






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 	_UpperCAmelCase  (  _A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    IFPipeline
      A          =    TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
      A          =    TEXT_TO_IMAGE_BATCH_PARAMS
      A          =    PipelineTesterMixin.required_optional_params - {'''latents'''}
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._get_dummy_components()
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0		):



          '''simple docstring'''
          if str(_lowerCAmelCase		).startswith("mps"		):
              lowerCAmelCase__   :List[str]      							=				torch.manual_seed(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :str      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)

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

          return inputs
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda"    ,	reason="float16 requires CUDA"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_save_load_local()
      def 			snake_case_      (   self		):



          '''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 			snake_case_      (   self		):



          '''simple docstring'''
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)







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




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # if

          lowerCAmelCase__   :Optional[Any]      							=				IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"    ,	variant="fp16"    ,	torch_dtype=torch.floataa		)

          lowerCAmelCase__   :Union[str, Any]      							=				IFSuperResolutionPipeline.from_pretrained(
              "DeepFloyd/IF-II-L-v1.0"    ,	variant="fp16"    ,	torch_dtype=torch.floataa    ,	text_encoder=_lowerCAmelCase    ,	tokenizer=_lowerCAmelCase		)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("cuda"		)

          lowerCAmelCase__							,lowerCAmelCase__   :Union[str, Any]      							=				pipe_a.encode_prompt("anime turtle"    ,	device="cuda"		)

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

          lowerCAmelCase__   :Union[str, Any]      							=				None
          lowerCAmelCase__   :Union[str, Any]      							=				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(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          lowerCAmelCase__   :List[Any]      							=				IFImgaImgPipeline(**pipe_a.components		)
          lowerCAmelCase__   :Union[str, Any]      							=				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(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          lowerCAmelCase__   :List[Any]      							=				IFInpaintingPipeline(**pipe_a.components		)
          lowerCAmelCase__   :Union[str, Any]      							=				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(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # pipeline 1

          _start_torch_memory_measurement()

          lowerCAmelCase__   :Tuple      							=				torch.Generator(device="cpu"		).manual_seed(0		)
          lowerCAmelCase__   :List[Any]      							=				pipe_a(
              prompt_embeds=_lowerCAmelCase    ,	negative_prompt_embeds=_lowerCAmelCase    ,	num_inference_steps=2    ,	generator=_lowerCAmelCase    ,	output_type="np"    ,	)

          lowerCAmelCase__   :List[str]      							=				output.images[0]

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

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

          lowerCAmelCase__   :List[Any]      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"		)
          assert_mean_pixel_difference(_lowerCAmelCase    ,	_lowerCAmelCase		)

          # pipeline 2

          _start_torch_memory_measurement()

          lowerCAmelCase__   :Any      							=				torch.Generator(device="cpu"		).manual_seed(0		)

          lowerCAmelCase__   :Optional[int]      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[Any]      							=				pipe_a(
              prompt_embeds=_lowerCAmelCase    ,	negative_prompt_embeds=_lowerCAmelCase    ,	image=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=2    ,	output_type="np"    ,	)

          lowerCAmelCase__   :List[Any]      							=				output.images[0]

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

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

          lowerCAmelCase__   :Optional[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(_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # pipeline 1

          _start_torch_memory_measurement()

          lowerCAmelCase__   :str      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)

          lowerCAmelCase__   :Dict      							=				torch.Generator(device="cpu"		).manual_seed(0		)

          lowerCAmelCase__   :Optional[int]      							=				pipe_a(
              prompt_embeds=_lowerCAmelCase    ,	negative_prompt_embeds=_lowerCAmelCase    ,	image=_lowerCAmelCase    ,	num_inference_steps=2    ,	generator=_lowerCAmelCase    ,	output_type="np"    ,	)

          lowerCAmelCase__   :List[Any]      							=				output.images[0]

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

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

          lowerCAmelCase__   :List[str]      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"		)
          assert_mean_pixel_difference(_lowerCAmelCase    ,	_lowerCAmelCase		)

          # pipeline 2

          _start_torch_memory_measurement()

          lowerCAmelCase__   :str      							=				torch.Generator(device="cpu"		).manual_seed(0		)

          lowerCAmelCase__   :str      							=				floats_tensor((1, 3, 256, 256)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				pipe_a(
              prompt_embeds=_lowerCAmelCase    ,	negative_prompt_embeds=_lowerCAmelCase    ,	image=_lowerCAmelCase    ,	original_image=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=2    ,	output_type="np"    ,	)

          lowerCAmelCase__   :Union[str, Any]      							=				output.images[0]

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

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

          lowerCAmelCase__   :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(_lowerCAmelCase    ,	_lowerCAmelCase		)



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # pipeline 1

          _start_torch_memory_measurement()

          lowerCAmelCase__   :str      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(1		)		).to(_lowerCAmelCase		)

          lowerCAmelCase__   :Dict      							=				torch.Generator(device="cpu"		).manual_seed(0		)
          lowerCAmelCase__   :Optional[int]      							=				pipe_a(
              prompt_embeds=_lowerCAmelCase    ,	negative_prompt_embeds=_lowerCAmelCase    ,	image=_lowerCAmelCase    ,	mask_image=_lowerCAmelCase    ,	num_inference_steps=2    ,	generator=_lowerCAmelCase    ,	output_type="np"    ,	)

          lowerCAmelCase__   :Tuple      							=				output.images[0]

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

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

          lowerCAmelCase__   :Union[str, Any]      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"		)
          assert_mean_pixel_difference(_lowerCAmelCase    ,	_lowerCAmelCase		)

          # pipeline 2

          _start_torch_memory_measurement()

          lowerCAmelCase__   :Tuple      							=				torch.Generator(device="cpu"		).manual_seed(0		)

          lowerCAmelCase__   :Any      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				floats_tensor((1, 3, 256, 256)    ,	rng=random.Random(0		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :int      							=				floats_tensor((1, 3, 256, 256)    ,	rng=random.Random(1		)		).to(_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				pipe_a(
              prompt_embeds=_lowerCAmelCase    ,	negative_prompt_embeds=_lowerCAmelCase    ,	image=_lowerCAmelCase    ,	mask_image=_lowerCAmelCase    ,	original_image=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=2    ,	output_type="np"    ,	)

          lowerCAmelCase__   :str      							=				output.images[0]

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

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

          lowerCAmelCase__   :Union[str, Any]      							=				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(_lowerCAmelCase    ,	_lowerCAmelCase		)





def 				snake_case__							(							):
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()











def 				snake_case__							(							UpperCAmelCase							:  Tuple       ):
    lowerCAmelCase__   :List[Any]      							=				len(UpperCAmelCase       )
    for i in range(length - 1       ):
        lowerCAmelCase__   :Union[str, Any]      							=				i
        for k in range(i + 1    ,			UpperCAmelCase       ):
            if collection[k] < collection[least]:
                lowerCAmelCase__   :Any      							=				k
        if least != i:
            lowerCAmelCase__							,lowerCAmelCase__   :int      							=				(collection[i], collection[least])
    return collection


if __name__ == "__main__":
 _a    :   Optional[int]        =			input("""Enter numbers separated by a comma:\n""").strip()
 _a    :   Any        =			[int(item) for item in user_input.split(""",""")]
 print(selection_sort(unsorted))



