




"""simple docstring"""





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A				=							TypeVar("""T""")



class   a__					(						Generic[T] ):
			lowercase_		=						42  # Cache store of keys
			lowercase_		=						42  # References of the keys in cache
			lowercase_		=						1_0  # Maximum capacity of cache


			def __init__(   self   :     int   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       deque()
						__UpperCAmelCase						:			Dict												=       set()
						if not n:
									__UpperCAmelCase						:			str												=       sys.maxsize
						elif n < 0:
									raise ValueError("n should be an integer greater than 0.")
						else:
									__UpperCAmelCase						:			Dict												=       n


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     T):


						"""simple docstring"""




						if x not in self.key_reference:
									if len(self.dq_store) == LRUCache._MAX_CAPACITY:
												__UpperCAmelCase						:			Dict												=       self.dq_store.pop()
												self.key_reference.remove(UpperCamelCase_)
						else:
									self.dq_store.remove(UpperCamelCase_)

						self.dq_store.appendleft(UpperCamelCase_)
						self.key_reference.add(UpperCamelCase_)


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						for k in self.dq_store:
									print(UpperCamelCase_)


			def __repr__(   self   :     Tuple):


						"""simple docstring"""




						return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store)}"


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					A				=							LRUCache(4)
					lru_cache.refer("""A""")
					lru_cache.refer(2)
					lru_cache.refer(3)
					lru_cache.refer("""A""")
					lru_cache.refer(4)
					lru_cache.refer(5)
					lru_cache.display()

					print(lru_cache)
					assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




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 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self							,				a							,				a					)       ->   Optional[int]:


								'''simple docstring'''

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

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

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

						def 			A_					(      self							,				a=0							,				a=(4, 4, 64, 64)							,				a=False					)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								return image

						def 			A_					(      self							,				a=False							,				a="CompVis/stable-diffusion-v1-4"					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						"""bf16""" if fpaa else None

								_UpperCamelCase     ,						_UpperCamelCase         =						FlaxUNetaDConditionModel.from_pretrained(
								    a							,				subfolder="""unet"""							,				dtype=a							,				revision=a					)
								return model, params

						def 			A_					(      self							,				a=0							,				a=(4, 77, 7_68)							,				a=False					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								return hidden_states

						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
						        [17, 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, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]					)
						def 			A_					(      self							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

								_UpperCamelCase         =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)							,				dtype=jnp.floataa					)
								_UpperCamelCase         =						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, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
						        [17, 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, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]					)
						def 			A_					(      self							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				shape=(4, 4, 96, 96)							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				shape=(4, 77, 10_24)							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

								_UpperCamelCase         =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)							,				dtype=jnp.floataa					)
								_UpperCamelCase         =						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					)



