"""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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



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





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



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





        '''simple docstring'''





        return 1_00



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





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	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    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



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





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            '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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



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





        '''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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	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    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





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

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            '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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

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

        a__:     str										=	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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





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







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

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

        a__:     Optional[int]										=	'a hat'

        a__:     int										=	KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowercase)

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	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'    ,			)

        a__:     str										=	output.images[0]

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

        assert_mean_pixel_difference(lowercase    ,			lowercase)



"""simple docstring"""
class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	data
        a__:     List[Any]										=	previous
        a__:     Any										=	next_node



      def __str__(						self)	->			str:





        '''simple docstring'''





        return f'{self.data}'



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.data



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return self.next



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        return self.previous







class    __snake_case       :



      def __init__(						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	head



      def __iter__(						self)	->			List[Any]:





        '''simple docstring'''





        return self



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        if not self.current:
          raise StopIteration
        else:
          a__:     Dict										=	self.current.get_data()
          a__:     Optional[Any]										=	self.current.get_next()
          return value







class    __snake_case       :



      def __init__(						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	None  # First node in list
        a__:     Optional[int]										=	None  # Last node in list



      def __str__(						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Dict										=	self.head
        a__:     Optional[Any]										=	[]
        while current is not None:
          nodes.append(current.get_data())
          a__:     str										=	current.get_next()
        return " ".join(str(lowercase) for node in nodes)



      def __contains__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.head
        while current:
          if current.get_data() == value:
            return True
          a__:     Dict										=	current.get_next()
        return False



      def __iter__(						self)	->			int:





        '''simple docstring'''





        return LinkedListIterator(self.head)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        if self.head:
          return self.head.get_data()
        return None



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        if self.tail:
          return self.tail.get_data()
        return None



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          a__:     Optional[Any]										=	node
          a__:     Optional[Any]										=	node
        else:
          self.insert_before_node(self.head    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          self.set_head(lowercase)
        else:
          self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Tuple										=	Node(lowercase)
        if self.head is None:
          self.set_head(lowercase)
        else:
          self.set_tail(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Union[str, Any]										=	node
        a__:     Optional[Any]										=	node.previous

        if node.get_previous() is None:
          a__:     Tuple										=	node_to_insert
        else:
          a__:     int										=	node_to_insert

        a__:     Optional[int]										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Optional[int]										=	node
        a__:     Tuple										=	node.next

        if node.get_next() is None:
          a__:     Optional[int]										=	node_to_insert
        else:
          a__:     Any										=	node_to_insert

        a__:     str										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Any										=	1
        a__:     Tuple										=	Node(lowercase)
        a__:     Tuple										=	self.head
        while node:
          if current_position == position:
            self.insert_before_node(lowercase    ,			lowercase)
            return
          current_position += 1
          a__:     List[Any]										=	node.next
        self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Node:





        '''simple docstring'''





        a__:     Tuple										=	self.head
        while node:
          if node.get_data() == item:
            return node
          a__:     List[str]										=	node.get_next()
        raise Exception('Node not found')



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        if (node := self.get_node(lowercase)) is not None:
          if node == self.head:
            a__:     Any										=	self.head.get_next()

          if node == self.tail:
            a__:     List[Any]										=	self.tail.get_previous()

          self.remove_node_pointers(lowercase)



      @staticmethod
      def   lowerCamelCase_   (						lowercase)	->			None:





        '''simple docstring'''





        if node.get_next():
          a__:     Any										=	node.previous

        if node.get_previous():
          a__:     List[str]										=	node.next

        a__:     int										=	None
        a__:     Union[str, Any]										=	None



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return self.head is None







def 						__a   (	)	->None:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()


