineOutput]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =self.unet.config.sample_size
							_SCREAMING_SNAKE_CASE							   =(batch_size, 3, img_size, img_size)

							_SCREAMING_SNAKE_CASE							   =self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							_SCREAMING_SNAKE_CASE							   =randn_tensor(_SCREAMING_SNAKE_CASE							, generator=_SCREAMING_SNAKE_CASE							, device=self.device			) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

							for t in self.progress_bar(self.scheduler.timesteps			):
												# here sigma_t == t_i from the paper
												_SCREAMING_SNAKE_CASE							   =self.scheduler.schedule[t]
												_SCREAMING_SNAKE_CASE							   =self.scheduler.schedule[t - 1] if t > 0 else 0

												# 1. Select temporarily increased noise level sigma_hat
												# 2. Add new noise to move from sample_i to sample_hat
												_SCREAMING_SNAKE_CASE							   =self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, generator=_SCREAMING_SNAKE_CASE			)

												# 3. Predict the noise residual given the noise magnitude `sigma_hat`
												# The model inputs and output are adjusted by following eq. (213) in [1].
												_SCREAMING_SNAKE_CASE							   =(sigma_hat / 2) * model((sample_hat + 1) / 2							, sigma_hat / 2			).sample

												# 4. Evaluate dx/dt at sigma_hat
												# 5. Take Euler step from sigma to sigma_prev
												_SCREAMING_SNAKE_CASE							   =self.scheduler.step(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE			)

												if sigma_prev != 0:
																	# 6. Apply 2nd order correction
																	# The model inputs and output are adjusted by following eq. (213) in [1].
																	_SCREAMING_SNAKE_CASE							   =(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							, sigma_prev / 2			).sample
																	_SCREAMING_SNAKE_CASE							   =self.scheduler.step_correct(
																	    _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, step_output.prev_sample							, step_output['derivative']							, )
												_SCREAMING_SNAKE_CASE							   =step_output.prev_sample

							_SCREAMING_SNAKE_CASE							   =(sample / 2 + 0.5).clamp(0							, 1			)
							_SCREAMING_SNAKE_CASE							   =sample.cpu().permute(0							, 2							, 3							, 1			).numpy()
							if output_type == "pil":
												_SCREAMING_SNAKE_CASE							   =self.numpy_to_pil(_SCREAMING_SNAKE_CASE			)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE			)



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

import datasets


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

lowerCamelCase			:				List[str] 			=					"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

lowerCamelCase			:				Dict 			=					"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Dict    )				->      List[str]:
     """simple docstring"""




     return float((preds == labels).mean()    )


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Any    )				->      Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =simple_accuracy(_UpperCamelCase							,			_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =float(fa_score(y_true=_UpperCamelCase							,			y_pred=_UpperCamelCase    )    )
     return {
         "accuracy": acc,
         "f1": fa,
     }


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[int]    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =float(pearsonr(_UpperCamelCase							,			_UpperCamelCase    )[0]    )
     _SCREAMING_SNAKE_CASE							   =float(spearmanr(_UpperCamelCase							,			_UpperCamelCase    )[0]    )
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
     }







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





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





       '''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 						A  (		self				:     str							, _a				:     str							, _a				:     List[Any]			)     ->   str:





       '''simple docstring'''






       if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(_a							, _a			)}
       elif self.config_name == "stsb":
            return pearson_and_spearman(_a							, _a			)
       elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(_a							, _a			)
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(_a							, _a			)}
       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"]'			)
