

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 				lowercase       ( unittest.TestCase    ):




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




				def 					a							(      self ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def 					a							(      self   ,     snake_case=0   ,     snake_case=(4, 4, 64, 64)   ,     snake_case=False ):
											snake_case_           =					jnp.bfloataa if fpaa else jnp.floataa
											snake_case_           =					jnp.array(load_hf_numpy(self.get_file_format(snake_case   ,     snake_case ) )   ,     dtype=snake_case )
											return image




				def 					a							(      self   ,     snake_case=False   ,     snake_case="CompVis/stable-diffusion-v1-4" ):
											snake_case_           =					jnp.bfloataa if fpaa else jnp.floataa
											snake_case_           =					'bf16' if fpaa else None

											snake_case_       ,					snake_case_           =					FlaxUNetaDConditionModel.from_pretrained(
											    snake_case   ,     subfolder='unet'   ,     dtype=snake_case   ,     revision=snake_case )
											return model, params




				def 					a							(      self   ,     snake_case=0   ,     snake_case=(4, 77, 768)   ,     snake_case=False ):
											snake_case_           =					jnp.bfloataa if fpaa else jnp.floataa
											snake_case_           =					jnp.array(load_hf_numpy(self.get_file_format(snake_case   ,     snake_case ) )   ,     dtype=snake_case )
											return hidden_states




				@parameterized.expand(
				    [
				        # fmt: off
				        [83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
				        [17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
				        [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
				        [3, 1000, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
				        # fmt: on
				    ] )
				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_       ,					snake_case_           =					self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'   ,     fpaa=snake_case )
											snake_case_           =					self.get_latents(snake_case   ,     fpaa=snake_case )
											snake_case_           =					self.get_encoder_hidden_states(snake_case   ,     fpaa=snake_case )

											snake_case_           =					model.apply(
											    {'params': params}   ,     snake_case   ,     jnp.array(snake_case   ,     dtype=jnp.intaa )   ,     encoder_hidden_states=snake_case   ,     ).sample

											assert sample.shape == latents.shape

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

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




				@parameterized.expand(
				    [
				        # fmt: off
				        [83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
				        [17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
				        [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
				        [3, 1000, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
				        # fmt: on
				    ] )
				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_       ,					snake_case_           =					self.get_unet_model(model_id='stabilityai/stable-diffusion-2'   ,     fpaa=snake_case )
											snake_case_           =					self.get_latents(snake_case   ,     shape=(4, 4, 96, 96)   ,     fpaa=snake_case )
											snake_case_           =					self.get_encoder_hidden_states(snake_case   ,     shape=(4, 77, 1024)   ,     fpaa=snake_case )

											snake_case_           =					model.apply(
											    {'params': params}   ,     snake_case   ,     jnp.array(snake_case   ,     dtype=jnp.intaa )   ,     encoder_hidden_states=snake_case   ,     ).sample

											assert sample.shape == latents.shape

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

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







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




