



"""simple docstring"""




import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

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


enable_full_determinism()






@skip_mps
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			VideoToVideoSDPipeline
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'}						) - {'image', 'width', 'height'}
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'}						) - {'image'}
      __SCREAMING_SNAKE_CASE						:       str								=			PipelineTesterMixin.required_optional_params - {'latents'}
      __SCREAMING_SNAKE_CASE						:       List[str]								=			False

      # No `output_type`.
      __SCREAMING_SNAKE_CASE						:       Tuple								=			frozenset(
          [
              'num_inference_steps',
              'generator',
              'latents',
              'return_dict',
              'callback',
              'callback_steps',
          ]						)



      def 						snake_case_ (					self					:     Tuple						):
           torch.manual_seed(0						)
           _UpperCAmelCase      :     int        =   UNetaDConditionModel(
               block_out_channels=(3_2, 6_4, 6_4, 6_4)					,     layers_per_block=2					,     sample_size=3_2					,     in_channels=4					,     out_channels=4					,     down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")					,     up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")					,     cross_attention_dim=3_2					,     attention_head_dim=4					,     )
           _UpperCAmelCase      :     str        =   DDIMScheduler(
               beta_start=0.00_085					,     beta_end=0.012					,     beta_schedule="scaled_linear"					,     clip_sample=A					,     set_alpha_to_one=A					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     str        =   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					,     sample_size=1_2_8					,     )
           torch.manual_seed(0						)
           _UpperCAmelCase      :     List[Any]        =   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					,     hidden_act="gelu"					,     projection_dim=5_1_2					,     )
           _UpperCAmelCase      :     Any        =   CLIPTextModel(A						)
           _UpperCAmelCase      :     Optional[Any]        =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

           _UpperCAmelCase      :     List[str]        =   {
               "unet": unet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
           }
           return components



      def 						snake_case_ (					self					:     List[Any]					,     A					:     Any					,     A					:     int=0						):
           # 3 frames
           _UpperCAmelCase      :     List[str]        =   floats_tensor((1, 3, 3, 3_2, 3_2)					,     rng=random.Random(A						)						).to(A						)

           if str(A						).startswith("mps"						):
                _UpperCAmelCase      :     Optional[Any]        =   torch.manual_seed(A						)
           else:
                _UpperCAmelCase      :     Optional[Any]        =   torch.Generator(device=A						).manual_seed(A						)
           _UpperCAmelCase      :     List[str]        =   {
               "prompt": "A painting of a squirrel eating a burger",
               "video": video,
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "pt",
           }
           return inputs



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Union[str, Any]        =   "cpu"  # ensure determinism for the device-dependent torch.Generator
           _UpperCAmelCase      :     Optional[int]        =   self.get_dummy_components()
           _UpperCAmelCase      :     List[str]        =   VideoToVideoSDPipeline(**A						)
           _UpperCAmelCase      :     List[Any]        =   sd_pipe.to(A						)
           sd_pipe.set_progress_bar_config(disable=A						)

           _UpperCAmelCase      :     Any        =   self.get_dummy_inputs(A						)
           _UpperCAmelCase      :     Optional[int]        =   "np"
           _UpperCAmelCase      :     Tuple        =   sd_pipe(**A						).frames
           _UpperCAmelCase      :     Union[str, Any]        =   frames[0][-3:, -3:, -1]

           assert frames[0].shape == (3_2, 3_2, 3)
           _UpperCAmelCase      :     int        =   np.array([1_0_6, 1_1_7, 1_1_3, 1_7_4, 1_3_7, 1_1_2, 1_4_8, 1_5_1, 1_3_1]						)

           assert np.abs(image_slice.flatten() - expected_slice						).max() < 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					:     List[Any]						):
           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A					,     expected_max_diff=5e-3						)



      @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."						)
      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass



      @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."						)
      def 						snake_case_ (					self					:     Any						):
           pass



      @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."						)
      def 						snake_case_ (					self					:     Tuple						):
           pass



      def 						snake_case_ (					self					:     int						):
           return super().test_progress_bar()







@slow
@skip_mps
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Any        =   VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"					,     torch_dtype=torch.floataa						)
           pipe.enable_model_cpu_offload()

           # 10 frames
           _UpperCAmelCase      :     Optional[int]        =   torch.Generator(device="cpu"						).manual_seed(0						)
           _UpperCAmelCase      :     Union[str, Any]        =   torch.randn((1, 1_0, 3, 1_0_2_4, 5_7_6)					,     generator=A						)
           _UpperCAmelCase      :     int        =   video.to("cuda"						)

           _UpperCAmelCase      :     Optional[Any]        =   "Spiderman is surfing"

           _UpperCAmelCase      :     Optional[int]        =   pipe(A					,     video=A					,     generator=A					,     num_inference_steps=3					,     output_type="pt"						).frames

           _UpperCAmelCase      :     int        =   np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656]						)
           assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array						).sum() < 1e-2





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   [], []
     while len(SCREAMING_SNAKE_CASE__    ) > 1:
          _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   min(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )
          start.append(SCREAMING_SNAKE_CASE__    )
          end.append(SCREAMING_SNAKE_CASE__    )
          collection.remove(SCREAMING_SNAKE_CASE__    )
          collection.remove(SCREAMING_SNAKE_CASE__    )
     end.reverse()
     return start + collection + end


if __name__ == "__main__":
 _lowerCAmelCase    : int							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase    : List[str]							=							[int(item) for item in user_input.split(",")]
 print(*merge_sort(unsorted), sep=",")
