'''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 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						Tuple	       =  KandinskyInpaintPipeline
    a__	:						Optional[int]	       =  ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    a__	:						Dict	       =  [
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    a__	:						Optional[Any]	       =  [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    a__	:						Tuple	       =  False



    @property
    def  _lowercase						(self   :   Dict			):
         return 32



    @property
    def  _lowercase						(self   :   Union[str, Any]			):
         return 32



    @property
    def  _lowercase						(self   :   List[Any]			):
         return self.time_input_dim



    @property
    def  _lowercase						(self   :   Tuple			):
         return self.time_input_dim * 4



    @property
    def  _lowercase						(self   :   str			):
         return 100



    @property
    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
         return tokenizer



    @property
    def  _lowercase						(self   :   List[Any]			):
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       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       ,      )

         UpperCAmelCase_	   =       MultilingualCLIP(__a			)
         UpperCAmelCase_	   =       text_encoder.eval()

         return text_encoder



    @property
    def  _lowercase						(self   :   Tuple			):
         torch.manual_seed(0			)

         UpperCAmelCase_	   =       {
             "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,
         }

         UpperCAmelCase_	   =       UNetaDConditionModel(**__a			)
         return model



    @property
    def  _lowercase						(self   :   str			):
         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  _lowercase						(self   :   int			):
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       VQModel(**self.dummy_movq_kwargs			)
         return model



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.dummy_text_encoder
         UpperCAmelCase_	   =       self.dummy_tokenizer
         UpperCAmelCase_	   =       self.dummy_unet
         UpperCAmelCase_	   =       self.dummy_movq

         UpperCAmelCase_	   =       DDIMScheduler(
             num_train_timesteps=1000       ,      beta_schedule="linear"       ,      beta_start=0.0_00_85       ,      beta_end=0.0_12       ,      clip_sample=__a       ,      set_alpha_to_one=__a       ,      steps_offset=1       ,      prediction_type="epsilon"       ,      thresholding=__a       ,      )

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

         return components



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Dict       ,      __a   :   Any=0			):
         UpperCAmelCase_	   =       floats_tensor((1, self.cross_attention_dim)       ,      rng=random.Random(__a			)			).to(__a			)
         UpperCAmelCase_	   =       floats_tensor((1, self.cross_attention_dim)       ,      rng=random.Random(seed + 1			)			).to(__a			)
         # create init_image
         UpperCAmelCase_	   =       floats_tensor((1, 3, 64, 64)       ,      rng=random.Random(__a			)			).to(__a			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			)[0]
         UpperCAmelCase_	   =       Image.fromarray(np.uinta(__a			)			).convert("RGB"			).resize((256, 256)			)
         # create mask
         UpperCAmelCase_	   =       np.ones((64, 64)       ,      dtype=np.floataa			)
         UpperCAmelCase_	   =       0

         if str(__a			).startswith("mps"			):
              UpperCAmelCase_	   =       torch.manual_seed(__a			)
         else:
              UpperCAmelCase_	   =       torch.Generator(device=__a			).manual_seed(__a			)
         UpperCAmelCase_	   =       {
             "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  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       "cpu"

         UpperCAmelCase_	   =       self.get_dummy_components()

         UpperCAmelCase_	   =       self.pipeline_class(**__a			)
         UpperCAmelCase_	   =       pipe.to(__a			)

         pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       pipe(**self.get_dummy_inputs(__a			)			)
         UpperCAmelCase_	   =       output.images

         UpperCAmelCase_	   =       pipe(
             **self.get_dummy_inputs(__a			)       ,      return_dict=__a       ,      )[0]

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

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

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

         UpperCAmelCase_	   =       np.array(
             [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]			)

         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  _lowercase						(self   :   Optional[int]			):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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



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






    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"			)

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

         UpperCAmelCase_	   =       "a hat"

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

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

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

         UpperCAmelCase_	   =       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"       ,      )

         UpperCAmelCase_	   =       output.images[0]

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

         assert_mean_pixel_difference(__a       ,      __a			)





'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


if __name__ == "__main__":
    print(f"{solution() = }")




