





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   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
       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      _snake_case  (							self    :Dict		)   ->   List[str]:



         """simple docstring"""



         return 32


       @property
       def      _snake_case  (							self    :Optional[int]		)   ->   Union[str, Any]:



         """simple docstring"""



         return 32


       @property
       def      _snake_case  (							self    :List[str]		)   ->   int:



         """simple docstring"""



         return self.time_input_dim


       @property
       def      _snake_case  (							self    :Optional[int]		)   ->   List[str]:



         """simple docstring"""



         return self.time_input_dim * 4


       @property
       def      _snake_case  (							self    :Optional[int]		)   ->   int:



         """simple docstring"""



         return 100


       @property
       def      _snake_case  (							self    :Union[str, Any]		)   ->   List[Any]:



         """simple docstring"""



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


       @property
       def      _snake_case  (							self    :List[str]		)   ->   Tuple:



         """simple docstring"""



         torch.manual_seed(0		)
         SCREAMING_SNAKE_CASE__			=      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							,				)

         SCREAMING_SNAKE_CASE__			=      MultilingualCLIP(__A		)
         SCREAMING_SNAKE_CASE__			=      text_encoder.eval()

         return text_encoder


       @property
       def      _snake_case  (							self    :List[str]		)   ->   Tuple:



         """simple docstring"""



         torch.manual_seed(0		)

         SCREAMING_SNAKE_CASE__			=      {
             """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,
         }

         SCREAMING_SNAKE_CASE__			=      UNetaDConditionModel(**__A		)
         return model


       @property
       def      _snake_case  (							self    :Any		)   ->   Optional[int]:



         """simple docstring"""



         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    :str		)   ->   Optional[Any]:



         """simple docstring"""



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


       def      _snake_case  (							self    :Any		)   ->   Optional[int]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.dummy_text_encoder
         SCREAMING_SNAKE_CASE__			=      self.dummy_tokenizer
         SCREAMING_SNAKE_CASE__			=      self.dummy_unet
         SCREAMING_SNAKE_CASE__			=      self.dummy_movq

         SCREAMING_SNAKE_CASE__			=      DDIMScheduler(
             num_train_timesteps=1000							,				beta_schedule="""linear"""							,				beta_start=0.0_0_0_8_5							,				beta_end=0.0_1_2							,				clip_sample=__A							,				set_alpha_to_one=__A							,				steps_offset=1							,				prediction_type="""epsilon"""							,				thresholding=__A							,				)

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

         return components


       def      _snake_case  (							self    :Dict							,				__A    :Optional[int]							,				__A    :Union[str, Any]=0		)   ->   Tuple:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      floats_tensor((1, self.cross_attention_dim)							,				rng=random.Random(__A		)		).to(__A		)
         SCREAMING_SNAKE_CASE__			=      floats_tensor((1, self.cross_attention_dim)							,				rng=random.Random(seed + 1		)		).to(__A		)
         # create init_image
         SCREAMING_SNAKE_CASE__			=      floats_tensor((1, 3, 64, 64)							,				rng=random.Random(__A		)		).to(__A		)
         SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		)[0]
         SCREAMING_SNAKE_CASE__			=      Image.fromarray(np.uinta(__A		)		).convert("""RGB"""		).resize((256, 256)		)
         # create mask
         SCREAMING_SNAKE_CASE__			=      np.ones((64, 64)							,				dtype=np.floataa		)
         SCREAMING_SNAKE_CASE__			=      0

         if str(__A		).startswith("""mps"""		):
           SCREAMING_SNAKE_CASE__			=      torch.manual_seed(__A		)
         else:
           SCREAMING_SNAKE_CASE__			=      torch.Generator(device=__A		).manual_seed(__A		)
         SCREAMING_SNAKE_CASE__			=      {
             """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      _snake_case  (							self    :int		)   ->   Dict:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      """cpu"""

         SCREAMING_SNAKE_CASE__			=      self.get_dummy_components()

         SCREAMING_SNAKE_CASE__			=      self.pipeline_class(**__A		)
         SCREAMING_SNAKE_CASE__			=      pipe.to(__A		)

         pipe.set_progress_bar_config(disable=__A		)

         SCREAMING_SNAKE_CASE__			=      pipe(**self.get_dummy_inputs(__A		)		)
         SCREAMING_SNAKE_CASE__			=      output.images

         SCREAMING_SNAKE_CASE__			=      pipe(
             **self.get_dummy_inputs(__A		)							,				return_dict=__A							,				)[0]

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

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

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

         SCREAMING_SNAKE_CASE__			=      np.array(
             [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]		)

         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      _snake_case  (							self    :Dict		)   ->   str:



         """simple docstring"""



         super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




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


       def      _snake_case  (							self    :Any		)   ->   Optional[int]:



         """simple docstring"""



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


       def      _snake_case  (							self    :Any		)   ->   Any:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""		)

         SCREAMING_SNAKE_CASE__			=      load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
         SCREAMING_SNAKE_CASE__			=      np.ones((768, 768)							,				dtype=np.floataa		)
         SCREAMING_SNAKE_CASE__			=      0

         SCREAMING_SNAKE_CASE__			=      """a hat"""

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

         SCREAMING_SNAKE_CASE__			=      KandinskyInpaintPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1-inpaint"""							,				torch_dtype=torch.floataa		)
         SCREAMING_SNAKE_CASE__			=      pipeline.to(__A		)
         pipeline.set_progress_bar_config(disable=__A		)

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

         SCREAMING_SNAKE_CASE__			=      pipeline(
             __A							,				image=__A							,				mask_image=__A							,				image_embeds=__A							,				negative_image_embeds=__A							,				generator=__A							,				num_inference_steps=100							,				height=768							,				width=768							,				output_type="""np"""							,				)

         SCREAMING_SNAKE_CASE__			=      output.images[0]

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

         assert_mean_pixel_difference(__A							,				__A		)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"timm_backbone"


							def __init__(							self    :Union[str, Any]							,				__A    :str=None							,				__A    :Union[str, Any]=3							,				__A    :str=True							,				__A    :Any=True							,				__A    :Optional[Any]=None							,				**__A    :List[str]							,				)   ->   Tuple:



									"""simple docstring"""



									super().__init__(**__A		)
									SCREAMING_SNAKE_CASE__			=      backbone
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      features_only
									SCREAMING_SNAKE_CASE__			=      use_pretrained_backbone
									SCREAMING_SNAKE_CASE__			=      True
									SCREAMING_SNAKE_CASE__			=      out_indices if out_indices is not None else (-1,)