




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		KandinskyVaaControlnetPipeline
     _a											=		["image_embeds", "negative_image_embeds", "hint"]
     _a											=		["image_embeds", "negative_image_embeds", "hint"]
     _a											=		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _a											=		False

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return 32

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

     @property
     def       a__			(						self       )		->							str:
           return self.time_input_dim

     @property
     def       a__			(						self       )		->							Tuple:
           return self.time_input_dim * 4

     @property
     def       a__			(						self       )		->							Dict:
           return 100

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

           _A							:  Dict    =		{
               """in_channels""": 8,
               # Out channels is double in channels because predicts mean and variance
               """out_channels""": 8,
               """addition_embed_type""": """image_hint""",
               """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""": """image_proj""",
               """cross_attention_dim""": self.cross_attention_dim,
               """attention_head_dim""": 4,
               """resnet_time_scale_shift""": """scale_shift""",
               """class_embed_type""": None,
           }

           _A							:  List[Any]    =		UNetaDConditionModel(**_a       )
           return model

     @property
     def       a__			(						self       )		->							Any:
           return {
               "block_out_channels": [32, 32, 64, 64],
               "down_block_types": [
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
               "vq_embed_dim": 4,
           }

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

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  int    =		self.dummy_unet
           _A							:  Tuple    =		self.dummy_movq

           _A							:  str    =		DDIMScheduler(
               num_train_timesteps=1000	,  beta_schedule="""linear"""	,  beta_start=0.00085	,  beta_end=0.012	,  clip_sample=_a	,  set_alpha_to_one=_a	,  steps_offset=1	,  prediction_type="""epsilon"""	,  thresholding=_a	,  )

           _A							:  Dict    =		{
               """unet""": unet,
               """scheduler""": scheduler,
               """movq""": movq,
           }
           return components

     def       a__			(						self	,  _a	,  _a=0       )		->							int:
           _A							:  Optional[Any]    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Tuple    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1       )       ).to(
               _a       )

           # create hint
           _A							:  Union[str, Any]    =		floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_a       )       ).to(_a       )

           if str(_a       ).startswith("""mps"""       ):
                 _A							:  Optional[Any]    =		torch.manual_seed(_a       )
           else:
                 _A							:  List[Any]    =		torch.Generator(device=_a       ).manual_seed(_a       )
           _A							:  Union[str, Any]    =		{
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """hint""": hint,
               """generator""": generator,
               """height""": 64,
               """width""": 64,
               """guidance_scale""": 4.0,
               """num_inference_steps""": 2,
               """output_type""": """np""",
           }
           return inputs



     def       a__			(						self       )		->							int:
           _A							:  Optional[Any]    =		"""cpu"""

           _A							:  Union[str, Any]    =		self.get_dummy_components()

           _A							:  str    =		self.pipeline_class(**_a       )
           _A							:  Optional[int]    =		pipe.to(_a       )

           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Union[str, Any]    =		pipe(**self.get_dummy_inputs(_a       )       )
           _A							:  List[Any]    =		output.images

           _A							:  Optional[Any]    =		pipe(
               **self.get_dummy_inputs(_a       )	,  return_dict=_a	,  )[0]

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

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

           _A							:  Dict    =		np.array(
               [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]       )

           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 		lowercase							(     unittest.TestCase	):

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



     def       a__			(						self       )		->							Any:
           _A							:  Optional[Any]    =		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

           _A							:  int    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/hint_image_cat.png"""       )
           _A							:  Optional[Any]    =		torch.from_numpy(np.array(_a       )       ).float() / 255.0
           _A							:  Optional[Any]    =		hint.permute(2	,  0	,  1       ).unsqueeze(0       )

           _A							:  Tuple    =		KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""	,  torch_dtype=torch.floataa       )
           pipe_prior.to(_a       )

           _A							:  Optional[Any]    =		KandinskyVaaControlnetPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,  torch_dtype=torch.floataa       )
           _A							:  Optional[int]    =		pipeline.to(_a       )
           pipeline.set_progress_bar_config(disable=_a       )

           _A							:  List[str]    =		"""A robot, 4k photo"""

           _A							:  Union[str, Any]    =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
           _A    ,     _A							:  Union[str, Any]    =		pipe_prior(
               _a	,  generator=_a	,  num_inference_steps=5	,  negative_prompt=""""""	,  ).to_tuple()

           _A							:  Tuple    =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
           _A							:  str    =		pipeline(
               image_embeds=_a	,  negative_image_embeds=_a	,  hint=_a	,  generator=_a	,  num_inference_steps=100	,  output_type="""np"""	,  )

           _A							:  Optional[Any]    =		output.images[0]

           assert image.shape == (512, 512, 3)

           assert_mean_pixel_difference(_a	,  _a       )







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def        lowerCAmelCase_			(  snake_case_  ):

      # A local function to see if a dot lands in the circle.
      def is_in_circle(snake_case_,snake_case_  ) -> bool:
            _A							:  List[str]    =		sqrt((x**2) + (y**2)  )
            # Our circle has a radius of 1, so a distance
            # greater than 1 would land outside the circle.
            return distance_from_centre <= 1

      # The proportion of guesses that landed in the circle
      _A							:  Optional[int]    =		mean(
          int(is_in_circle(uniform(-1.0,1.0  ),uniform(-1.0,1.0  )  )  )
          for _ in range(snake_case_  )  )
      # The ratio of the area for circle to square is pi/4.
      _A							:  List[str]    =		proportion * 4
      print(f'''The estimated value of pi is {pi_estimate}'''  )
      print(f'''The numpy value of pi is {pi}'''  )
      print(f'''The total error is {abs(pi - pi_estimate  )}'''  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_ = 0.0,snake_case_ = 1.0,):
      return mean(
          function_to_integrate(uniform(snake_case_,snake_case_  )  ) for _ in range(snake_case_  )  ) * (max_value - min_value)







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = 1.0  ):

      def identity_function(snake_case_  ) -> float:
            return x

      _A							:  Any    =		area_under_curve_estimator(
          snake_case_,snake_case_,snake_case_,snake_case_  )
      _A							:  Tuple    =		(max_value * max_value - min_value * min_value) / 2

      print("""******************"""  )
      print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {expected_value}'''  )
      print(f'''Total error is {abs(estimated_value - expected_value  )}'''  )
      print("""******************"""  )







def        lowerCAmelCase_			(  snake_case_  ):

      def function_to_integrate(snake_case_  ) -> float:
            return sqrt(4.0 - x * x  )

      _A							:  Optional[int]    =		area_under_curve_estimator(
          snake_case_,snake_case_,0.0,2.0  )

      print("""******************"""  )
      print("""Estimating pi using area_under_curve_estimator"""  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {pi}'''  )
      print(f'''Total error is {abs(estimated_value - pi  )}'''  )
      print("""******************"""  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

