"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = ShapEImgaImgPipeline
					snake_case__				    = ["image"]
					snake_case__				    = ["image"]
					snake_case__				    = [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return 32







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







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







					@property
					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return 8







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size    ,image_size=64    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_channels=3    ,num_hidden_layers=5    ,patch_size=1    ,)

									UpperCAmelCase__             =							CLIPVisionModel(lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									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







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

									UpperCAmelCase__             =							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 16,
									    'embedding_dim': self.time_input_dim,
									    'num_embeddings': 32,
									    'embedding_proj_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'num_layers': 1,
									    'clip_embed_dim': self.time_input_dim * 2,
									    'additional_embeddings': 0,
									    'time_embed_act_fn': 'gelu',
									    'norm_in_type': 'layer',
									    'embedding_proj_norm_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									UpperCAmelCase__             =							PriorTransformer(**lowerCamelCase__			)
									return model







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

									UpperCAmelCase__             =							{
									    'param_shapes': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    'd_latent': self.time_input_dim,
									    'd_hidden': self.renderer_dim,
									    'n_output': 12,
									    'background': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									UpperCAmelCase__             =							ShapERenderer(**lowerCamelCase__			)
									return model







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							self.dummy_prior
									UpperCAmelCase__             =							self.dummy_image_encoder
									UpperCAmelCase__             =							self.dummy_image_processor
									UpperCAmelCase__             =							self.dummy_renderer

									UpperCAmelCase__             =							HeunDiscreteScheduler(
									    beta_schedule='exp'    ,num_train_timesteps=1_024    ,prediction_type='sample'    ,use_karras_sigmas=lowerCamelCase__    ,clip_sample=lowerCamelCase__    ,clip_sample_range=1.0    ,)
									UpperCAmelCase__             =							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'image_processor': image_processor,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': input_image,
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									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.images[0]
									UpperCAmelCase__             =							image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									UpperCAmelCase__             =							np.array(
									    [
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									    ]			)

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







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							torch_device == 'cpu'
									UpperCAmelCase__             =							True
									self._test_inference_batch_single_identical(
									    batch_size=2    ,test_max_difference=lowerCamelCase__    ,relax_max_difference=lowerCamelCase__    ,)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							2

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)

									for key in inputs.keys():
													if key in self.batch_params:
																	UpperCAmelCase__             =							batch_size * [inputs[key]]

									UpperCAmelCase__             =							pipe(**lowerCamelCase__    ,num_images_per_prompt=lowerCamelCase__			)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_img2img_out.npy'			)
									UpperCAmelCase__             =							ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(0			)

									UpperCAmelCase__             =							pipe(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,guidance_scale=3.0    ,num_inference_steps=64    ,frame_size=64    ,output_type='np'    ,).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)







'''simple docstring'''






from sklearn.metrics import recall_score

import datasets


__A					:  Dict											=      "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


__A					:  List[Any]											=      "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


__A					:  str											=      "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"






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







    """simple docstring"""



    def __lowercase   (   self    :  str		)   ->  Dict:
      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.recall_score.html"""]    ,						)





    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  Optional[Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Optional[Any]=None    ,						lowerCamelCase    :  Optional[int]=1    ,						lowerCamelCase    :  Union[str, Any]="binary"    ,						lowerCamelCase    :  Any=None    ,						lowerCamelCase    :  str="warn"    ,						)   ->  List[Any]:
      lowerCAmelCase_			:      Optional[int]			=   recall_score(
          lowerCamelCase    ,						lowerCamelCase    ,						labels=lowerCamelCase    ,						pos_label=lowerCamelCase    ,						average=lowerCamelCase    ,						sample_weight=lowerCamelCase    ,						zero_division=lowerCamelCase    ,						)
      return {"recall": float(lowerCamelCase		) if score.size == 1 else score}



