






"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  CycleDiffusionPipeline
      __UpperCamelCase	 =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          "negative_prompt",
          "height",
          "width",
          "negative_prompt_embeds",
      }
      __UpperCamelCase	 =  PipelineTesterMixin.required_optional_params - {"latents"}
      __UpperCamelCase	 =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
      __UpperCamelCase	 =  IMAGE_TO_IMAGE_IMAGE_PARAMS
      __UpperCamelCase	 =  IMAGE_TO_IMAGE_IMAGE_PARAMS
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		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						,	)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		DDIMScheduler(
              beta_start=0.0_00_85						,	beta_end=0.0_12						,	beta_schedule='''scaled_linear'''						,	num_train_timesteps=1000						,	clip_sample=lowercase_						,	set_alpha_to_one=lowercase_						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		AutoencoderKL(
              block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,	latent_channels=4						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		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=1000						,	)
          SCREAMING_SNAKE_CASE_      :  int    						=		CLIPTextModel(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

          SCREAMING_SNAKE_CASE_      :  Dict    						=		{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]=0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		floats_tensor((1, 3, 32, 32)						,	rng=random.Random(lowercase_)).to(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		image / 2 + 0.5
          if str(lowercase_).startswith('''mps'''):
              SCREAMING_SNAKE_CASE_      :  int    						=		torch.manual_seed(lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.Generator(device=lowercase_).manual_seed(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		{
              '''prompt''': '''An astronaut riding an elephant''',
              '''source_prompt''': '''An astronaut riding a horse''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''eta''': 0.1,
              '''strength''': 0.8,
              '''guidance_scale''': 3,
              '''source_guidance_scale''': 1,
              '''output_type''': '''numpy''',
          }
          return inputs
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.get_dummy_components()
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		CycleDiffusionPipeline(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)

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

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		images[0, -3:, -3:, -1]

          assert images.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      @unittest.skipIf(torch_device != '''cuda'''						,	'''This test requires a GPU''')
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.get_dummy_components()
          for name, module in components.items():
              if hasattr(lowercase_						,	'''half'''):
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		module.half()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		CycleDiffusionPipeline(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		self.get_dummy_inputs(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		pipe(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		output.images

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		images[0, -3:, -3:, -1]

          assert images.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_      :  int    						=		np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          return super().test_save_load_local()
      @unittest.skip('''non-deterministic pipeline''')
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          return super().test_inference_batch_single_identical()
      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          return super().test_save_load_optional_components()



      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          return super().test_attention_slicing_forward_pass()




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/cycle-diffusion/black_colored_car.png''')
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''')
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		init_image.resize((512, 512))

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''CompVis/stable-diffusion-v1-4'''
          SCREAMING_SNAKE_CASE_      :  int    						=		DDIMScheduler.from_pretrained(lowercase_						,	subfolder='''scheduler''')
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		CycleDiffusionPipeline.from_pretrained(
              lowercase_						,	scheduler=lowercase_						,	safety_checker=lowercase_						,	torch_dtype=torch.floataa						,	revision='''fp16''')

          pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)
          pipe.enable_attention_slicing()

          SCREAMING_SNAKE_CASE_      :  Any    						=		'''A black colored car'''
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''A blue colored car'''

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		pipe(
              prompt=lowercase_						,	source_prompt=lowercase_						,	image=lowercase_						,	num_inference_steps=100						,	eta=0.1						,	strength=0.85						,	guidance_scale=3						,	source_guidance_scale=1						,	generator=lowercase_						,	output_type='''np'''						,	)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		output.images

          # the values aren't exactly equal, but the images look the same visually
          assert np.abs(image - expected_image).max() < 5e-1



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/cycle-diffusion/black_colored_car.png''')
          SCREAMING_SNAKE_CASE_      :  Any    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''')
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		init_image.resize((512, 512))

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''CompVis/stable-diffusion-v1-4'''
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		DDIMScheduler.from_pretrained(lowercase_						,	subfolder='''scheduler''')
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		CycleDiffusionPipeline.from_pretrained(lowercase_						,	scheduler=lowercase_						,	safety_checker=lowercase_)

          pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)
          pipe.enable_attention_slicing()

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''A black colored car'''
          SCREAMING_SNAKE_CASE_      :  Dict    						=		'''A blue colored car'''

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		pipe(
              prompt=lowercase_						,	source_prompt=lowercase_						,	image=lowercase_						,	num_inference_steps=100						,	eta=0.1						,	strength=0.85						,	guidance_scale=3						,	source_guidance_scale=1						,	generator=lowercase_						,	output_type='''np'''						,	)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		output.images

          assert np.abs(image - expected_image).max() < 2e-2







'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






