



"""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        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			Tuple							=		KandinskyImgaImgPipeline
 _lowerCAmelCase   :			Dict							=		["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
 _lowerCAmelCase   :			Optional[Any]							=		[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 _lowerCAmelCase   :			Union[str, Any]							=		[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCAmelCase   :			Optional[int]							=		False

 @property
 def 						_snake_case					(     self		:       List[str]				):
     return 32

 @property
 def 						_snake_case					(     self		:       Dict				):
     return 32

 @property
 def 						_snake_case					(     self		:       Any				):
     return self.time_input_dim

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

 @property
 def 						_snake_case					(     self		:       Any				):
     return 100

 @property
 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Any				=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
     return tokenizer

 @property
 def 						_snake_case					(     self		:       int				):
     torch.manual_seed(0				)
     snake_case_ :     Optional[int]				=	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  ,     )

     snake_case_ :     List[str]				=	MultilingualCLIP(lowercase_				)
     snake_case_ :     Union[str, Any]				=	text_encoder.eval()

     return text_encoder

 @property
 def 						_snake_case					(     self		:       int				):
     torch.manual_seed(0				)

     snake_case_ :     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,
     }

     snake_case_ :     Tuple				=	UNetaDConditionModel(**lowercase_				)
     return model

 @property
 def 						_snake_case					(     self		:       Dict				):
     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 						_snake_case					(     self		:       Any				):
     torch.manual_seed(0				)
     snake_case_ :     Tuple				=	VQModel(**self.dummy_movq_kwargs				)
     return model

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     str				=	self.dummy_text_encoder
     snake_case_ :     Union[str, Any]				=	self.dummy_tokenizer
     snake_case_ :     str				=	self.dummy_unet
     snake_case_ :     Dict				=	self.dummy_movq

     snake_case_ :     Any				=	{
         '''num_train_timesteps''': 1000,
         '''beta_schedule''': '''linear''',
         '''beta_start''': 0.0_00_85,
         '''beta_end''': 0.0_12,
         '''clip_sample''': False,
         '''set_alpha_to_one''': False,
         '''steps_offset''': 0,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
     }

     snake_case_ :     str				=	DDIMScheduler(**lowercase_				)

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

     return components

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       str  ,     lowercase_		:       Any=0				):
     snake_case_ :     List[str]				=	floats_tensor((1, self.cross_attention_dim)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)
     snake_case_ :     List[str]				=	floats_tensor((1, self.cross_attention_dim)  ,     rng=random.Random(seed + 1				)				).to(lowercase_				)
     # create init_image
     snake_case_ :     str				=	floats_tensor((1, 3, 64, 64)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)
     snake_case_ :     Any				=	image.cpu().permute(0  ,     2  ,     3  ,     1				)[0]
     snake_case_ :     Union[str, Any]				=	Image.fromarray(np.uinta(lowercase_				)				).convert('''RGB'''				).resize((256, 256)				)

     if str(lowercase_				).startswith('''mps'''				):
         snake_case_ :     Tuple				=	torch.manual_seed(lowercase_				)
     else:
         snake_case_ :     List[Any]				=	torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     snake_case_ :     List[Any]				=	{
         '''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 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     str				=	'''cpu'''

     snake_case_ :     Dict				=	self.get_dummy_components()

     snake_case_ :     int				=	self.pipeline_class(**lowercase_				)
     snake_case_ :     List[Any]				=	pipe.to(lowercase_				)

     pipe.set_progress_bar_config(disable=lowercase_				)

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

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

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

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

     snake_case_ :     Optional[Any]				=	np.array(
         [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]				)
     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        _UpperCAmelCase       (			unittest.TestCase):

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






 def 						_snake_case					(     self		:       str				):
     snake_case_ :     str				=	load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinsky/kandinsky_img2img_frog.npy'''				)

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

     snake_case_ :     Union[str, Any]				=	KandinskyPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-1-prior'''  ,     torch_dtype=torch.floataa				)
     pipe_prior.to(lowercase_				)

     snake_case_ :     Union[str, Any]				=	KandinskyImgaImgPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-1'''  ,     torch_dtype=torch.floataa				)
     snake_case_ :     Dict				=	pipeline.to(lowercase_				)

     pipeline.set_progress_bar_config(disable=lowercase_				)

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

     snake_case_ :     Union[str, Any]				=	pipeline(
         lowercase_  ,     image=lowercase_  ,     image_embeds=lowercase_  ,     negative_image_embeds=lowercase_  ,     generator=lowercase_  ,     num_inference_steps=100  ,     height=768  ,     width=768  ,     strength=0.2  ,     output_type='''np'''  ,     )

     snake_case_ :     List[str]				=	output.images[0]

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

     assert_mean_pixel_difference(lowercase_  ,     lowercase_				)










"""simple docstring"""

import numpy as np




def __lowercase			(							_a    ):
    return (2 / (1 + np.exp(-2 * vector    ))) - 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





