





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 						UpperCamelCase_  (  unittest.TestCase							):



					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     Dict						):
							return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase						) for s in shape]						)}.npy"



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



					def   __a     (		self     :     Dict       ,      lowerCamelCase     :     List[str]=0       ,      lowerCamelCase     :     Optional[Any]=(4, 4, 64, 64)       ,      lowerCamelCase     :     Optional[Any]=False						):
							lowerCamelCase_	:     Tuple 						=      jnp.bfloataa if fpaa else jnp.floataa
							lowerCamelCase_	:     Union[str, Any] 						=      jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase       ,      lowerCamelCase						)						)       ,      dtype=lowerCamelCase						)
							return image



					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     List[str]=False       ,      lowerCamelCase     :     Optional[int]="CompVis/stable-diffusion-v1-4"						):
							lowerCamelCase_	:     str 						=      jnp.bfloataa if fpaa else jnp.floataa
							lowerCamelCase_	:     Tuple 						=      'bf16' if fpaa else None

							lowerCamelCase_						,      lowerCamelCase_	:     str 						=      FlaxUNetaDConditionModel.from_pretrained(
							    lowerCamelCase       ,      subfolder='unet'       ,      dtype=lowerCamelCase       ,      revision=lowerCamelCase						)
							return model, params



					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     Optional[int]=0       ,      lowerCamelCase     :     int=(4, 77, 7_68)       ,      lowerCamelCase     :     List[str]=False						):
							lowerCamelCase_	:     Tuple 						=      jnp.bfloataa if fpaa else jnp.floataa
							lowerCamelCase_	:     Optional[Any] 						=      jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase       ,      lowerCamelCase						)						)       ,      dtype=lowerCamelCase						)
							return hidden_states



					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
					        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
					        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
					        [3, 10_00, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
					        # fmt: on
					    ]						)
					def   __a     (		self     :     Dict       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'       ,      fpaa=lowerCamelCase						)
							lowerCamelCase_	:     Any 						=      self.get_latents(lowerCamelCase       ,      fpaa=lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      self.get_encoder_hidden_states(lowerCamelCase       ,      fpaa=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      model.apply(
							    {'params': params}       ,      lowerCamelCase       ,      jnp.array(lowerCamelCase       ,      dtype=jnp.intaa						)       ,      encoder_hidden_states=lowerCamelCase       ,      ).sample

							assert sample.shape == latents.shape

							lowerCamelCase_	:     List[Any] 						=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)       ,      dtype=jnp.floataa						)
							lowerCamelCase_	:     List[str] 						=      jnp.array(lowerCamelCase       ,      dtype=jnp.floataa						)

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





					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
					        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
					        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
					        [3, 10_00, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
					        # fmt: on
					    ]						)
					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     int						):
							lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      self.get_unet_model(model_id='stabilityai/stable-diffusion-2'       ,      fpaa=lowerCamelCase						)
							lowerCamelCase_	:     Any 						=      self.get_latents(lowerCamelCase       ,      shape=(4, 4, 96, 96)       ,      fpaa=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      self.get_encoder_hidden_states(lowerCamelCase       ,      shape=(4, 77, 10_24)       ,      fpaa=lowerCamelCase						)

							lowerCamelCase_	:     Optional[Any] 						=      model.apply(
							    {'params': params}       ,      lowerCamelCase       ,      jnp.array(lowerCamelCase       ,      dtype=jnp.intaa						)       ,      encoder_hidden_states=lowerCamelCase       ,      ).sample

							assert sample.shape == latents.shape

							lowerCamelCase_	:     List[Any] 						=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)       ,      dtype=jnp.floataa						)
							lowerCamelCase_	:     int 						=      jnp.array(lowerCamelCase       ,      dtype=jnp.floataa						)

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







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     int 						=      str(bin(lowerCAmelCase__						)						)
		binary_number += "0" * shift_amount
		return binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     Union[str, Any] 						=      str(bin(lowerCAmelCase__						)						)[2:]
		if shift_amount >= len(lowerCAmelCase__						):
				return "0b0"
		lowerCamelCase_	:     List[str] 						=      binary_number[: len(lowerCAmelCase__						) - shift_amount]
		return "0b" + shifted_binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number >= 0:  # Get binary representation of positive number
				lowerCamelCase_	:     List[Any] 						=      '0' + str(bin(lowerCAmelCase__						)						).strip('-'						)[2:]
		else:  # Get binary (2's complement) representation of negative number
				lowerCamelCase_	:     Any 						=      len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
				lowerCamelCase_	:     List[str] 						=      bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
				lowerCamelCase_	:     List[str] 						=      (
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase__						):
				return "0b" + binary_number[0] * len(lowerCAmelCase__						)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
