






"""simple docstring"""





import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin






class 					a						( _lowerCamelCase  ,				_lowerCamelCase  ,				unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:					Dict						=			UNetaDModel
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			"""sample"""






    @property
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		int:
      lowerCamelCase_   					=						4
      lowerCamelCase_   					=						14
      lowerCamelCase_   					=						16

      lowerCamelCase_   					=						floats_tensor((batch_size, num_features, seq_len) ).to(__UpperCamelCase )
      lowerCamelCase_   					=						torch.tensor([10] * batch_size ).to(__UpperCamelCase )

      return {"sample": noise, "timestep": time_step}






    @property
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		List[str]:
      return (4, 14, 16)






    @property
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		List[str]:
      return (4, 14, 16)






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		str:
      pass






    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      pass






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							int )					->		Tuple:
      super().test_determinism()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							str )					->		List[Any]:
      super().test_outputs_equivalence()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							List[str] )					->		int:
      super().test_from_save_pretrained()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		str:
      super().test_from_save_pretrained_variant()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		str:
      super().test_model_from_pretrained()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Dict )					->		List[str]:
      super().test_output()






    def        UpperCamelCase	(	self					:							List[str] )					->		Dict:
      lowerCamelCase_   					=						{
          'block_out_channels': (32, 64, 128, 256),
          'in_channels': 14,
          'out_channels': 14,
          'time_embedding_type': 'positional',
          'use_timestep_embedding': True,
          'flip_sin_to_cos': False,
          'freq_shift': 1.0,
          'out_block_type': 'OutConv1DBlock',
          'mid_block_type': 'MidResTemporalBlock1D',
          'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
          'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
          'act_fn': 'mish',
      }
      lowerCamelCase_   					=						self.dummy_input
      return init_dict, inputs_dict






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							str )					->		Any:
      lowerCamelCase_   					=						UNetaDModel.from_pretrained(
          'bglick13/hopper-medium-v2-value-function-hor32'	,						output_loading_info=__UpperCamelCase	,						subfolder='unet' )
      self.assertIsNotNone(__UpperCamelCase )
      self.assertEqual(len(loading_info['missing_keys'] )	,						0 )

      model.to(__UpperCamelCase )
      lowerCamelCase_   					=						model(**self.dummy_input )

      assert image is not None, "Make sure output is not None"






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							str )					->		Any:
      lowerCamelCase_   					=						UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'	,						subfolder='unet' )
      torch.manual_seed(0 )
      if torch.cuda.is_available():
        torch.cuda.manual_seed_all(0 )

      lowerCamelCase_   					=						model.config.in_channels
      lowerCamelCase_   					=						16
      lowerCamelCase_   					=						torch.randn((1, seq_len, num_features) ).permute(
          0	,						2	,						1 )  # match original, we can update values and remove
      lowerCamelCase_   					=						torch.full((num_features,)	,						0 )

      with torch.no_grad():
        lowerCamelCase_   					=						model(__UpperCamelCase	,						__UpperCamelCase ).sample.permute(0	,						2	,						1 )

      lowerCamelCase_   					=						output[0, -3:, -3:].flatten()
      # fmt: off
      lowerCamelCase_   					=						torch.tensor([-2.137_172, 1.1_426_016, 0.3_688_687, -0.766_922, 0.7_303_146, 0.11_038_864, -0.4_760_633, 0.13_270_172, 0.02_591_348] )
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCamelCase	,						__UpperCamelCase	,						rtol=1e-3 ) )






    def        UpperCamelCase	(	self					:							str )					->		Optional[int]:
      pass






    @slow
    def        UpperCamelCase	(	self					:							Tuple )					->		Optional[Any]:
      lowerCamelCase_   					=						'harmonai/maestro-150k'
      lowerCamelCase_   					=						UNetaDModel.from_pretrained(__UpperCamelCase	,						subfolder='unet' )
      model.to(__UpperCamelCase )

      lowerCamelCase_   					=						65536
      lowerCamelCase_   					=						torch.sin(torch.arange(__UpperCamelCase )[None, None, :].repeat(1	,						2	,						1 ) ).to(__UpperCamelCase )
      lowerCamelCase_   					=						torch.tensor([1] ).to(__UpperCamelCase )

      with torch.no_grad():
        lowerCamelCase_   					=						model(__UpperCamelCase	,						__UpperCamelCase ).sample

      lowerCamelCase_   					=						output.abs().sum()
      lowerCamelCase_   					=						output.abs().max()

      assert (output_sum - 224.0896).abs() < 0.5
      assert (output_max - 0.0_607).abs() < 4e-4






class 					a						( _lowerCamelCase  ,				_lowerCamelCase  ,				unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:					Tuple						=			UNetaDModel
    SCREAMING_SNAKE_CASE						:					Optional[int]						=			"""sample"""






    @property
    def        UpperCamelCase	(	self					:							List[str] )					->		int:
      lowerCamelCase_   					=						4
      lowerCamelCase_   					=						14
      lowerCamelCase_   					=						16

      lowerCamelCase_   					=						floats_tensor((batch_size, num_features, seq_len) ).to(__UpperCamelCase )
      lowerCamelCase_   					=						torch.tensor([10] * batch_size ).to(__UpperCamelCase )

      return {"sample": noise, "timestep": time_step}






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Optional[int]:
      return (4, 14, 16)






    @property
    def        UpperCamelCase	(	self					:							List[str] )					->		List[str]:
      return (4, 14, 1)






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							int )					->		Union[str, Any]:
      super().test_determinism()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							List[Any] )					->		Optional[int]:
      super().test_outputs_equivalence()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Dict:
      super().test_from_save_pretrained()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Optional[int] )					->		List[Any]:
      super().test_from_save_pretrained_variant()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							List[str] )					->		Any:
      super().test_model_from_pretrained()






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Any )					->		int:
      lowerCamelCase_   					=						self.prepare_init_args_and_inputs_for_common()
      lowerCamelCase_   					=						self.model_class(**__UpperCamelCase )
      model.to(__UpperCamelCase )
      model.eval()

      with torch.no_grad():
        lowerCamelCase_   					=						model(**__UpperCamelCase )

        if isinstance(__UpperCamelCase	,						__UpperCamelCase ):
          lowerCamelCase_   					=						output.sample

      self.assertIsNotNone(__UpperCamelCase )
      lowerCamelCase_   					=						torch.Size((inputs_dict['sample'].shape[0], 1) )
      self.assertEqual(output.shape	,						__UpperCamelCase	,						'Input and output shapes do not match' )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[str]:
      pass






    def        UpperCamelCase	(	self					:							Any )					->		Tuple:
      pass






    def        UpperCamelCase	(	self					:							List[str] )					->		int:
      lowerCamelCase_   					=						{
          'in_channels': 14,
          'out_channels': 14,
          'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
          'up_block_types': [],
          'out_block_type': 'ValueFunction',
          'mid_block_type': 'ValueFunctionMidBlock1D',
          'block_out_channels': [32, 64, 128, 256],
          'layers_per_block': 1,
          'downsample_each_block': True,
          'use_timestep_embedding': True,
          'freq_shift': 1.0,
          'flip_sin_to_cos': False,
          'time_embedding_type': 'positional',
          'act_fn': 'mish',
      }
      lowerCamelCase_   					=						self.dummy_input
      return init_dict, inputs_dict






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							int )					->		int:
      lowerCamelCase_   					=						UNetaDModel.from_pretrained(
          'bglick13/hopper-medium-v2-value-function-hor32'	,						output_loading_info=__UpperCamelCase	,						subfolder='value_function' )
      self.assertIsNotNone(__UpperCamelCase )
      self.assertEqual(len(vf_loading_info['missing_keys'] )	,						0 )

      value_function.to(__UpperCamelCase )
      lowerCamelCase_   					=						value_function(**self.dummy_input )

      assert image is not None, "Make sure output is not None"






    @unittest.skipIf(torch_device == 'mps'	,						'mish op not supported in MPS' )
    def        UpperCamelCase	(	self					:							Tuple )					->		int:
      lowerCamelCase_   					=						UNetaDModel.from_pretrained(
          'bglick13/hopper-medium-v2-value-function-hor32'	,						output_loading_info=__UpperCamelCase	,						subfolder='value_function' )
      torch.manual_seed(0 )
      if torch.cuda.is_available():
        torch.cuda.manual_seed_all(0 )

      lowerCamelCase_   					=						value_function.config.in_channels
      lowerCamelCase_   					=						14
      lowerCamelCase_   					=						torch.randn((1, seq_len, num_features) ).permute(
          0	,						2	,						1 )  # match original, we can update values and remove
      lowerCamelCase_   					=						torch.full((num_features,)	,						0 )

      with torch.no_grad():
        lowerCamelCase_   					=						value_function(__UpperCamelCase	,						__UpperCamelCase ).sample

      # fmt: off
      lowerCamelCase_   					=						torch.tensor([165.25] * seq_len )
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCamelCase	,						__UpperCamelCase	,						rtol=1e-3 ) )






    def        UpperCamelCase	(	self					:							Any )					->		Tuple:
      pass












"""simple docstring"""





from __future__ import annotations

from random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  if len(_lowerCamelCase			) < 2:
    return collection
  lowerCamelCase_   					=						randrange(len(_lowerCamelCase			)			)  # Use random element as pivot
  lowerCamelCase_   					=						collection[pivot_index]
  lowerCamelCase_   					=						[]  # All elements greater than pivot
  lowerCamelCase_   					=						[]  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  return [*quick_sort(_lowerCamelCase			), pivot, *quick_sort(_lowerCamelCase			)]


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




