
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   def __lt__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return self[-1] < other[-1]


   def __eq__(					self     :							Any			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      return self[-1] == other[-1]






def   lowerCamelCase__							(					a      )      ->   list:
   _A:  list[Stack]					=       []
   # sort into stacks
   for element in collection:
      _A:  Union[str, Any]					=       Stack([element]      )
      _A:  Optional[int]					=       bisect_left(a		,		a      )
      if i != len(a      ):
         stacks[i].append(a      )
      else:
         stacks.append(a      )

    # use a heap-based merge to merge stack efficiently
   _A:  Any					=       merge(*(reversed(a      ) for stack in stacks)      )
   return collection


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





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return F"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_     ) for s in shape]     )}.npy"""


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							Tuple=(4, 4, 6_4, 6_4)			,			lowerCAmelCase_     :							List[str]=False     ):

      """simple docstring"""
      _A:  List[str]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Optional[int]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return image


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Optional[Any]="CompVis/stable-diffusion-v1-4"     ):

      """simple docstring"""
      _A:  Tuple					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  str					=       '''bf16''' if fpaa else None

      _A			,  _A:  Union[str, Any]					=       FlaxUNetaDConditionModel.from_pretrained(
          lowerCAmelCase_			,			subfolder='''unet'''			,			dtype=lowerCAmelCase_			,			revision=lowerCAmelCase_     )
      return model, params


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							str=(4, 7_7, 7_6_8)			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      _A:  Optional[int]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Union[str, Any]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return hidden_states


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      _A			,  _A:  Optional[Any]					=       self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''			,			fpaa=lowerCAmelCase_     )
      _A:  List[str]					=       self.get_latents(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )
      _A:  Optional[int]					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )

      _A:  List[str]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  Any					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  Tuple					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_latents(lowerCAmelCase_			,			shape=(4, 4, 9_6, 9_6)			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			shape=(4, 7_7, 1_0_2_4)			,			fpaa=lowerCAmelCase_     )

      _A:  Optional[int]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  List[str]					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  List[str]					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )



