




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 			a__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    Dict					=    KandinskyImgaImgPipeline
						_a							:    List[Any]					=    ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
						_a							:    str					=    [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						]
						_a							:    List[Any]					=    [
						    """generator""",
						    """height""",
						    """width""",
						    """strength""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_a							:    int					=    False






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 3_2






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 3_2






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.time_input_dim






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.time_input_dim * 4






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 1_0_0






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
											return tokenizer






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)
											__lowerCAmelCase				   =       MCLIPConfig(
											    numDims=self.cross_attention_dim		,			transformerDimensions=self.text_embedder_hidden_size		,			hidden_size=self.text_embedder_hidden_size		,			intermediate_size=3_7		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=1_0_0_5		,			)

											__lowerCAmelCase				   =       MultilingualCLIP(_A			)
											__lowerCAmelCase				   =       text_encoder.eval()

											return text_encoder






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)

											__lowerCAmelCase				   =       {
											    "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,
											}

											__lowerCAmelCase				   =       UNetaDConditionModel(**_A			)
											return model






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return {
											    "block_out_channels": [3_2, 6_4],
											    "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": 1_2,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)
											__lowerCAmelCase				   =       VQModel(**self.dummy_movq_kwargs			)
											return model






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.dummy_text_encoder
											__lowerCAmelCase				   =       self.dummy_tokenizer
											__lowerCAmelCase				   =       self.dummy_unet
											__lowerCAmelCase				   =       self.dummy_movq

											__lowerCAmelCase				   =       {
											    "num_train_timesteps": 1_0_0_0,
											    "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,
											}

											__lowerCAmelCase				   =       DDIMScheduler(**_A			)

											__lowerCAmelCase				   =       {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "movq": movq,
											}

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1			)			).to(_A			)
											# create init_image
											__lowerCAmelCase				   =       floats_tensor((1, 3, 6_4, 6_4)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       image.cpu().permute(0		,			2		,			3		,			1			)[0]
											__lowerCAmelCase				   =       Image.fromarray(np.uinta(_A			)			).convert("RGB"			).resize((2_5_6, 2_5_6)			)

											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "horse",
											    "image": init_image,
											    "image_embeds": image_embeds,
											    "negative_image_embeds": negative_image_embeds,
											    "generator": generator,
											    "height": 6_4,
											    "width": 6_4,
											    "num_inference_steps": 1_0,
											    "guidance_scale": 7.0,
											    "strength": 0.2,
											    "output_type": "np",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "cpu"

											__lowerCAmelCase				   =       self.get_dummy_components()

											__lowerCAmelCase				   =       self.pipeline_class(**_A			)
											__lowerCAmelCase				   =       pipe.to(_A			)

											pipe.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       pipe(**self.get_dummy_inputs(_A			)			)
											__lowerCAmelCase				   =       output.images

											__lowerCAmelCase				   =       pipe(
											    **self.get_dummy_inputs(_A			)		,			return_dict=_A		,			)[0]

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

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowerCAmelCase				   =       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 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/kandinsky/kandinsky_img2img_frog.npy"			)

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

											__lowerCAmelCase				   =       KandinskyPriorPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1-prior"		,			torch_dtype=torch.floataa			)
											pipe_prior.to(_A			)

											__lowerCAmelCase				   =       KandinskyImgaImgPipeline.from_pretrained(
											    "kandinsky-community/kandinsky-2-1"		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       pipeline.to(_A			)

											pipeline.set_progress_bar_config(disable=_A			)

											__lowerCAmelCase				   =       torch.Generator(device="cpu"			).manual_seed(0			)
											__lowerCAmelCase   ,							__lowerCAmelCase				   =       pipe_prior(
											    _A		,			generator=_A		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

											__lowerCAmelCase				   =       pipeline(
											    _A		,			image=_A		,			image_embeds=_A		,			negative_image_embeds=_A		,			generator=_A		,			num_inference_steps=1_0_0		,			height=7_6_8		,			width=7_6_8		,			strength=0.2		,			output_type="np"		,			)

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)








"""simple docstring"""





def    _snake_case  (  _snake_case :	int	):

      if not isinstance(_snake_case       ,      _snake_case	):
            raise TypeError('''only integers accepted as input'''	)
      else:
            lowerCAmelCase    :		List[str]				=      str(abs(_snake_case	)	)
            lowerCAmelCase    :		Optional[Any]				=      [list(_snake_case	) for char in range(len(_snake_case	)	)]
            for index in range(len(_snake_case	)	):
                  num_transpositions[index].pop(_snake_case	)
            return max(
                int(''''''.join(list(_snake_case	)	)	) for transposition in num_transpositions	)


if __name__ == "__main__":
 __import__('''doctest''').testmod()


