





'''simple docstring'''





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard







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




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             super().tearDown()
             gc.collect()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-canny'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Optional[Any]	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Union[str, Any]	=		controlnet_params

             _lowerCamelCase							:     str	=		'''bird'''
             _lowerCamelCase							:     Union[str, Any]	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		pipe.prepare_text_inputs([prompts] * num_samples)

             _lowerCamelCase							:     int	=		load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''')
             _lowerCamelCase							:     Tuple	=		pipe.prepare_image_inputs([canny_image] * num_samples)

             _lowerCamelCase							:     Union[str, Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     Tuple	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     Dict	=		replicate(a__)
             _lowerCamelCase							:     Tuple	=		shard(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)

             _lowerCamelCase							:     Optional[Any]	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

             _lowerCamelCase							:     Optional[int]	=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
             _lowerCamelCase							:     List[Any]	=		images[0, 253:256, 253:256, -1]

             _lowerCamelCase							:     Union[str, Any]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		jnp.array(
                 [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
             print(F"""output_slice: {output_slice}""")
             assert jnp.abs(output_slice - expected_slice).max() < 1e-2



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-openpose'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Any	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Optional[Any]	=		controlnet_params

             _lowerCamelCase							:     Optional[Any]	=		'''Chef in the kitchen'''
             _lowerCamelCase							:     int	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		pipe.prepare_text_inputs([prompts] * num_samples)

             _lowerCamelCase							:     List[Any]	=		load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png''')
             _lowerCamelCase							:     str	=		pipe.prepare_image_inputs([pose_image] * num_samples)

             _lowerCamelCase							:     Optional[Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     List[str]	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     str	=		replicate(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)
             _lowerCamelCase							:     List[Any]	=		shard(a__)

             _lowerCamelCase							:     Any	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

             _lowerCamelCase							:     int	=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
             _lowerCamelCase							:     Dict	=		images[0, 253:256, 253:256, -1]

             _lowerCamelCase							:     Optional[int]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		jnp.array(
                 [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
             print(F"""output_slice: {output_slice}""")
             assert jnp.abs(output_slice - expected_slice).max() < 1e-2








def    __UpperCAmelCase(      lowercase_      ):
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       _lowerCamelCase							:     Tuple	=		len(lowercase_      )
       _lowerCamelCase							:     List[str]	=		max(lowercase_      )
       _lowerCamelCase							:     Any	=		min(lowercase_      )

       # create the counting array
       _lowerCamelCase							:     str	=		coll_max + 1 - coll_min
       _lowerCamelCase							:     Union[str, Any]	=		[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,    lowercase_      ):
              _lowerCamelCase							:     Union[str, Any]	=		counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       _lowerCamelCase							:     Dict	=		[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,    lowercase_      )      ):
              _lowerCamelCase							:     Union[str, Any]	=		collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered




def    __UpperCAmelCase(      lowercase_      ):
       return "".join([chr(lowercase_      ) for i in counting_sort([ord(lowercase_      ) for c in string]      )]      )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))

