
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 					_SCREAMING_SNAKE_CASE  (      lowercase__       ,					unittest.TestCase ):
      _A						:		List[Any]    	=     KandinskyVaaControlnetImgaImgPipeline
      _A						:		Optional[Any]    	=     ['image_embeds', 'negative_image_embeds', 'image', 'hint']
      _A						:		Optional[int]    	=     ['image_embeds', 'negative_image_embeds', 'image', 'hint']
      _A						:		Optional[int]    	=     [
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _A						:		Dict    	=     False
      @property
      def A_			(			self ):
          return 32
      @property
      def A_			(			self ):
          return 32
      @property
      def A_			(			self ):
          return self.time_input_dim
      @property
      def A_			(			self ):
          return self.time_input_dim * 4
      @property
      def A_			(			self ):
          return 1_00
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				{
              '''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,
          }

          snake_case__						=				UNetaDConditionModel(**UpperCAmelCase__ )
          return model
      @property
      def A_			(			self ):
          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 ):
          torch.manual_seed(0 )
          snake_case__						=				VQModel(**self.dummy_movq_kwargs )
          return model
      def A_			(			self ):
          snake_case__						=				self.dummy_unet
          snake_case__						=				self.dummy_movq

          snake_case__						=				{
              '''num_train_timesteps''': 10_00,
              '''beta_schedule''': '''linear''',
              '''beta_start''': 0.0_0_0_8_5,
              '''beta_end''': 0.0_1_2,
              '''clip_sample''': False,
              '''set_alpha_to_one''': False,
              '''steps_offset''': 0,
              '''prediction_type''': '''epsilon''',
              '''thresholding''': False,
          }

          snake_case__						=				DDIMScheduler(**UpperCAmelCase__ )

          snake_case__						=				{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0 ):
          snake_case__						=				floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
          snake_case__						=				floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(seed + 1 ) ).to(
              UpperCAmelCase__ )
          # create init_image
          snake_case__						=				floats_tensor((1, 3, 64, 64)		,	rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
          snake_case__						=				image.cpu().permute(0		,	2		,	3		,	1 )[0]
          snake_case__						=				Image.fromarray(np.uinta(UpperCAmelCase__ ) ).convert("RGB" ).resize((2_56, 2_56) )
          # create hint
          snake_case__						=				floats_tensor((1, 3, 64, 64)		,	rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )

          if str(UpperCAmelCase__ ).startswith("mps" ):
              snake_case__						=				torch.manual_seed(UpperCAmelCase__ )
          else:
              snake_case__						=				torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
          snake_case__						=				{
              '''image''': init_image,
              '''image_embeds''': image_embeds,
              '''negative_image_embeds''': negative_image_embeds,
              '''hint''': hint,
              '''generator''': generator,
              '''height''': 64,
              '''width''': 64,
              '''num_inference_steps''': 10,
              '''guidance_scale''': 7.0,
              '''strength''': 0.2,
              '''output_type''': '''np''',
          }
          return inputs



      def A_			(			self ):
          snake_case__						=				'''cpu'''

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**UpperCAmelCase__ )
          snake_case__						=				pipe.to(UpperCAmelCase__ )

          pipe.set_progress_bar_config(disable=UpperCAmelCase__ )

          snake_case__						=				pipe(**self.get_dummy_inputs(UpperCAmelCase__ ) )
          snake_case__						=				output.images

          snake_case__						=				pipe(
              **self.get_dummy_inputs(UpperCAmelCase__ )		,	return_dict=UpperCAmelCase__		,	)[0]

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

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

          snake_case__						=				np.array(
              [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6] )
          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 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def A_			(			self ):
          snake_case__						=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy" )

          snake_case__						=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
          snake_case__						=				init_image.resize((5_12, 5_12) )

          snake_case__						=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/hint_image_cat.png" )
          snake_case__						=				torch.from_numpy(np.array(UpperCAmelCase__ ) ).float() / 2_55.0
          snake_case__						=				hint.permute(2		,	0		,	1 ).unsqueeze(0 )

          snake_case__						=				'''A robot, 4k photo'''

          snake_case__						=				KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-prior"		,	torch_dtype=torch.floataa )
          pipe_prior.to(UpperCAmelCase__ )

          snake_case__						=				KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-controlnet-depth"		,	torch_dtype=torch.floataa )
          snake_case__						=				pipeline.to(UpperCAmelCase__ )

          pipeline.set_progress_bar_config(disable=UpperCAmelCase__ )

          snake_case__						=				torch.Generator(device="cpu" ).manual_seed(0 )

          snake_case__						=				pipe_prior(
              UpperCAmelCase__		,	image=UpperCAmelCase__		,	strength=0.8_5		,	generator=UpperCAmelCase__		,	negative_prompt=""		,	).to_tuple()

          snake_case__						=				pipeline(
              image=UpperCAmelCase__		,	image_embeds=UpperCAmelCase__		,	negative_image_embeds=UpperCAmelCase__		,	hint=UpperCAmelCase__		,	generator=UpperCAmelCase__		,	num_inference_steps=1_00		,	height=5_12		,	width=5_12		,	strength=0.5		,	output_type="np"		,	)

          snake_case__						=				output.images[0]

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

          assert_mean_pixel_difference(UpperCAmelCase__		,	UpperCAmelCase__ )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if len(__lowerCAmelCase							) == 0:
        return False
    snake_case__						=				len(__lowerCAmelCase							) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]    ,		__lowerCAmelCase							)
    else:
        return binary_search(a_list[midpoint + 1 :]    ,		__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  input('''Enter numbers separated by comma:\n''').strip()
     __magic_name__            =  [int(item.strip()) for item in user_input.split(''',''')]
     __magic_name__            =  int(input('''Enter the number to be found in the list:\n''').strip())
     __magic_name__            =  '''''' if binary_search(sequence, target) else '''not '''
     print(F'''{target} was {not_str}found in {sequence}''')



