






'''simple docstring'''







from sklearn.metrics import precision_score

import datasets


__A      :       List[str]      =	'\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n'


__A      :       Dict      =	'\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. 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 `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` 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. This option 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 (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - \'warn\': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'precision\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'precision\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(results)\n        {\'precision\': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'precision\': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results[\'precision\']])\n        [0.67, 0.0, 0.0]\n'


__A      :       Union[str, Any]      =	'\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 					a__    (			datasets.Metric						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	List[Any]					)  ->       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.precision_score.html"]				,				)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	List[str]				,				lowercase__    :	str				,				lowercase__    :	Dict=None				,				lowercase__    :	Optional[Any]=1				,				lowercase__    :	Any="binary"				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Union[str, Any]="warn"				,				)  ->       Optional[int]:
           __A		   =      precision_score(
               lowercase__				,				lowercase__				,				labels=lowercase__				,				pos_label=lowercase__				,				average=lowercase__				,				sample_weight=lowercase__				,				zero_division=lowercase__				,				)
           return {"precision": float(lowercase__					) if score.size == 1 else score}










'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 					a__    (			lowerCamelCase ,						unittest.TestCase						):
     '''simple docstring'''







     _snake_case             =  UnCLIPPipeline
     _snake_case             =  TEXT_TO_IMAGE_PARAMS - {
         'negative_prompt',
         'height',
         'width',
         'negative_prompt_embeds',
         'guidance_scale',
         'prompt_embeds',
         'cross_attention_kwargs',
     }
     _snake_case             =  TEXT_TO_IMAGE_BATCH_PARAMS
     _snake_case             =  [
         'generator',
         'return_dict',
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]
     _snake_case             =  False






     @property
     def     _UpperCamelCase   (self    :	Dict					)  ->       Union[str, Any]:
           return 32






     @property
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           return 32






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       Dict:
           return self.time_input_dim






     @property
     def     _UpperCamelCase   (self    :	List[Any]					)  ->       int:
           return self.time_input_dim * 4






     @property
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       List[str]:
           return 100






     @property
     def     _UpperCamelCase   (self    :	Tuple					)  ->       Optional[Any]:
           __A		   =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
           return tokenizer






     @property
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           torch.manual_seed(0					)
           __A		   =      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=1000				,				)
           return CLIPTextModelWithProjection(lowercase__					)






     @property
     def     _UpperCamelCase   (self    :	int					)  ->       Optional[Any]:
           torch.manual_seed(0					)

           __A		   =      {
               "num_attention_heads": 2,
               "attention_head_dim": 12,
               "embedding_dim": self.text_embedder_hidden_size,
               "num_layers": 1,
           }

           __A		   =      PriorTransformer(**lowercase__					)
           return model






     @property
     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Union[str, Any]:
           torch.manual_seed(0					)

           __A		   =      {
               "clip_embeddings_dim": self.text_embedder_hidden_size,
               "time_embed_dim": self.time_embed_dim,
               "cross_attention_dim": self.cross_attention_dim,
           }

           __A		   =      UnCLIPTextProjModel(**lowercase__					)
           return model






     @property
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Tuple:
           torch.manual_seed(0					)

           __A		   =      {
               "sample_size": 32,
               # RGB in channels
               "in_channels": 3,
               # Out channels is double in channels because predicts mean and variance
               "out_channels": 6,
               "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
               "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
               "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "layers_per_block": 1,
               "cross_attention_dim": self.cross_attention_dim,
               "attention_head_dim": 4,
               "resnet_time_scale_shift": "scale_shift",
               "class_embed_type": "identity",
           }

           __A		   =      UNetaDConditionModel(**lowercase__					)
           return model






     @property
     def     _UpperCamelCase   (self    :	Any					)  ->       str:
           return {
               "sample_size": 64,
               "layers_per_block": 1,
               "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
               "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "in_channels": 6,
               "out_channels": 3,
           }






     @property
     def     _UpperCamelCase   (self    :	List[str]					)  ->       List[str]:
           torch.manual_seed(0					)

           __A		   =      UNetaDModel(**self.dummy_super_res_kwargs					)
           return model






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       Optional[Any]:
           # seeded differently to get different unet than `self.dummy_super_res_first`
           torch.manual_seed(1					)

           __A		   =      UNetaDModel(**self.dummy_super_res_kwargs					)
           return model






     def     _UpperCamelCase   (self    :	Any					)  ->       Any:
           __A		   =      self.dummy_prior
           __A		   =      self.dummy_decoder
           __A		   =      self.dummy_text_proj
           __A		   =      self.dummy_text_encoder
           __A		   =      self.dummy_tokenizer
           __A		   =      self.dummy_super_res_first
           __A		   =      self.dummy_super_res_last

           __A		   =      UnCLIPScheduler(
               variance_type="fixed_small_log"				,				prediction_type="sample"				,				num_train_timesteps=1000				,				clip_sample_range=5.0				,				)

           __A		   =      UnCLIPScheduler(
               variance_type="learned_range"				,				prediction_type="epsilon"				,				num_train_timesteps=1000				,				)

           __A		   =      UnCLIPScheduler(
               variance_type="fixed_small_log"				,				prediction_type="epsilon"				,				num_train_timesteps=1000				,				)

           __A		   =      {
               "prior": prior,
               "decoder": decoder,
               "text_proj": text_proj,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "super_res_first": super_res_first,
               "super_res_last": super_res_last,
               "prior_scheduler": prior_scheduler,
               "decoder_scheduler": decoder_scheduler,
               "super_res_scheduler": super_res_scheduler,
           }

           return components






     def     _UpperCamelCase   (self    :	int				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Any=0					)  ->       str:
           if str(lowercase__					).startswith("mps"					):
                 __A		   =      torch.manual_seed(lowercase__					)
           else:
                 __A		   =      torch.Generator(device=lowercase__					).manual_seed(lowercase__					)
           __A		   =      {
               "prompt": "horse",
               "generator": generator,
               "prior_num_inference_steps": 2,
               "decoder_num_inference_steps": 2,
               "super_res_num_inference_steps": 2,
               "output_type": "numpy",
           }
           return inputs






     def     _UpperCamelCase   (self    :	str					)  ->       Tuple:
           __A		   =      "cpu"

           __A		   =      self.get_dummy_components()

           __A		   =      self.pipeline_class(**lowercase__					)
           __A		   =      pipe.to(lowercase__					)

           pipe.set_progress_bar_config(disable=lowercase__					)

           __A		   =      pipe(**self.get_dummy_inputs(lowercase__					)					)
           __A		   =      output.images

           __A		   =      pipe(
               **self.get_dummy_inputs(lowercase__					)				,				return_dict=lowercase__				,				)[0]

           __A		   =      image[0, -3:, -3:, -1]
           __A		   =      image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           __A		   =      np.array(
               [
                   0.99_97,
                   0.99_88,
                   0.00_28,
                   0.99_97,
                   0.99_84,
                   0.99_65,
                   0.00_29,
                   0.99_86,
                   0.00_25,
               ]					)

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






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[Any]:
           __A		   =      torch.device("cpu"					)






           class 					a__    :
                '''simple docstring'''







                _snake_case             =  1







           __A		   =      self.get_dummy_components()

           __A		   =      self.pipeline_class(**lowercase__					)
           __A		   =      pipe.to(lowercase__					)

           __A		   =      components["prior"]
           __A		   =      components["decoder"]
           __A		   =      components["super_res_first"]
           __A		   =      components["tokenizer"]
           __A		   =      components["text_encoder"]

           __A		   =      torch.Generator(device=lowercase__					).manual_seed(0					)
           __A		   =      prior.dtype
           __A		   =      1

           __A		   =      (batch_size, prior.config.embedding_dim)
           __A		   =      pipe.prepare_latents(
               lowercase__				,				dtype=lowercase__				,				device=lowercase__				,				generator=lowercase__				,				latents=lowercase__				,				scheduler=DummyScheduler()					)
           __A		   =      (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
           __A		   =      pipe.prepare_latents(
               lowercase__				,				dtype=lowercase__				,				device=lowercase__				,				generator=lowercase__				,				latents=lowercase__				,				scheduler=DummyScheduler()					)

           __A		   =      (
               batch_size,
               super_res_first.config.in_channels // 2,
               super_res_first.config.sample_size,
               super_res_first.config.sample_size,
           )
           __A		   =      pipe.prepare_latents(
               lowercase__				,				dtype=lowercase__				,				device=lowercase__				,				generator=lowercase__				,				latents=lowercase__				,				scheduler=DummyScheduler()					)

           pipe.set_progress_bar_config(disable=lowercase__					)

           __A		   =      "this is a prompt example"

           __A		   =      torch.Generator(device=lowercase__					).manual_seed(0					)
           __A		   =      pipe(
               [prompt]				,				generator=lowercase__				,				prior_num_inference_steps=2				,				decoder_num_inference_steps=2				,				super_res_num_inference_steps=2				,				prior_latents=lowercase__				,				decoder_latents=lowercase__				,				super_res_latents=lowercase__				,				output_type="np"				,				)
           __A		   =      output.images

           __A		   =      tokenizer(
               lowercase__				,				padding="max_length"				,				max_length=tokenizer.model_max_length				,				return_tensors="pt"				,				)
           __A		   =      text_encoder(text_inputs.input_ids					)
           __A		   =      text_inputs.attention_mask

           __A		   =      torch.Generator(device=lowercase__					).manual_seed(0					)
           __A		   =      pipe(
               generator=lowercase__				,				prior_num_inference_steps=2				,				decoder_num_inference_steps=2				,				super_res_num_inference_steps=2				,				prior_latents=lowercase__				,				decoder_latents=lowercase__				,				super_res_latents=lowercase__				,				text_model_output=lowercase__				,				text_attention_mask=lowercase__				,				output_type="np"				,				)[0]

           # make sure passing text embeddings manually is identical
           assert np.abs(image - image_from_text					).max() < 1e-4






     @skip_mps
     def     _UpperCamelCase   (self    :	str					)  ->       str:
           __A		   =      torch_device == "cpu"

           self._test_attention_slicing_forward_pass(test_max_difference=lowercase__				,				expected_max_diff=0.01					)






     @skip_mps
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       str:
           __A		   =      torch_device == "cpu"
           __A		   =      True
           __A		   =      [
               "prior_num_inference_steps",
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

           self._test_inference_batch_single_identical(
               test_max_difference=lowercase__				,				relax_max_difference=lowercase__				,				additional_params_copy_to_batched_inputs=lowercase__				,				)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[int]:
           __A		   =      [
               "prior_num_inference_steps",
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

           if torch_device == "mps":
                 # TODO: MPS errors with larger batch sizes
                 __A		   =      [2, 3]
                 self._test_inference_batch_consistent(
                     batch_sizes=lowercase__				,				additional_params_copy_to_batched_inputs=lowercase__				,				)
           else:
                 self._test_inference_batch_consistent(
                     additional_params_copy_to_batched_inputs=lowercase__					)






     @skip_mps
     def     _UpperCamelCase   (self    :	int					)  ->       int:
           return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       int:
           return super().test_save_load_local()






     @skip_mps
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Any:
           return super().test_save_load_optional_components()







@nightly
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







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






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Tuple:
           __A		   =      load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_horse_cpu.npy"					)

           __A		   =      UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"					)
           pipeline.set_progress_bar_config(disable=lowercase__					)

           __A		   =      torch.manual_seed(0					)
           __A		   =      pipeline(
               "horse"				,				num_images_per_prompt=1				,				generator=lowercase__				,				output_type="np"				,				)

           __A		   =      output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image					).max() < 1e-1







@slow
@require_torch_gpu
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







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






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Dict:
           __A		   =      load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_horse_fp16.npy"					)

           __A		   =      UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"				,				torch_dtype=torch.floataa					)
           __A		   =      pipeline.to(lowercase__					)
           pipeline.set_progress_bar_config(disable=lowercase__					)

           __A		   =      torch.Generator(device="cpu"					).manual_seed(0					)
           __A		   =      pipeline(
               "horse"				,				generator=lowercase__				,				output_type="np"				,				)

           __A		   =      output.images[0]

           assert image.shape == (256, 256, 3)

           assert_mean_pixel_difference(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Any:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           __A		   =      UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"				,				torch_dtype=torch.floataa					)
           __A		   =      pipe.to(lowercase__					)
           pipe.set_progress_bar_config(disable=lowercase__					)
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           __A		   =      pipe(
               "horse"				,				num_images_per_prompt=1				,				prior_num_inference_steps=2				,				decoder_num_inference_steps=2				,				super_res_num_inference_steps=2				,				output_type="np"				,				)

           __A		   =      torch.cuda.max_memory_allocated()
           # make sure that less than 7 GB is allocated
           assert mem_bytes < 7 * 10**9


