




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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 lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			KandinskyInpaintPipeline
			lowerCamelCase__      =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowerCamelCase__      =			[
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowerCamelCase__      =			[
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowerCamelCase__      =			False
			@property
			def 					A_    (  self  ):
							return 32
			@property
			def 					A_    (  self  ):
							return 32
			@property
			def 					A_    (  self  ):
							return self.time_input_dim
			@property
			def 					A_    (  self  ):
							return self.time_input_dim * 4
			@property
			def 					A_    (  self  ):
							return 100
			@property
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
							return tokenizer
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    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=1005  ,      )

							_lowerCamelCase     :			str    =    MultilingualCLIP(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    text_encoder.eval()

							return text_encoder
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )

							_lowerCamelCase     :			List[str]    =    {
							    'in_channels': 9,
							    # 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     :			Union[str, Any]    =    UNetaDConditionModel(**lowercase  )
							return model
			@property
			def 					A_    (  self  ):
							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 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    VQModel(**self.dummy_movq_kwargs  )
							return model
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.dummy_text_encoder
							_lowerCamelCase     :			Optional[Any]    =    self.dummy_tokenizer
							_lowerCamelCase     :			Optional[int]    =    self.dummy_unet
							_lowerCamelCase     :			Tuple    =    self.dummy_movq

							_lowerCamelCase     :			Dict    =    DDIMScheduler(
							    num_train_timesteps=1000  ,      beta_schedule='linear'  ,      beta_start=0.0_00_85  ,      beta_end=0.0_12  ,      clip_sample=lowercase  ,      set_alpha_to_one=lowercase  ,      steps_offset=1  ,      prediction_type='epsilon'  ,      thresholding=lowercase  ,      )

							_lowerCamelCase     :			List[str]    =    {
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							_lowerCamelCase     :			List[Any]    =    floats_tensor((1, self.cross_attention_dim)  ,      rng=random.Random(lowercase  )  ).to(lowercase  )
							_lowerCamelCase     :			List[str]    =    floats_tensor((1, self.cross_attention_dim)  ,      rng=random.Random(seed + 1  )  ).to(lowercase  )
							# create init_image
							_lowerCamelCase     :			int    =    floats_tensor((1, 3, 64, 64)  ,      rng=random.Random(lowercase  )  ).to(lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    image.cpu().permute(0  ,      2  ,      3  ,      1  )[0]
							_lowerCamelCase     :			List[str]    =    Image.fromarray(np.uinta(lowercase  )  ).convert('RGB'  ).resize((256, 256)  )
							# create mask
							_lowerCamelCase     :			Tuple    =    np.ones((64, 64)  ,      dtype=np.floataa  )
							_lowerCamelCase     :			Optional[int]    =    0

							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			List[Any]    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[Any]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			str    =    {
							    'prompt': 'horse',
							    'image': init_image,
							    'mask_image': mask,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'generator': generator,
							    'height': 64,
							    'width': 64,
							    'num_inference_steps': 2,
							    'guidance_scale': 4.0,
							    'output_type': 'np',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()

							_lowerCamelCase     :			List[str]    =    self.pipeline_class(**lowercase  )
							_lowerCamelCase     :			Optional[int]    =    pipe.to(lowercase  )

							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			int    =    pipe(**self.get_dummy_inputs(lowercase  )  )
							_lowerCamelCase     :			List[Any]    =    output.images

							_lowerCamelCase     :			Dict    =    pipe(
							    **self.get_dummy_inputs(lowercase  )  ,      return_dict=lowercase  ,      )[0]

							_lowerCamelCase     :			Optional[int]    =    image[0, -3:, -3:, -1]
							_lowerCamelCase     :			str    =    image_from_tuple[0, -3:, -3:, -1]

							print(F'''image.shape {image.shape}'''  )

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

							_lowerCamelCase     :			List[Any]    =    np.array(
							    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]  )

							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()}'''






			def 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






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






			'''simple docstring'''



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






			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

							_lowerCamelCase     :			Optional[int]    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
							_lowerCamelCase     :			str    =    np.ones((768, 768)  ,      dtype=np.floataa  )
							_lowerCamelCase     :			Any    =    0

							_lowerCamelCase     :			Any    =    'a hat'

							_lowerCamelCase     :			Optional[int]    =    KandinskyPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-prior'  ,      torch_dtype=torch.floataa  )
							pipe_prior.to(lowercase  )

							_lowerCamelCase     :			List[Any]    =    KandinskyInpaintPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-inpaint'  ,      torch_dtype=torch.floataa  )
							_lowerCamelCase     :			Optional[int]    =    pipeline.to(lowercase  )
							pipeline.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			Optional[Any]    =    torch.Generator(device='cpu'  ).manual_seed(0  )
							_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    pipe_prior(
							    lowercase  ,      generator=lowercase  ,      num_inference_steps=5  ,      negative_prompt=''  ,      ).to_tuple()

							_lowerCamelCase     :			int    =    pipeline(
							    lowercase  ,      image=lowercase  ,      mask_image=lowercase  ,      image_embeds=lowercase  ,      negative_image_embeds=lowercase  ,      generator=lowercase  ,      num_inference_steps=100  ,      height=768  ,      width=768  ,      output_type='np'  ,      )

							_lowerCamelCase     :			Dict    =    output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(lowercase  ,      lowercase  )
from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)