
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 			lowercase_		(      A  ,      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				)						->    Tuple:
              return 32


       @property
       def 	_snake_case				(    self				)						->    Optional[Any]:
              return 32


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


       @property
       def 	_snake_case				(    self				)						->    str:
              return self.time_input_dim * 4


       @property
       def 	_snake_case				(    self				)						->    Optional[int]:
              return 100


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


       @property
       def 	_snake_case				(    self				)						->    int:
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Any		=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=1_005						,       )

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=MultilingualCLIP(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=text_encoder.eval()

              return text_encoder


       @property
       def 	_snake_case				(    self				)						->    str:
              torch.manual_seed(0				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''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_							:				int		=UNetaDConditionModel(**__A				)
              return model


       @property
       def 	_snake_case				(    self				)						->    List[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 	_snake_case				(    self				)						->    Optional[int]:
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=VQModel(**self.dummy_movq_kwargs				)
              return model


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_text_encoder
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_tokenizer
              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_unet
              SCREAMING_SNAKE_CASE_							:				Any		=self.dummy_movq

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=DDIMScheduler(
                  num_train_timesteps=1_000						,       beta_schedule='''linear'''						,       beta_start=0.00_085						,       beta_end=0.012						,       clip_sample=__A						,       set_alpha_to_one=__A						,       steps_offset=1						,       prediction_type='''epsilon'''						,       thresholding=__A						,       )

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }

              return components


       def 	_snake_case				(    self						,       __A						,       __A=0				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Tuple		=floats_tensor((1, self.cross_attention_dim)						,       rng=random.Random(__A				)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=floats_tensor((1, self.cross_attention_dim)						,       rng=random.Random(seed + 1				)				).to(__A				)
              # create init_image
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=floats_tensor((1, 3, 64, 64)						,       rng=random.Random(__A				)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=image.cpu().permute(0						,       2						,       3						,       1				)[0]
              SCREAMING_SNAKE_CASE_							:				Any		=Image.fromarray(np.uinta(__A				)				).convert('''RGB'''				).resize((256, 256)				)
              # create mask
              SCREAMING_SNAKE_CASE_							:				List[Any]		=np.ones((64, 64)						,       dtype=np.floataa				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

              if str(__A				).startswith('''mps'''				):
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.manual_seed(__A				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Any		=torch.Generator(device=__A				).manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''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				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				int		='''cpu'''

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_dummy_components()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.pipeline_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=pipe.to(__A				)

              pipe.set_progress_bar_config(disable=__A				)

              SCREAMING_SNAKE_CASE_							:				int		=pipe(**self.get_dummy_inputs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=output.images

              SCREAMING_SNAKE_CASE_							:				str		=pipe(
                  **self.get_dummy_inputs(__A				)						,       return_dict=__A						,       )[0]

              SCREAMING_SNAKE_CASE_							:				Tuple		=image[0, -3:, -3:, -1]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_from_tuple[0, -3:, -3:, -1]

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

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

              SCREAMING_SNAKE_CASE_							:				List[Any]		=np.array(
                  [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]				)

              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				)						->    List[str]:
              super().test_inference_batch_single_identical(expected_max_diff=3e-3				)






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


       def 	_snake_case				(    self				)						->    List[str]:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Any		=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

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

              SCREAMING_SNAKE_CASE_							:				int		='''a hat'''

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

              SCREAMING_SNAKE_CASE_							:				Dict		=KandinskyInpaintPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-1-inpaint'''						,       torch_dtype=torch.floataa				)
              SCREAMING_SNAKE_CASE_							:				int		=pipeline.to(__A				)
              pipeline.set_progress_bar_config(disable=__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.Generator(device='''cpu'''				).manual_seed(0				)
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=pipe_prior(
                  __A						,       generator=__A						,       num_inference_steps=5						,       negative_prompt=''''''						,       ).to_tuple()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=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_							:				List[str]		=output.images[0]

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

              assert_mean_pixel_difference(__A						,       __A				)


from math import ceil, sqrt
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 1_0_0_0_0_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0

       for outer_width in range(3			,							(limit // 4) + 2				):
              if outer_width**2 > limit:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=max(ceil(sqrt(outer_width**2 - limit				)				)			,							1				)
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=1
              if (outer_width - hole_width_lower_bound) % 2:
                     hole_width_lower_bound += 1

              answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

       return answer


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