'''simple docstring'''

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						KandinskyVaaPriorEmbaEmbPipeline
   __UpperCAmelCase           =						["""prompt""", """image"""]
   __UpperCAmelCase           =						["""prompt""", """image"""]
   __UpperCAmelCase           =						[
       """num_images_per_prompt""",
       """strength""",
       """generator""",
       """num_inference_steps""",
       """latents""",
       """negative_prompt""",
       """guidance_scale""",
       """output_type""",
       """return_dict""",
   ]
   __UpperCAmelCase           =						False



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        return 32



   @property
   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        return 32



   @property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        return self.time_input_dim



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.time_input_dim * 4



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return 100



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :str       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
        return tokenizer



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =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  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        torch.manual_seed(0     )

        _UpperCamelCase  :Optional[int]       ={
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        _UpperCamelCase  :List[str]       =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  :Dict       =nn.Parameter(torch.ones(model.clip_std.shape     )     )
        return model



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :Any       =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  :Union[str, Any]       =CLIPVisionModelWithProjection(lowerCAmelCase__     )
        return model



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =CLIPImageProcessor(
            crop_size=224					,      do_center_crop=lowerCAmelCase__					,      do_normalize=lowerCAmelCase__					,      do_resize=lowerCAmelCase__					,      image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]					,      image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,      resample=3					,      size=224					,      )

        return image_processor



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.dummy_prior
        _UpperCamelCase  :Optional[int]       =self.dummy_image_encoder
        _UpperCamelCase  :Tuple       =self.dummy_text_encoder
        _UpperCamelCase  :Union[str, Any]       =self.dummy_tokenizer
        _UpperCamelCase  :Any       =self.dummy_image_processor

        _UpperCamelCase  :Dict       =UnCLIPScheduler(
            variance_type="""fixed_small_log"""					,      prediction_type="""sample"""					,      num_train_timesteps=1_000					,      clip_sample=lowerCAmelCase__					,      clip_sample_range=10.0					,      )

        _UpperCamelCase  :int       ={
            """prior""": prior,
            """image_encoder""": image_encoder,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """scheduler""": scheduler,
            """image_processor""": image_processor,
        }

        return components



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=0     )				->     Optional[Any]:
        """simple docstring"""





        if str(lowerCAmelCase__     ).startswith("""mps"""     ):
             _UpperCamelCase  :List[Any]       =torch.manual_seed(lowerCAmelCase__     )
        else:
             _UpperCamelCase  :Optional[int]       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       =floats_tensor((1, 3, 64, 64)					,      rng=random.Random(lowerCAmelCase__     )     ).to(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =image.cpu().permute(0					,      2					,      3					,      1     )[0]
        _UpperCamelCase  :Dict       =Image.fromarray(np.uinta(lowerCAmelCase__     )     ).convert("""RGB"""     ).resize((256, 256)     )

        _UpperCamelCase  :Tuple       ={
            """prompt""": """horse""",
            """image""": init_image,
            """strength""": 0.5,
            """generator""": generator,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       ="""cpu"""

        _UpperCamelCase  :List[Any]       =self.get_dummy_components()

        _UpperCamelCase  :int       =self.pipeline_class(**lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =pipe.to(lowerCAmelCase__     )

        pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =pipe(**self.get_dummy_inputs(lowerCAmelCase__     )     )
        _UpperCamelCase  :Any       =output.image_embeds

        _UpperCamelCase  :Optional[Any]       =pipe(
            **self.get_dummy_inputs(lowerCAmelCase__     )					,      return_dict=lowerCAmelCase__					,      )[0]

        _UpperCamelCase  :int       =image[0, -10:]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        _UpperCamelCase  :int       =np.array(
            [
                0.107_1284,
                1.333_0271,
                0.6126_0223,
                -0.669_1065,
                -0.384_6852,
                -1.030_3661,
                0.2271_6111,
                0.0334_8901,
                0.3004_0675,
                -0.2480_5029,
            ]     )

        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  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =torch_device == """cpu"""
        _UpperCamelCase  :List[Any]       =True
        _UpperCamelCase  :Dict       =False

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




   @skip_mps
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =torch_device == """cpu"""
        _UpperCamelCase  :Union[str, Any]       =False

        self._test_attention_slicing_forward_pass(
            test_max_difference=lowerCAmelCase__					,      test_mean_pixel_difference=lowerCAmelCase__					,      )
'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase :				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}
}
"""

_lowerCamelCase :				List[Any]														= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_lowerCamelCase :				Union[str, Any]														= """
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    lowerCamelCase__  (   datasets.Metric     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """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  _UpperCamelCase		(    self     )				->     Tuple:
        """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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}