



"""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    __lowercase     (					_UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							List[str]							       =		KandinskyInpaintPipeline
						_A		:							List[str]							       =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						_A		:							List[Any]							       =		[
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						_A		:							Union[str, Any]							       =		[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_A		:							Tuple							       =		False







						@property
						def 						__UpperCamelCase							(self       ):
											return 32







						@property
						def 						__UpperCamelCase							(self       ):
											return 32







						@property
						def 						__UpperCamelCase							(self       ):
											return self.time_input_dim







						@property
						def 						__UpperCamelCase							(self       ):
											return self.time_input_dim * 4







						@property
						def 						__UpperCamelCase							(self       ):
											return 1_00







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""       )
											return tokenizer







						@property
						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )
											snake_case_					:		Union[str, 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=10_05   ,		)

											snake_case_					:		List[Any] 					=					MultilingualCLIP(lowercase__       )
											snake_case_					:		Tuple 					=					text_encoder.eval()

											return text_encoder







						@property
						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )

											snake_case_					:		Dict 					=					{
											    """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,
											}

											snake_case_					:		Union[str, Any] 					=					UNetaDConditionModel(**lowercase__       )
											return model







						@property
						def 						__UpperCamelCase							(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 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )
											snake_case_					:		int 					=					VQModel(**self.dummy_movq_kwargs       )
											return model







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.dummy_text_encoder
											snake_case_					:		Tuple 					=					self.dummy_tokenizer
											snake_case_					:		Optional[Any] 					=					self.dummy_unet
											snake_case_					:		int 					=					self.dummy_movq

											snake_case_					:		Optional[int] 					=					DDIMScheduler(
											    num_train_timesteps=10_00   ,		beta_schedule="""linear"""   ,		beta_start=0.00085   ,		beta_end=0.012   ,		clip_sample=lowercase__   ,		set_alpha_to_one=lowercase__   ,		steps_offset=1   ,		prediction_type="""epsilon"""   ,		thresholding=lowercase__   ,		)

											snake_case_					:		Any 					=					{
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}

											return components







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__=0       ):
											snake_case_					:		Any 					=					floats_tensor((1, self.cross_attention_dim)   ,		rng=random.Random(lowercase__       )       ).to(lowercase__       )
											snake_case_					:		Optional[int] 					=					floats_tensor((1, self.cross_attention_dim)   ,		rng=random.Random(seed + 1       )       ).to(lowercase__       )
											# create init_image
											snake_case_					:		Tuple 					=					floats_tensor((1, 3, 64, 64)   ,		rng=random.Random(lowercase__       )       ).to(lowercase__       )
											snake_case_					:		int 					=					image.cpu().permute(0   ,		2   ,		3   ,		1       )[0]
											snake_case_					:		Dict 					=					Image.fromarray(np.uinta(lowercase__       )       ).convert("""RGB"""       ).resize((2_56, 2_56)       )
											# create mask
											snake_case_					:		Any 					=					np.ones((64, 64)   ,		dtype=np.floataa       )
											snake_case_					:		List[Any] 					=					0

											if str(lowercase__       ).startswith("""mps"""       ):
																snake_case_					:		Any 					=					torch.manual_seed(lowercase__       )
											else:
																snake_case_					:		Dict 					=					torch.Generator(device=lowercase__       ).manual_seed(lowercase__       )
											snake_case_					:		Union[str, Any] 					=					{
											    """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 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					"""cpu"""

											snake_case_					:		Dict 					=					self.get_dummy_components()

											snake_case_					:		Any 					=					self.pipeline_class(**lowercase__       )
											snake_case_					:		Any 					=					pipe.to(lowercase__       )

											pipe.set_progress_bar_config(disable=lowercase__       )

											snake_case_					:		Union[str, Any] 					=					pipe(**self.get_dummy_inputs(lowercase__       )       )
											snake_case_					:		List[Any] 					=					output.images

											snake_case_					:		Any 					=					pipe(
											    **self.get_dummy_inputs(lowercase__       )   ,		return_dict=lowercase__   ,		)[0]

											snake_case_					:		List[str] 					=					image[0, -3:, -3:, -1]
											snake_case_					:		List[str] 					=					image_from_tuple[0, -3:, -3:, -1]

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

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

											snake_case_					:		Dict 					=					np.array(
											    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]       )

											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 						__UpperCamelCase							(self       ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""       )

											snake_case_					:		Optional[Any] 					=					load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
											snake_case_					:		int 					=					np.ones((7_68, 7_68)   ,		dtype=np.floataa       )
											snake_case_					:		int 					=					0

											snake_case_					:		str 					=					"""a hat"""

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

											snake_case_					:		Union[str, Any] 					=					KandinskyInpaintPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-1-inpaint"""   ,		torch_dtype=torch.floataa       )
											snake_case_					:		List[str] 					=					pipeline.to(lowercase__       )
											pipeline.set_progress_bar_config(disable=lowercase__       )

											snake_case_					:		List[str] 					=					torch.Generator(device="""cpu"""       ).manual_seed(0       )
											snake_case_					,			snake_case_					:		Union[str, Any] 					=					pipe_prior(
											    lowercase__   ,		generator=lowercase__   ,		num_inference_steps=5   ,		negative_prompt=""""""   ,		).to_tuple()

											snake_case_					:		Optional[int] 					=					pipeline(
											    lowercase__   ,		image=lowercase__   ,		mask_image=lowercase__   ,		image_embeds=lowercase__   ,		negative_image_embeds=lowercase__   ,		generator=lowercase__   ,		num_inference_steps=1_00   ,		height=7_68   ,		width=7_68   ,		output_type="""np"""   ,		)

											snake_case_					:		Union[str, Any] 					=					output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(lowercase__   ,		lowercase__       )









"""simple docstring"""

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""				,    [False, True]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[int] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Tuple 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					tmp_path / """cache"""
					snake_case_					:		int 					=					{"""text""": """string"""}
					snake_case_					:		Any 					=					features.copy() if features else default_expected_features
					snake_case_					:		List[Any] 					=					(
					    Features({feature: Value(SCREAMING_SNAKE_CASE__			) for feature, dtype in features.items()}			) if features is not None else None
					)
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    features=SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[Any] 					=					{"""text""": """string"""}
					snake_case_					:		Optional[int] 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    split=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type"""				,    [str, list]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					if issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		List[str] 					=					text_path
					elif issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		str 					=					[text_path]
					snake_case_					:		List[str] 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]=("train",)			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for split in splits:
										snake_case_					:		Dict 					=					dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""				,    [False, True]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		int 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Optional[Any] 					=					TextDatasetReader({"""train""": text_path}				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		int 					=					features.copy() if features else default_expected_features
					snake_case_					:		Tuple 					=					(
					    Features({feature: Value(SCREAMING_SNAKE_CASE__			) for feature, dtype in features.items()}			) if features is not None else None
					)
					snake_case_					:		str 					=					TextDatasetReader({"""train""": text_path}				,    features=SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					if split:
										snake_case_					:		Union[str, Any] 					=					{split: text_path}
					else:
										snake_case_					:		Union[str, Any] 					=					"""train"""
										snake_case_					:		int 					=					{"""train""": text_path, """test""": text_path}
					snake_case_					:		List[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Tuple 					=					{"""text""": """string"""}
					snake_case_					:		int 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    splits=list(path.keys()			)			)
					assert all(dataset[split].split == split for split in path.keys()			)




