





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     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> Tuple:






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






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   int  				=     FlaxControlNetModel.from_pretrained(
									    """lllyasviel/sd-controlnet-canny""",   from_pt=lowercase_,   dtype=jnp.bfloataa  )
									lowercase_				,		lowercase_	:   Tuple  				=     FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5""",   controlnet=lowercase_,   from_pt=lowercase_,   dtype=jnp.bfloataa  )
									lowercase_	:   Optional[int]  				=     controlnet_params

									lowercase_	:   int  				=     """bird"""
									lowercase_	:   int  				=     jax.device_count()
									lowercase_	:   str  				=     pipe.prepare_text_inputs([prompts] * num_samples  )

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

									lowercase_	:   Union[str, Any]  				=     jax.random.PRNGKey(0  )
									lowercase_	:   List[Any]  				=     jax.random.split(lowercase_,   jax.device_count()  )

									lowercase_	:   Any  				=     replicate(lowercase_  )
									lowercase_	:   Tuple  				=     shard(lowercase_  )
									lowercase_	:   Union[str, Any]  				=     shard(lowercase_  )

									lowercase_	:   List[Any]  				=     pipe(
									    prompt_ids=lowercase_,   image=lowercase_,   params=lowercase_,   prng_seed=lowercase_,   num_inference_steps=50,   jit=lowercase_,   ).images
									assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

									lowercase_	:   List[Any]  				=     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
									lowercase_	:   Tuple  				=     images[0, 2_53:2_56, 2_53:2_56, -1]

									lowercase_	:   Optional[int]  				=     jnp.asarray(jax.device_get(image_slice.flatten()  )  )
									lowercase_	:   str  				=     jnp.array(
									    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
									print(f"""output_slice: {output_slice}"""  )
									assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   Tuple  				=     FlaxControlNetModel.from_pretrained(
									    """lllyasviel/sd-controlnet-openpose""",   from_pt=lowercase_,   dtype=jnp.bfloataa  )
									lowercase_				,		lowercase_	:   List[Any]  				=     FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5""",   controlnet=lowercase_,   from_pt=lowercase_,   dtype=jnp.bfloataa  )
									lowercase_	:   List[Any]  				=     controlnet_params

									lowercase_	:   Union[str, Any]  				=     """Chef in the kitchen"""
									lowercase_	:   Dict  				=     jax.device_count()
									lowercase_	:   Optional[int]  				=     pipe.prepare_text_inputs([prompts] * num_samples  )

									lowercase_	:   Dict  				=     load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""  )
									lowercase_	:   Tuple  				=     pipe.prepare_image_inputs([pose_image] * num_samples  )

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

									lowercase_	:   int  				=     replicate(lowercase_  )
									lowercase_	:   List[str]  				=     shard(lowercase_  )
									lowercase_	:   Tuple  				=     shard(lowercase_  )

									lowercase_	:   List[Any]  				=     pipe(
									    prompt_ids=lowercase_,   image=lowercase_,   params=lowercase_,   prng_seed=lowercase_,   num_inference_steps=50,   jit=lowercase_,   ).images
									assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

									lowercase_	:   Dict  				=     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
									lowercase_	:   List[str]  				=     images[0, 2_53:2_56, 2_53:2_56, -1]

									lowercase_	:   int  				=     jnp.asarray(jax.device_get(image_slice.flatten()  )  )
									lowercase_	:   Tuple  				=     jnp.array(
									    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
									print(f"""output_slice: {output_slice}"""  )
									assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2

"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_            =    [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE_            =    [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       len(lowerCAmelCase			)

    for i in range(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for j in range(i + 1			,					lowerCAmelCase			):
            if arr[i] < arr[j]:
                UpperCAmelCase          =       arr[j]
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    for i, outer in enumerate(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for inner in arr[i + 1 :]:
            if outer < inner:
                UpperCAmelCase          =       inner
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       []
    UpperCAmelCase          =       [-1] * arr_size

    for index in reversed(range(lowerCAmelCase			)			):
        if stack:
            while stack[-1] <= arr[index]:
                stack.pop()
                if not stack:
                    break
        if stack:
            UpperCAmelCase          =       stack[-1]
        stack.append(arr[index]			)
    return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   SCREAMING_SNAKE_CASE_            =    (
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )



