

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   _a					(			unittest.TestCase				):




      """simple docstring"""

      def 		__A	(   self							:				Union[str, Any]						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				List[str]	):
            return f'''gaussian_noise_s={seed}_shape={"_".join([str(UpperCAmelCase	) for s in shape]	)}.npy'''





      def 		__A	(   self							:				Union[str, Any]	):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				List[Any]=0						,			UpperCAmelCase							:				List[Any]=(4, 4, 64, 64)						,			UpperCAmelCase							:				Optional[Any]=False	):
            A_						  =				jnp.bfloataa if fpaa else jnp.floataa
            A_						  =				jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase						,			UpperCAmelCase	)	)						,			dtype=UpperCAmelCase	)
            return image





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				List[str]=False						,			UpperCAmelCase							:				Union[str, Any]="CompVis/stable-diffusion-v1-4"	):
            A_						  =				jnp.bfloataa if fpaa else jnp.floataa
            A_						  =				"bf16" if fpaa else None

            A_  ,     A_						  =				FlaxUNetaDConditionModel.from_pretrained(
                UpperCAmelCase						,			subfolder="unet"						,			dtype=UpperCAmelCase						,			revision=UpperCAmelCase	)
            return model, params





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				Optional[int]=0						,			UpperCAmelCase							:				List[str]=(4, 77, 768)						,			UpperCAmelCase							:				Tuple=False	):
            A_						  =				jnp.bfloataa if fpaa else jnp.floataa
            A_						  =				jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase						,			UpperCAmelCase	)	)						,			dtype=UpperCAmelCase	)
            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, 1000, [-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							:				Tuple						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Any	):
            A_  ,     A_						  =				self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"						,			fpaa=UpperCAmelCase	)
            A_						  =				self.get_latents(UpperCAmelCase						,			fpaa=UpperCAmelCase	)
            A_						  =				self.get_encoder_hidden_states(UpperCAmelCase						,			fpaa=UpperCAmelCase	)

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

            assert sample.shape == latents.shape

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

            # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
            assert jnp.allclose(UpperCAmelCase						,			UpperCAmelCase						,			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, 1000, [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							:				Any						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Any	):
            A_  ,     A_						  =				self.get_unet_model(model_id="stabilityai/stable-diffusion-2"						,			fpaa=UpperCAmelCase	)
            A_						  =				self.get_latents(UpperCAmelCase						,			shape=(4, 4, 96, 96)						,			fpaa=UpperCAmelCase	)
            A_						  =				self.get_encoder_hidden_states(UpperCAmelCase						,			shape=(4, 77, 1024)						,			fpaa=UpperCAmelCase	)

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

            assert sample.shape == latents.shape

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

