

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    A__(a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							int   			=	KandinskyPipeline
						_A  :							str   			=	[
						    '''prompt''',
						    '''image_embeds''',
						    '''negative_image_embeds''',
						]
						_A  :							Tuple   			=	['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
						_A  :							str   			=	[
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''negative_prompt''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_A  :							Any   			=	False





						@property
						def 	UpperCamelCase__			(  self  )      ->				Dict:
									return 32





						@property
						def 	UpperCamelCase__			(  self  )      ->				int:
									return 32





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





						@property
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									return self.time_input_dim * 4





						@property
						def 	UpperCamelCase__			(  self  )      ->				Any:
									return 100





						@property
						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_		:		Optional[Any]				    =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
									return tokenizer





						@property
						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									torch.manual_seed(0  )
									a_		:		Any				    =				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       ,     )

									a_		:		Tuple				    =				MultilingualCLIP(_lowercase  )
									a_		:		List[str]				    =				text_encoder.eval()

									return text_encoder





						@property
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									torch.manual_seed(0  )

									a_		:		Optional[Any]				    =				{
									    """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,
									}

									a_		:		int				    =				UNetaDConditionModel(**_lowercase  )
									return model





						@property
						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									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 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									torch.manual_seed(0  )
									a_		:		Union[str, Any]				    =				VQModel(**self.dummy_movq_kwargs  )
									return model





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		Any				    =				self.dummy_text_encoder
									a_		:		List[str]				    =				self.dummy_tokenizer
									a_		:		Tuple				    =				self.dummy_unet
									a_		:		Tuple				    =				self.dummy_movq

									a_		:		Tuple				    =				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=_lowercase       ,     set_alpha_to_one=_lowercase       ,     steps_offset=1       ,     prediction_type="""epsilon"""       ,     thresholding=_lowercase       ,     )

									a_		:		Tuple				    =				{
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}
									return components





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=0  )      ->				Optional[int]:
									a_		:		int				    =				floats_tensor((1, self.cross_attention_dim)       ,     rng=random.Random(_lowercase  )  ).to(_lowercase  )
									a_		:		Optional[int]				    =				floats_tensor((1, self.cross_attention_dim)       ,     rng=random.Random(seed + 1  )  ).to(_lowercase  )
									if str(_lowercase  ).startswith("""mps"""  ):
												a_		:		Dict				    =				torch.manual_seed(_lowercase  )
									else:
												a_		:		Tuple				    =				torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
									a_		:		Dict				    =				{
									    """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 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		List[str]				    =				"""cpu"""

									a_		:		Dict				    =				self.get_dummy_components()

									a_		:		int				    =				self.pipeline_class(**_lowercase  )
									a_		:		List[Any]				    =				pipe.to(_lowercase  )

									pipe.set_progress_bar_config(disable=_lowercase  )

									a_		:		Any				    =				pipe(**self.get_dummy_inputs(_lowercase  )  )
									a_		:		List[str]				    =				output.images

									a_		:		Tuple				    =				pipe(
									    **self.get_dummy_inputs(_lowercase  )       ,     return_dict=_lowercase       ,     )[0]

									a_		:		Union[str, Any]				    =				image[0, -3:, -3:, -1]
									a_		:		str				    =				image_from_tuple[0, -3:, -3:, -1]

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

									a_		:		int				    =				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    A__(unittest.TestCase    ):
						"""simple docstring"""






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





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		Optional[Any]				    =				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""  )

									a_		:		int				    =				KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""       ,     torch_dtype=torch.floataa  )
									pipe_prior.to(_lowercase  )

									a_		:		Optional[int]				    =				KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""       ,     torch_dtype=torch.floataa  )
									a_		:		Optional[int]				    =				pipeline.to(_lowercase  )
									pipeline.set_progress_bar_config(disable=_lowercase  )

									a_		:		str				    =				"""red cat, 4k photo"""

									a_		:		Tuple				    =				torch.Generator(device="""cuda"""  ).manual_seed(0  )
									a_    ,			a_		:		Optional[Any]				    =				pipe_prior(
									    _lowercase       ,     generator=_lowercase       ,     num_inference_steps=5       ,     negative_prompt=""""""       ,     ).to_tuple()

									a_		:		List[Any]				    =				torch.Generator(device="""cuda"""  ).manual_seed(0  )
									a_		:		str				    =				pipeline(
									    _lowercase       ,     image_embeds=_lowercase       ,     negative_image_embeds=_lowercase       ,     generator=_lowercase       ,     num_inference_steps=100       ,     output_type="""np"""       ,     )

									a_		:		Dict				    =				output.images[0]

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

									assert_mean_pixel_difference(_lowercase       ,     _lowercase  )





from sklearn.metrics import mean_absolute_error

import datasets


__snake_case							:     List[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}
}
"""

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


__snake_case							:     str 						=	"""
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    A__(datasets.Metric    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									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  )      ->				Union[str, Any]:
									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       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


