
"""simple docstring"""




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 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          return f"""gaussian_noise_s={seed}_shape={"_".join([str(_a				) for s in shape]				)}.npy"""
   def 			__magic_name__			(			self				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
   def 			__magic_name__			(			self	,    _a=0	,    _a=(4, 4, 64, 64)	,    _a=False				):
          lowercase    :				int       		=							jnp.bfloataa if fpaa else jnp.floataa
          lowercase    :				List[Any]       		=							jnp.array(load_hf_numpy(self.get_file_format(_a	,    _a				)				)	,    dtype=_a				)
          return image
   def 			__magic_name__			(			self	,    _a=False	,    _a="CompVis/stable-diffusion-v1-4"				):
          lowercase    :				str       		=							jnp.bfloataa if fpaa else jnp.floataa
          lowercase    :				Any       		=							"bf16" if fpaa else None

          lowercase		,    lowercase    :				List[Any]       		=							FlaxUNetaDConditionModel.from_pretrained(
              _a	,    subfolder="unet"	,    dtype=_a	,    revision=_a				)
          return model, params
   def 			__magic_name__			(			self	,    _a=0	,    _a=(4, 77, 768)	,    _a=False				):
          lowercase    :				List[Any]       		=							jnp.bfloataa if fpaa else jnp.floataa
          lowercase    :				Optional[int]       		=							jnp.array(load_hf_numpy(self.get_file_format(_a	,    _a				)				)	,    dtype=_a				)
          return hidden_states
   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
           [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
           [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
           [3, 1_000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
           # fmt: on
       ]				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase		,    lowercase    :				Dict       		=							self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"	,    fpaa=_a				)
          lowercase    :				List[Any]       		=							self.get_latents(_a	,    fpaa=_a				)
          lowercase    :				List[Any]       		=							self.get_encoder_hidden_states(_a	,    fpaa=_a				)

          lowercase    :				List[str]       		=							model.apply(
              {"params": params}	,    _a	,    jnp.array(_a	,    dtype=jnp.intaa				)	,    encoder_hidden_states=_a	,    ).sample

          assert sample.shape == latents.shape

          lowercase    :				Optional[int]       		=							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)	,    dtype=jnp.floataa				)
          lowercase    :				List[str]       		=							jnp.array(_a	,    dtype=jnp.floataa				)

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

   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
           [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
           [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
           [3, 1_000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
           # fmt: on
       ]				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase		,    lowercase    :				str       		=							self.get_unet_model(model_id="stabilityai/stable-diffusion-2"	,    fpaa=_a				)
          lowercase    :				Tuple       		=							self.get_latents(_a	,    shape=(4, 4, 96, 96)	,    fpaa=_a				)
          lowercase    :				Optional[Any]       		=							self.get_encoder_hidden_states(_a	,    shape=(4, 77, 1_024)	,    fpaa=_a				)

          lowercase    :				Any       		=							model.apply(
              {"params": params}	,    _a	,    jnp.array(_a	,    dtype=jnp.intaa				)	,    encoder_hidden_states=_a	,    ).sample

          assert sample.shape == latents.shape

          lowercase    :				Union[str, Any]       		=							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)	,    dtype=jnp.floataa				)
          lowercase    :				Dict       		=							jnp.array(_a	,    dtype=jnp.floataa				)

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




"""simple docstring"""




def   __magic_name__	(      __snake_case				:       list  ) ->				list:

       if len(__snake_case  ) < 2:
              return collection

       def circle_sort_util(__snake_case				:       list     , __snake_case				:       int     , __snake_case				:       int  ) -> bool:
              lowercase    :				List[Any]       		=							False

              if low == high:
                     return swapped

              lowercase    :				Union[str, Any]       		=							low
              lowercase    :				str       		=							high

              while left < right:
                     if collection[left] > collection[right]:
                            lowercase		,    lowercase    :				Optional[Any]       		=							(
                                collection[right],
                                collection[left],
                            )
                            lowercase    :				Tuple       		=							True

                     left += 1
                     right -= 1

              if left == right and collection[left] > collection[right + 1]:
                     lowercase		,    lowercase    :				str       		=							(
                         collection[right + 1],
                         collection[left],
                     )

                     lowercase    :				Union[str, Any]       		=							True

              lowercase    :				Any       		=							low + int((high - low) / 2  )
              lowercase    :				Tuple       		=							circle_sort_util(__snake_case     , __snake_case     , __snake_case  )
              lowercase    :				List[Any]       		=							circle_sort_util(__snake_case     , mid + 1     , __snake_case  )

              return swapped or left_swap or right_swap

       lowercase    :				int       		=							True

       while is_not_sorted is True:
              lowercase    :				int       		=							circle_sort_util(__snake_case     , 0     , len(__snake_case  ) - 1  )

       return collection


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


