'''simple docstring'''

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class 				_lowercase	(      UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Union[str, Any]      							=   IFPipeline
      UpperCAmelCase_      :      str      							=   TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
      UpperCAmelCase_      :      Tuple      							=   TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCAmelCase_      :      int      							=   PipelineTesterMixin.required_optional_params - {'''latents'''}




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return self._get_dummy_components()




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			Union[str, Any]:



         '''simple docstring'''



         if str(UpperCamelCase__							).startswith('''mps'''							):
            UpperCAmelCase__							:				Tuple   				=  torch.manual_seed(UpperCamelCase__							)
         else:
            UpperCAmelCase__							:				Dict   				=  torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)

         UpperCAmelCase__							:				int   				=  {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != '''cuda'''					,reason='''float16 requires CUDA'''							)
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1							)




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         self._test_save_load_local()




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,)




      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()					,reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,)
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)




@slow
@require_torch_gpu
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''					,variant='''fp16'''					,torch_dtype=torch.floataa							)

         UpperCAmelCase__							:				str   				=  IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0'''					,variant='''fp16'''					,torch_dtype=torch.floataa					,text_encoder=UpperCamelCase__					,tokenizer=UpperCamelCase__							)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda'''							)

         UpperCAmelCase__							:				Dict   				=  pipe_a.encode_prompt('''anime turtle'''					,device='''cuda'''							)

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         UpperCAmelCase__							:				Tuple   				=  None
         UpperCAmelCase__							:				str   				=  None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

         self._test_if(UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__							)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         UpperCAmelCase__							:				Optional[int]   				=  IFImgaImgPipeline(**pipe_a.components							)
         UpperCAmelCase__							:				Optional[int]   				=  IFImgaImgSuperResolutionPipeline(**pipe_a.components							)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

         self._test_if_imgaimg(UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__							)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         UpperCAmelCase__							:				Tuple   				=  IFInpaintingPipeline(**pipe_a.components							)
         UpperCAmelCase__							:				Optional[int]   				=  IFInpaintingSuperResolutionPipeline(**pipe_a.components							)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

         self._test_if_inpainting(UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



         _start_torch_memory_measurement()

         UpperCAmelCase__							:				List[str]   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)
         UpperCAmelCase__							:				Optional[Any]   				=  pipe_a(
             prompt_embeds=UpperCamelCase__					,negative_prompt_embeds=UpperCamelCase__					,num_inference_steps=2					,generator=UpperCamelCase__					,output_type='''np'''					,)

         UpperCAmelCase__							:				Optional[int]   				=  output.images[0]

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

         UpperCAmelCase__							:				Tuple   				=  torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         UpperCAmelCase__							:				Optional[Any]   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''							)
         assert_mean_pixel_difference(UpperCamelCase__					,UpperCamelCase__							)

         # pipeline 2

         _start_torch_memory_measurement()

         UpperCAmelCase__							:				Tuple   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)

         UpperCAmelCase__							:				int   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(0							)							).to(UpperCamelCase__							)

         UpperCAmelCase__							:				Any   				=  pipe_a(
             prompt_embeds=UpperCamelCase__					,negative_prompt_embeds=UpperCamelCase__					,image=UpperCamelCase__					,generator=UpperCamelCase__					,num_inference_steps=2					,output_type='''np'''					,)

         UpperCAmelCase__							:				Tuple   				=  output.images[0]

         assert image.shape == (256, 256, 3)

         UpperCAmelCase__							:				Union[str, Any]   				=  torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         UpperCAmelCase__							:				int   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''							)
         assert_mean_pixel_difference(UpperCamelCase__					,UpperCamelCase__							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         _start_torch_memory_measurement()

         UpperCAmelCase__							:				Tuple   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(0							)							).to(UpperCamelCase__							)

         UpperCAmelCase__							:				Any   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)

         UpperCAmelCase__							:				List[Any]   				=  pipe_a(
             prompt_embeds=UpperCamelCase__					,negative_prompt_embeds=UpperCamelCase__					,image=UpperCamelCase__					,num_inference_steps=2					,generator=UpperCamelCase__					,output_type='''np'''					,)

         UpperCAmelCase__							:				int   				=  output.images[0]

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

         UpperCAmelCase__							:				Optional[int]   				=  torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         UpperCAmelCase__							:				Tuple   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''							)
         assert_mean_pixel_difference(UpperCamelCase__					,UpperCamelCase__							)

         # pipeline 2

         _start_torch_memory_measurement()

         UpperCAmelCase__							:				Union[str, Any]   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)

         UpperCAmelCase__							:				Optional[Any]   				=  floats_tensor((1, 3, 256, 256)					,rng=random.Random(0							)							).to(UpperCamelCase__							)
         UpperCAmelCase__							:				List[Any]   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(0							)							).to(UpperCamelCase__							)

         UpperCAmelCase__							:				str   				=  pipe_a(
             prompt_embeds=UpperCamelCase__					,negative_prompt_embeds=UpperCamelCase__					,image=UpperCamelCase__					,original_image=UpperCamelCase__					,generator=UpperCamelCase__					,num_inference_steps=2					,output_type='''np'''					,)

         UpperCAmelCase__							:				Optional[int]   				=  output.images[0]

         assert image.shape == (256, 256, 3)

         UpperCAmelCase__							:				List[Any]   				=  torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         UpperCAmelCase__							:				Optional[Any]   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''							)
         assert_mean_pixel_difference(UpperCamelCase__					,UpperCamelCase__							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         _start_torch_memory_measurement()

         UpperCAmelCase__							:				str   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(0							)							).to(UpperCamelCase__							)
         UpperCAmelCase__							:				Union[str, Any]   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(1							)							).to(UpperCamelCase__							)

         UpperCAmelCase__							:				Optional[int]   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)
         UpperCAmelCase__							:				int   				=  pipe_a(
             prompt_embeds=UpperCamelCase__					,negative_prompt_embeds=UpperCamelCase__					,image=UpperCamelCase__					,mask_image=UpperCamelCase__					,num_inference_steps=2					,generator=UpperCamelCase__					,output_type='''np'''					,)

         UpperCAmelCase__							:				Any   				=  output.images[0]

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

         UpperCAmelCase__							:				Any   				=  torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         UpperCAmelCase__							:				List[Any]   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''							)
         assert_mean_pixel_difference(UpperCamelCase__					,UpperCamelCase__							)

         # pipeline 2

         _start_torch_memory_measurement()

         UpperCAmelCase__							:				str   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)

         UpperCAmelCase__							:				List[str]   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(0							)							).to(UpperCamelCase__							)
         UpperCAmelCase__							:				int   				=  floats_tensor((1, 3, 256, 256)					,rng=random.Random(0							)							).to(UpperCamelCase__							)
         UpperCAmelCase__							:				Optional[int]   				=  floats_tensor((1, 3, 256, 256)					,rng=random.Random(1							)							).to(UpperCamelCase__							)

         UpperCAmelCase__							:				Dict   				=  pipe_a(
             prompt_embeds=UpperCamelCase__					,negative_prompt_embeds=UpperCamelCase__					,image=UpperCamelCase__					,mask_image=UpperCamelCase__					,original_image=UpperCamelCase__					,generator=UpperCamelCase__					,num_inference_steps=2					,output_type='''np'''					,)

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

         assert image.shape == (256, 256, 3)

         UpperCAmelCase__							:				Any   				=  torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         UpperCAmelCase__							:				Tuple   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''							)
         assert_mean_pixel_difference(UpperCamelCase__					,UpperCamelCase__							)







def __UpperCamelCase(   ):
   '''simple docstring'''
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()





'''simple docstring'''

def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''

   if length <= 0 or not isinstance(_A  ,			_A    ):
      raise ValueError('''Length must be a positive integer.'''    )
   return [n * (2 * n - 1) for n in range(_A    )]


if __name__ == "__main__":
  print(hexagonal_numbers(length=5))
  print(hexagonal_numbers(length=10))




