"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			KandinskyPriorPipeline
					snake_case__       =			["prompt"]
					snake_case__       =			["prompt", "negative_prompt"]
					snake_case__       =			[
					    "num_images_per_prompt",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__       =			False







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 100







					@property
					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
									return tokenizer







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPTextConfig(
									    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,layer_norm_eps=1e-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_000   ,)
									return CLIPTextModelWithProjection(lowerCamelCase__      )







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									torch.manual_seed(0      )

									UpperCAmelCase__					=							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__					=							PriorTransformer(**lowerCamelCase__      )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									UpperCAmelCase__					=							nn.Parameter(torch.ones(model.clip_std.shape      )      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,image_size=224   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=14   ,)

									UpperCAmelCase__					=							CLIPVisionModelWithProjection(lowerCamelCase__      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							CLIPImageProcessor(
									    crop_size=224   ,do_center_crop=lowerCamelCase__   ,do_normalize=lowerCamelCase__   ,do_resize=lowerCamelCase__   ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,resample=3   ,size=224   ,)

									return image_processor







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.dummy_prior
									UpperCAmelCase__					=							self.dummy_image_encoder
									UpperCAmelCase__					=							self.dummy_text_encoder
									UpperCAmelCase__					=							self.dummy_tokenizer
									UpperCAmelCase__					=							self.dummy_image_processor

									UpperCAmelCase__					=							UnCLIPScheduler(
									    variance_type='fixed_small_log'   ,prediction_type='sample'   ,num_train_timesteps=1_000   ,clip_sample=lowerCamelCase__   ,clip_sample_range=1_0.0   ,)

									UpperCAmelCase__					=							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int]=0      ):
									if str(lowerCamelCase__      ).startswith('mps'      ):
													UpperCAmelCase__					=							torch.manual_seed(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )
									UpperCAmelCase__					=							{
									    'prompt': 'horse',
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							'cpu'

									UpperCAmelCase__					=							self.get_dummy_components()

									UpperCAmelCase__					=							self.pipeline_class(**lowerCamelCase__      )
									UpperCAmelCase__					=							pipe.to(lowerCamelCase__      )

									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									UpperCAmelCase__					=							pipe(**self.get_dummy_inputs(lowerCamelCase__      )      )
									UpperCAmelCase__					=							output.image_embeds

									UpperCAmelCase__					=							pipe(
									    **self.get_dummy_inputs(lowerCamelCase__      )   ,return_dict=lowerCamelCase__   ,)[0]

									UpperCAmelCase__					=							image[0, -10:]
									UpperCAmelCase__					=							image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__					=							np.array(
									    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

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







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							True
									UpperCAmelCase__					=							False

									self._test_inference_batch_single_identical(
									    test_max_difference=lowerCamelCase__   ,relax_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
									            'references': datasets.Sequence(datasets.Value('int32'      )      ),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'      ),
									            'references': datasets.Value('int32'      ),
									        }      )   ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']   ,)







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



