




'''simple docstring'''




from sklearn.metrics import mean_absolute_error

import datasets


a_   :	Union[str, Any]							   = '''\
@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}
}
'''

a_   :	int							   = '''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


a_   :	Optional[int]							   = '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''


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






       def   lowercase__  (							self						):



              """simple docstring"""







              return datasets.MetricInfo(
                  description=_DESCRIPTION,	citation=_CITATION,	inputs_description=_KWARGS_DESCRIPTION,	features=datasets.Features(self._get_feature_types()						),	reference_urls=[
                      '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
                  ],	)






       def   lowercase__  (							self						):



              """simple docstring"""







              if self.config_name == "multilist":
                     return {
                         "predictions": datasets.Sequence(datasets.Value('''float'''						)						),
                         "references": datasets.Sequence(datasets.Value('''float'''						)						),
                     }
              else:
                     return {
                         "predictions": datasets.Value('''float'''						),
                         "references": datasets.Value('''float'''						),
                     }






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase=None,	lowerCAmelCase="uniform_average"						):



              """simple docstring"""







              lowerCamelCase_							=mean_absolute_error(_A,	_A,	sample_weight=_A,	multioutput=_A						)

              return {"mae": mae_score}










'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Tuple   		=KandinskyPipeline
       lowercase			:					Optional[int]   		=[
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       lowercase			:					Union[str, Any]   		=['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       lowercase			:					Dict   		=[
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       lowercase			:					Dict   		=False






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim * 4






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 100






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
              return tokenizer






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=MCLIPConfig(
                  numDims=self.cross_attention_dim,	transformerDimensions=self.text_embedder_hidden_size,	hidden_size=self.text_embedder_hidden_size,	intermediate_size=37,	num_attention_heads=4,	num_hidden_layers=5,	vocab_size=1_005,	)

              lowerCamelCase_							=MultilingualCLIP(lowerCAmelCase						)
              lowerCamelCase_							=text_encoder.eval()

              return text_encoder






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)

              lowerCamelCase_							={
                  '''in_channels''': 4,
                  # Out channels is double in channels because predicts mean and variance
                  '''out_channels''': 8,
                  '''addition_embed_type''': '''text_image''',
                  '''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,
                  '''encoder_hid_dim''': self.text_embedder_hidden_size,
                  '''encoder_hid_dim_type''': '''text_image_proj''',
                  '''cross_attention_dim''': self.cross_attention_dim,
                  '''attention_head_dim''': 4,
                  '''resnet_time_scale_shift''': '''scale_shift''',
                  '''class_embed_type''': None,
              }

              lowerCamelCase_							=UNetaDConditionModel(**lowerCAmelCase						)
              return model






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return {
                  "block_out_channels": [32, 64],
                  "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                  "in_channels": 3,
                  "latent_channels": 4,
                  "layers_per_block": 1,
                  "norm_num_groups": 8,
                  "norm_type": "spatial",
                  "num_vq_embeddings": 12,
                  "out_channels": 3,
                  "up_block_types": [
                      "AttnUpDecoderBlock2D",
                      "UpDecoderBlock2D",
                  ],
                  "vq_embed_dim": 4,
              }






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=VQModel(**self.dummy_movq_kwargs						)
              return model






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=self.dummy_tokenizer
              lowerCamelCase_							=self.dummy_unet
              lowerCamelCase_							=self.dummy_movq

              lowerCamelCase_							=DDIMScheduler(
                  num_train_timesteps=1_000,	beta_schedule='''linear''',	beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	clip_sample=lowerCAmelCase,	set_alpha_to_one=lowerCAmelCase,	steps_offset=1,	prediction_type='''epsilon''',	thresholding=lowerCAmelCase,	)

              lowerCamelCase_							={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=0						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(seed + 1						)						).to(lowerCAmelCase						)
              if str(lowerCAmelCase						).startswith('''mps'''						):
                     lowerCamelCase_							=torch.manual_seed(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
              lowerCamelCase_							={
                  '''prompt''': '''horse''',
                  '''image_embeds''': image_embeds,
                  '''negative_image_embeds''': negative_image_embeds,
                  '''generator''': generator,
                  '''height''': 64,
                  '''width''': 64,
                  '''guidance_scale''': 4.0,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''np''',
              }
              return inputs






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''

              lowerCamelCase_							=self.get_dummy_components()

              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase						)
              lowerCamelCase_							=pipe.to(lowerCAmelCase						)

              pipe.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							=pipe(**self.get_dummy_inputs(lowerCAmelCase						)						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=pipe(
                  **self.get_dummy_inputs(lowerCAmelCase						),	return_dict=lowerCAmelCase,	)[0]

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

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

              lowerCamelCase_							=np.array(
                  [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

              assert (
                  np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
              assert (
                  np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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






       def   lowercase__  (							self						):



              """simple docstring"""







              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''						)

              lowerCamelCase_							=KandinskyPriorPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-1-prior''',	torch_dtype=torch.floataa						)
              pipe_prior.to(lowerCAmelCase						)

              lowerCamelCase_							=KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipeline.to(lowerCAmelCase						)
              pipeline.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							='''red cat, 4k photo'''

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_,   lowerCamelCase_							=pipe_prior(
                  lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=5,	negative_prompt='''''',	).to_tuple()

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_							=pipeline(
                  lowerCAmelCase,	image_embeds=lowerCAmelCase,	negative_image_embeds=lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=100,	output_type='''np''',	)

              lowerCamelCase_							=output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(lowerCAmelCase,	lowerCAmelCase						)




