

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 							A_ (		lowerCAmelCase_							,	unittest.TestCase					):
     _lowercase			:     Optional[Any]								=	KandinskyInpaintPipeline
     _lowercase			:     str								=	['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
     _lowercase			:     str								=	[
         'prompt',
         'negative_prompt',
         'image_embeds',
         'negative_image_embeds',
         'image',
         'mask_image',
     ]
     _lowercase			:     List[Any]								=	[
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'negative_prompt',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     _lowercase			:     str								=	False


     @property
     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Optional[Any]:
      return 3_2


     @property
     def   UpperCAmelCase  (							self			:		Dict					)							->	Tuple:
      return 3_2


     @property
     def   UpperCAmelCase  (							self			:		List[str]					)							->	int:
      return self.time_input_dim


     @property
     def   UpperCAmelCase  (							self			:		List[str]					)							->	Optional[int]:
      return self.time_input_dim * 4


     @property
     def   UpperCAmelCase  (							self			:		str					)							->	List[Any]:
      return 1_0_0


     @property
     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	Tuple:
      __lowerCAmelCase:   List[Any]   	=      XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
      return tokenizer


     @property
     def   UpperCAmelCase  (							self			:		List[Any]					)							->	str:
      torch.manual_seed(0					)
      __lowerCAmelCase:   Tuple   	=      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:   Dict   	=      MultilingualCLIP(_snake_case					)
      __lowerCAmelCase:   Any   	=      text_encoder.eval()

      return text_encoder


     @property
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Union[str, Any]:
      torch.manual_seed(0					)

      __lowerCAmelCase:   Union[str, 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,
      }

      __lowerCAmelCase:   Optional[int]   	=      UNetaDConditionModel(**_snake_case					)
      return model


     @property
     def   UpperCAmelCase  (							self			:		Any					)							->	Tuple:
      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   UpperCAmelCase  (							self			:		str					)							->	Dict:
      torch.manual_seed(0					)
      __lowerCAmelCase:   List[str]   	=      VQModel(**self.dummy_movq_kwargs					)
      return model


     def   UpperCAmelCase  (							self			:		str					)							->	List[Any]:
      __lowerCAmelCase:   str   	=      self.dummy_text_encoder
      __lowerCAmelCase:   Optional[Any]   	=      self.dummy_tokenizer
      __lowerCAmelCase:   List[Any]   	=      self.dummy_unet
      __lowerCAmelCase:   Optional[int]   	=      self.dummy_movq

      __lowerCAmelCase:   Optional[Any]   	=      DDIMScheduler(
          num_train_timesteps=1_0_0_0			,  beta_schedule='linear'			,  beta_start=0.00085			,  beta_end=0.012			,  clip_sample=_snake_case			,  set_alpha_to_one=_snake_case			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=_snake_case			,  )

      __lowerCAmelCase:   Union[str, Any]   	=      {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components


     def   UpperCAmelCase  (							self			:		str			,  UpperCAmelCase			:		Any			,  UpperCAmelCase			:		Tuple=0					)							->	str:
      __lowerCAmelCase:   List[str]   	=      floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(_snake_case					)					).to(_snake_case					)
      __lowerCAmelCase:   List[str]   	=      floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1					)					).to(_snake_case					)
      # create init_image
      __lowerCAmelCase:   Any   	=      floats_tensor((1, 3, 6_4, 6_4)			,  rng=random.Random(_snake_case					)					).to(_snake_case					)
      __lowerCAmelCase:   int   	=      image.cpu().permute(0			,  2			,  3			,  1					)[0]
      __lowerCAmelCase:   Dict   	=      Image.fromarray(np.uinta(_snake_case					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)
      # create mask
      __lowerCAmelCase:   Optional[Any]   	=      np.ones((6_4, 6_4)			,  dtype=np.floataa					)
      __lowerCAmelCase:   Optional[Any]   	=      0

      if str(_snake_case					).startswith('mps'					):
       __lowerCAmelCase:   Any   	=      torch.manual_seed(_snake_case					)
      else:
       __lowerCAmelCase:   int   	=      torch.Generator(device=_snake_case					).manual_seed(_snake_case					)
      __lowerCAmelCase:   int   	=      {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 6_4,
          'width': 6_4,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs


     def   UpperCAmelCase  (							self			:		List[str]					)							->	Any:
      __lowerCAmelCase:   Any   	=      'cpu'

      __lowerCAmelCase:   str   	=      self.get_dummy_components()

      __lowerCAmelCase:   Any   	=      self.pipeline_class(**_snake_case					)
      __lowerCAmelCase:   Any   	=      pipe.to(_snake_case					)

      pipe.set_progress_bar_config(disable=_snake_case					)

      __lowerCAmelCase:   Optional[int]   	=      pipe(**self.get_dummy_inputs(_snake_case					)					)
      __lowerCAmelCase:   int   	=      output.images

      __lowerCAmelCase:   str   	=      pipe(
          **self.get_dummy_inputs(_snake_case					)			,  return_dict=_snake_case			,  )[0]

      __lowerCAmelCase:   Optional[Any]   	=      image[0, -3:, -3:, -1]
      __lowerCAmelCase:   Union[str, Any]   	=      image_from_tuple[0, -3:, -3:, -1]

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

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

      __lowerCAmelCase:   Any   	=      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   UpperCAmelCase  (							self			:		Dict					)							->	Optional[int]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3					)







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


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


     def   UpperCAmelCase  (							self			:		Any					)							->	Tuple:
      __lowerCAmelCase:   Optional[Any]   	=      load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

      __lowerCAmelCase:   Optional[Any]   	=      load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
      __lowerCAmelCase:   Dict   	=      np.ones((7_6_8, 7_6_8)			,  dtype=np.floataa					)
      __lowerCAmelCase:   Union[str, Any]   	=      0

      __lowerCAmelCase:   Optional[Any]   	=      'a hat'

      __lowerCAmelCase:   Optional[int]   	=      KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa					)
      pipe_prior.to(_snake_case					)

      __lowerCAmelCase:   List[str]   	=      KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa					)
      __lowerCAmelCase:   Any   	=      pipeline.to(_snake_case					)
      pipeline.set_progress_bar_config(disable=_snake_case					)

      __lowerCAmelCase:   Dict   	=      torch.Generator(device='cpu'					).manual_seed(0					)
      __lowerCAmelCase:   List[Any]   	=      pipe_prior(
          _snake_case			,  generator=_snake_case			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __lowerCAmelCase:   Dict   	=      pipeline(
          _snake_case			,  image=_snake_case			,  mask_image=_snake_case			,  image_embeds=_snake_case			,  negative_image_embeds=_snake_case			,  generator=_snake_case			,  num_inference_steps=1_0_0			,  height=7_6_8			,  width=7_6_8			,  output_type='np'			,  )

      __lowerCAmelCase:   List[Any]   	=      output.images[0]

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

      assert_mean_pixel_difference(_snake_case			,  _snake_case					)










from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)






