
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		str    	=     StableDiffusionLDMaDPipeline
      _A						:		List[str]    	=     TEXT_TO_IMAGE_PARAMS
      _A						:		Optional[int]    	=     TEXT_TO_IMAGE_BATCH_PARAMS
      _A						:		Union[str, Any]    	=     TEXT_TO_IMAGE_IMAGE_PARAMS
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				UNetaDConditionModel(
              block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=4		,	out_channels=4		,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,	cross_attention_dim=32		,	)
          snake_case__						=				DDIMScheduler(
              beta_start=0.0_0_0_8_5		,	beta_end=0.0_1_2		,	beta_schedule="scaled_linear"		,	clip_sample=lowerCamelCase		,	set_alpha_to_one=lowerCamelCase		,	)
          torch.manual_seed(0 )
          snake_case__						=				AutoencoderKL(
              block_out_channels=[32, 64]		,	in_channels=6		,	out_channels=6		,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,	latent_channels=4		,	)
          torch.manual_seed(0 )
          snake_case__						=				CLIPTextConfig(
              bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1e-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=10_00		,	)
          snake_case__						=				CLIPTextModel(lowerCamelCase )
          snake_case__						=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

          snake_case__						=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0 ):
          if str(lowerCamelCase ).startswith("mps" ):
              snake_case__						=				torch.manual_seed(lowerCamelCase )
          else:
              snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
          snake_case__						=				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }
          return inputs
      def A_			(			self ):
          snake_case__						=				"cpu"  # ensure determinism for the device-dependent torch.Generator

          snake_case__						=				self.get_dummy_components()
          snake_case__						=				StableDiffusionLDMaDPipeline(**lowerCamelCase )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth

          snake_case__						=				rgb[0, -3:, -3:, -1]
          snake_case__						=				depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          snake_case__						=				np.array(
              [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
          snake_case__						=				np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36] )

          assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
          assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
      def A_			(			self ):
          snake_case__						=				self.get_dummy_components()
          snake_case__						=				StableDiffusionLDMaDPipeline(**lowerCamelCase )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				3 * [inputs["prompt"]]

          # forward
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb_slice_a[0, -3:, -3:, -1]
          snake_case__						=				depth_slice_a[0, -3:, -1]

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				3 * [inputs.pop("prompt" )]

          snake_case__						=				ldmad_pipe.tokenizer(
              lowerCamelCase		,	padding="max_length"		,	max_length=ldmad_pipe.tokenizer.model_max_length		,	truncation=lowerCamelCase		,	return_tensors="pt"		,	)
          snake_case__						=				text_inputs["input_ids"].to(lowerCamelCase )

          snake_case__						=				ldmad_pipe.text_encoder(lowerCamelCase )[0]

          snake_case__						=				prompt_embeds

          # forward
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb_slice_a[0, -3:, -3:, -1]
          snake_case__						=				depth_slice_a[0, -3:, -1]

          assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
          assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4



      def A_			(			self ):
          snake_case__						=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case__						=				self.get_dummy_components()
          snake_case__						=				PNDMScheduler(skip_prk_steps=lowerCamelCase )
          snake_case__						=				StableDiffusionLDMaDPipeline(**lowerCamelCase )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				"french fries"
          snake_case__						=				ldmad_pipe(**lowerCamelCase		,	negative_prompt=lowerCamelCase )

          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb[0, -3:, -3:, -1]
          snake_case__						=				depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          snake_case__						=				np.array(
              [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
          snake_case__						=				np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35] )
          assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
          assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2





@slow
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase="cpu"		,	lowerCamelCase=torch.floataa		,	lowerCamelCase=0 ):
          snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
          snake_case__						=				np.random.RandomState(lowerCamelCase ).standard_normal((1, 4, 64, 64) )
          snake_case__						=				torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase		,	dtype=lowerCamelCase )
          snake_case__						=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs



      def A_			(			self ):
          snake_case__						=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb[0, -3:, -3:, -1].flatten()
          snake_case__						=				rgb[0, -3:, -1].flatten()

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12)

          snake_case__						=				np.array(
              [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
          snake_case__						=				np.array(
              [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
          assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
          assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3





@nightly
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase="cpu"		,	lowerCamelCase=torch.floataa		,	lowerCamelCase=0 ):
          snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
          snake_case__						=				np.random.RandomState(lowerCamelCase ).standard_normal((1, 4, 64, 64) )
          snake_case__						=				torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase		,	dtype=lowerCamelCase )
          snake_case__						=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 50,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs
      def A_			(			self ):
          snake_case__						=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth

          snake_case__						=				0.4_9_5_5_8_6
          snake_case__						=				0.3_3_7_9_5_5_1_5
          snake_case__						=				1_12.4_85_18
          snake_case__						=				98.48_97_46
          assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
          assert np.abs(expected_depth_std - depth.std() ) < 1e-3



      def A_			(			self ):
          snake_case__						=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth

          snake_case__						=				0.4_1_9_4_1_2_7
          snake_case__						=				0.3_5_3_7_5_5_8_6
          snake_case__						=				0.5_6_3_8_5_0_2
          snake_case__						=				0.3_4_6_8_6_1_0_3

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12, 1)
          assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
          assert np.abs(expected_depth_std - depth.std() ) < 1e-3





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}''')



