






"""simple docstring"""





from __future__ import annotations

UpperCamelCase_						:					str      				=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]


class   _lowercase			:


      def __init__(     self					:						Tuple				, a					:						int    ):





            """simple docstring"""







            __snake_case   :     int	      =-1
            __snake_case   :     int	      =-1
            __snake_case   :     list[int]	      =[-1] * length
            __snake_case   :     Node | None	      =None
            __snake_case   :     Node | None	      =None







      def __repr__(     self					:						List[str]    ):





            """simple docstring"""







            return f'''Node(min_value={self.minn} max_value={self.maxx})'''



def      __lowercase       (     a			:					Any			)       ->       Node | None:
      __snake_case   :     int	      =Node(len(a			)			)
      __snake_case   :     Optional[int]	      =min(a			), max(a			)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      __snake_case   :     Optional[Any]	      =(root.minn + root.maxx) // 2

      __snake_case   :     list[int]	      =[]
      __snake_case   :     list[int]	      =[]

      for index, num in enumerate(a			):
            if num <= pivot:
                  left_arr.append(a			)
            else:
                  right_arr.append(a			)
            __snake_case   :     Optional[Any]	      =len(a			)
      __snake_case   :     Any	      =build_tree(a			)
      __snake_case   :     Tuple	      =build_tree(a			)
      return root



def      __lowercase       (     a			:					Optional[Any]							,		a			:					List[Any]							,		a			:					Any			)       ->       int:





      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      __snake_case   :     Optional[Any]	      =(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left							,		a							,		node.map_left[index] - 1			)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right							,		a							,		index - node.map_left[index]			)



def      __lowercase       (     a			:					Any							,		a			:					Tuple							,		a			:					Any							,		a			:					Any			)       ->       int:





      if start > end:
            return 0
      __snake_case   :     Dict	      =rank_till_index(a							,		a							,		a			)
      __snake_case   :     Dict	      =rank_till_index(a							,		a							,		start - 1			)
      return rank_till_end - rank_before_start



def      __lowercase       (     a			:					Tuple							,		a			:					List[str]							,		a			:					int							,		a			:					List[str]			)       ->       int:





      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      __snake_case   :     Any	      =node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left							,		a							,		(node.map_left[start - 1] if start else 0)							,		node.map_left[end] - 1							,		)
      else:
            return quantile(
                node.right							,		index - num_elements_in_left_tree							,		start - (node.map_left[start - 1] if start else 0)							,		end - node.map_left[end]							,		)



def      __lowercase       (     a			:					List[str]							,		a			:					Optional[int]							,		a			:					Any							,		a			:					Any							,		a			:					Optional[Any]			)       ->       int:





      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      __snake_case   :     Optional[Any]	      =range_counting(
          node.left							,		(node.map_left[start - 1] if start else 0)							,		node.map_left[end] - 1							,		a							,		a							,		)
      __snake_case   :     List[str]	      =range_counting(
          node.right							,		start - (node.map_left[start - 1] if start else 0)							,		end - node.map_left[end]							,		a							,		a							,		)
      return left + right


if __name__ == "__main__":
    import doctest

    doctest.testmod()








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				int      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       [
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   @property
   def 			_a    (						self :							str  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Tuple  )->      Any:
     return 32






   @property
   def 			_a    (						self :							Union[str, Any]  )->      List[str]:
     return self.time_input_dim






   @property
   def 			_a    (						self :							Optional[Any]  )->      Tuple:
     return self.time_input_dim * 4






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 1_00






   @property
   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			List[Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							List[str]  )->      Optional[int]:
     torch.manual_seed(0  )
     _lowercase:			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=10_05    ,       )

     _lowercase:			Optional[Any]      =     MultilingualCLIP(UpperCamelCase_  )
     _lowercase:			int      =     text_encoder.eval()

     return text_encoder






   @property
   def 			_a    (						self :							Optional[int]  )->      Optional[Any]:
     torch.manual_seed(0  )

     _lowercase:			Optional[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,
     }

     _lowercase:			str      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      Optional[Any]:
     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 			_a    (						self :							List[str]  )->      int:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     VQModel(**self.dummy_movq_kwargs  )
     return model






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

     _lowercase:			Optional[Any]      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=UpperCamelCase_    ,       )

     _lowercase:			List[Any]      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]=0  )->      Union[str, Any]:
     _lowercase:			str      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(UpperCamelCase_  )  ).to(UpperCamelCase_  )
     _lowercase:			Optional[int]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(UpperCamelCase_  )
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Any      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[str]      =     {
         'prompt': 'horse',
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs






   def 			_a    (						self :							List[str]  )->      List[str]:
     _lowercase:			str      =     'cpu'

     _lowercase:			Optional[Any]      =     self.get_dummy_components()

     _lowercase:			int      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Union[str, Any]      =     output.images

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

     _lowercase:			int      =     image[0, -3:, -3:, -1]
     _lowercase:			List[str]      =     image_from_tuple[0, -3:, -3:, -1]

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

     _lowercase:			Union[str, Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     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 			_a    (						self :							Optional[int]  )->      Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			List[Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(UpperCamelCase_  )

     _lowercase:			str      =     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[str]      =     pipeline.to(UpperCamelCase_  )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Dict      =     'red cat, 4k photo'

     _lowercase:			List[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			Dict      =     pipe_prior(
         UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			str      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         UpperCamelCase_    ,       image_embeds=UpperCamelCase_    ,       negative_image_embeds=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Optional[int]      =     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )
