



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 		_snake_case   (      lowerCamelCase       ,lowerCamelCase       ,unittest.TestCase					):





 """simple docstring"""



 lowerCamelCase_        =      CycleDiffusionPipeline
 lowerCamelCase_        =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     '''negative_prompt''',
     '''height''',
     '''width''',
     '''negative_prompt_embeds''',
 }
 lowerCamelCase_        =      PipelineTesterMixin.required_optional_params - {'''latents'''}
 lowerCamelCase_        =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}					)
 lowerCamelCase_        =      IMAGE_TO_IMAGE_IMAGE_PARAMS
 lowerCamelCase_        =      IMAGE_TO_IMAGE_IMAGE_PARAMS
 def lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   torch.manual_seed(0       )
   _A							     =     UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)						,     layers_per_block=2						,     sample_size=3_2						,     in_channels=4						,     out_channels=4						,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,     cross_attention_dim=3_2						,     )
   _A							     =     DDIMScheduler(
       beta_start=0.00_085						,     beta_end=0.012						,     beta_schedule='''scaled_linear'''						,     num_train_timesteps=1_0_0_0						,     clip_sample=_UpperCamelCase						,     set_alpha_to_one=_UpperCamelCase						,     )
   torch.manual_seed(0       )
   _A							     =     AutoencoderKL(
       block_out_channels=[3_2, 6_4]						,     in_channels=3						,     out_channels=3						,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,     latent_channels=4						,     )
   torch.manual_seed(0       )
   _A							     =     CLIPTextConfig(
       bos_token_id=0						,     eos_token_id=2						,     hidden_size=3_2						,     intermediate_size=3_7						,     layer_norm_eps=1e-05						,     num_attention_heads=4						,     num_hidden_layers=5						,     pad_token_id=1						,     vocab_size=1_0_0_0						,     )
   _A							     =     CLIPTextModel(_UpperCamelCase       )
   _A							     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

   _A							     =     {
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''vae''': vae,
       '''text_encoder''': text_encoder,
       '''tokenizer''': tokenizer,
       '''safety_checker''': None,
       '''feature_extractor''': None,
   }
   return components
 def lowercase_		(    self						,     a						,     a=0       )						->				List[Any]:




   """simple docstring"""







   _A							     =     floats_tensor((1, 3, 3_2, 3_2)						,     rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
   _A							     =     image / 2 + 0.5
   if str(_UpperCamelCase       ).startswith('''mps'''       ):
     _A							     =     torch.manual_seed(_UpperCamelCase       )
   else:
     _A							     =     torch.Generator(device=_UpperCamelCase       ).manual_seed(_UpperCamelCase       )
   _A							     =     {
       '''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 lowercase_		(    self       )						->				Any:




   """simple docstring"""







   _A							     =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator

   _A							     =     self.get_dummy_components()
   _A							     =     CycleDiffusionPipeline(**_UpperCamelCase       )
   _A							     =     pipe.to(_UpperCamelCase       )
   pipe.set_progress_bar_config(disable=_UpperCamelCase       )

   _A							     =     self.get_dummy_inputs(_UpperCamelCase       )
   _A							     =     pipe(**_UpperCamelCase       )
   _A							     =     output.images

   _A							     =     images[0, -3:, -3:, -1]

   assert images.shape == (1, 3_2, 3_2, 3)
   _A							     =     np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]       )

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




   """simple docstring"""







   _A							     =     self.get_dummy_components()
   for name, module in components.items():
     if hasattr(_UpperCamelCase						,     '''half'''       ):
       _A							     =     module.half()
   _A							     =     CycleDiffusionPipeline(**_UpperCamelCase       )
   _A							     =     pipe.to(_UpperCamelCase       )
   pipe.set_progress_bar_config(disable=_UpperCamelCase       )

   _A							     =     self.get_dummy_inputs(_UpperCamelCase       )
   _A							     =     pipe(**_UpperCamelCase       )
   _A							     =     output.images

   _A							     =     images[0, -3:, -3:, -1]

   assert images.shape == (1, 3_2, 3_2, 3)
   _A							     =     np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]       )

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




   """simple docstring"""







   return super().test_save_load_local()
 @unittest.skip('''non-deterministic pipeline'''       )
 def lowercase_		(    self       )						->				List[Any]:




   """simple docstring"""







   return super().test_inference_batch_single_identical()
 @skip_mps
 def lowercase_		(    self       )						->				int:




   """simple docstring"""







   return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def lowercase_		(    self       )						->				Tuple:




   """simple docstring"""







   return super().test_save_load_optional_components()



 @skip_mps
 def lowercase_		(    self       )						->				Optional[Any]:




   """simple docstring"""







   return super().test_attention_slicing_forward_pass()







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





 """simple docstring"""



 def lowercase_		(    self       )						->				Any:




   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def lowercase_		(    self       )						->				int:




   """simple docstring"""







   _A							     =     load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/cycle-diffusion/black_colored_car.png'''       )
   _A							     =     load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''       )
   _A							     =     init_image.resize((5_1_2, 5_1_2)       )

   _A							     =     '''CompVis/stable-diffusion-v1-4'''
   _A							     =     DDIMScheduler.from_pretrained(_UpperCamelCase						,     subfolder='''scheduler'''       )
   _A							     =     CycleDiffusionPipeline.from_pretrained(
       _UpperCamelCase						,     scheduler=_UpperCamelCase						,     safety_checker=_UpperCamelCase						,     torch_dtype=torch.floataa						,     revision='''fp16'''       )

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

   _A							     =     '''A black colored car'''
   _A							     =     '''A blue colored car'''

   _A							     =     torch.manual_seed(0       )
   _A							     =     pipe(
       prompt=_UpperCamelCase						,     source_prompt=_UpperCamelCase						,     image=_UpperCamelCase						,     num_inference_steps=1_0_0						,     eta=0.1						,     strength=0.85						,     guidance_scale=3						,     source_guidance_scale=1						,     generator=_UpperCamelCase						,     output_type='''np'''						,     )
   _A							     =     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 lowercase_		(    self       )						->				Union[str, Any]:




   """simple docstring"""







   _A							     =     load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/cycle-diffusion/black_colored_car.png'''       )
   _A							     =     load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''       )
   _A							     =     init_image.resize((5_1_2, 5_1_2)       )

   _A							     =     '''CompVis/stable-diffusion-v1-4'''
   _A							     =     DDIMScheduler.from_pretrained(_UpperCamelCase						,     subfolder='''scheduler'''       )
   _A							     =     CycleDiffusionPipeline.from_pretrained(_UpperCamelCase						,     scheduler=_UpperCamelCase						,     safety_checker=_UpperCamelCase       )

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

   _A							     =     '''A black colored car'''
   _A							     =     '''A blue colored car'''

   _A							     =     torch.manual_seed(0       )
   _A							     =     pipe(
       prompt=_UpperCamelCase						,     source_prompt=_UpperCamelCase						,     image=_UpperCamelCase						,     num_inference_steps=1_0_0						,     eta=0.1						,     strength=0.85						,     guidance_scale=3						,     source_guidance_scale=1						,     generator=_UpperCamelCase						,     output_type='''np'''						,     )
   _A							     =     output.images

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



'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	list:







     """simple docstring"""
     _A				=							int(__UpperCamelCase		)
     if n_element < 1:
          _A				=							ValueError('a should be a positive number'		)
          raise my_error

     _A				=							[1]
     _A		,				_A		,				_A				=							(0, 0, 0)
     _A				=							1
     while index < n_element:
          while hamming_list[i] * 2 <= hamming_list[-1]:
               i += 1
          while hamming_list[j] * 3 <= hamming_list[-1]:
               j += 1
          while hamming_list[k] * 5 <= hamming_list[-1]:
               k += 1
          hamming_list.append(
              min(hamming_list[i] * 2							,    hamming_list[j] * 3							,    hamming_list[k] * 5		)		)
          index += 1
     return hamming_list


if __name__ == "__main__":
    lowerCAmelCase						=					input("""Enter the last number (nth term) of the Hamming Number Series: """)
    print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
    lowerCAmelCase						=					hamming(int(n))
    print("""-----------------------------------------------------""")
    print(F'The list with nth numbers is: {hamming_numbers}')
    print("""-----------------------------------------------------""")





