



"""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, KandinskyImgaImgPipeline, 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__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					KandinskyImgaImgPipeline
							_UpperCAmelCase      				=					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
							_UpperCAmelCase      				=					[
							    '''prompt''',
							    '''negative_prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							]
							_UpperCAmelCase      				=					[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''strength''',
							    '''guidance_scale''',
							    '''negative_prompt''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							_UpperCAmelCase      				=					False






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return 32






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






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									return self.time_input_dim






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return self.time_input_dim * 4






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






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
									return tokenizer






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

									_UpperCAmelCase							 =		MultilingualCLIP(snake_case						)
									_UpperCAmelCase							 =		text_encoder.eval()

									return text_encoder






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									torch.manual_seed(0						)

									_UpperCAmelCase							 =		{
									    '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,
									}

									_UpperCAmelCase							 =		UNetaDConditionModel(**snake_case						)
									return model






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									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        lowerCamelCase_   (  self						)			->							Optional[Any]:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		VQModel(**self.dummy_movq_kwargs						)
									return model






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		self.dummy_text_encoder
									_UpperCAmelCase							 =		self.dummy_tokenizer
									_UpperCAmelCase							 =		self.dummy_unet
									_UpperCAmelCase							 =		self.dummy_movq

									_UpperCAmelCase							 =		{
									    'num_train_timesteps': 1000,
									    'beta_schedule': 'linear',
									    'beta_start': 0.00085,
									    'beta_end': 0.012,
									    'clip_sample': False,
									    'set_alpha_to_one': False,
									    'steps_offset': 0,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									}

									_UpperCAmelCase							 =		DDIMScheduler(**snake_case						)

									_UpperCAmelCase							 =		{
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}

									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							List[str]:
									_UpperCAmelCase							 =		floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(snake_case						)						).to(snake_case						)
									_UpperCAmelCase							 =		floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(seed + 1						)						).to(snake_case						)
									# create init_image
									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(snake_case						)						).to(snake_case						)
									_UpperCAmelCase							 =		image.cpu().permute(0	,       2	,       3	,       1						)[0]
									_UpperCAmelCase							 =		Image.fromarray(np.uinta(snake_case						)						).convert('RGB'						).resize((256, 256)						)

									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'prompt': 'horse',
									    'image': init_image,
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'num_inference_steps': 10,
									    'guidance_scale': 7.0,
									    'strength': 0.2,
									    'output_type': 'np',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()

									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)

									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		pipe(**self.get_dummy_inputs(snake_case						)						)
									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		pipe(
									    **self.get_dummy_inputs(snake_case						)	,       return_dict=snake_case	,       )[0]

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

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

									_UpperCAmelCase							 =		np.array(
									    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]						)
									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 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







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






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/kandinsky/kandinsky_img2img_frog.npy'						)

									_UpperCAmelCase							 =		load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
									_UpperCAmelCase							 =		'A red cartoon frog, 4k'

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

									_UpperCAmelCase							 =		KandinskyImgaImgPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-1'	,       torch_dtype=torch.floataa						)
									_UpperCAmelCase							 =		pipeline.to(snake_case						)

									pipeline.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		pipe_prior(
									    snake_case	,       generator=snake_case	,       num_inference_steps=5	,       negative_prompt=''	,       ).to_tuple()

									_UpperCAmelCase							 =		pipeline(
									    snake_case	,       image=snake_case	,       image_embeds=snake_case	,       negative_image_embeds=snake_case	,       generator=snake_case	,       num_inference_steps=100	,       height=768	,       width=768	,       strength=0.2	,       output_type='np'	,       )

									_UpperCAmelCase							 =		output.images[0]

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

									assert_mean_pixel_difference(snake_case	,       snake_case						)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  							=					{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CLIPSegModel''',
					    '''CLIPSegPreTrainedModel''',
					    '''CLIPSegTextModel''',
					    '''CLIPSegVisionModel''',
					    '''CLIPSegForImageSegmentation''',
					]

if TYPE_CHECKING:
					from .configuration_clipseg import (
					    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPSegConfig,
					    CLIPSegTextConfig,
					    CLIPSegVisionConfig,
					)
					from .processing_clipseg import CLIPSegProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clipseg import (
										    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPSegForImageSegmentation,
										    CLIPSegModel,
										    CLIPSegPreTrainedModel,
										    CLIPSegTextModel,
										    CLIPSegVisionModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


