

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     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





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




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




							def        A__  ( self					,						_A=0					,						_A=(4, 4, 6_4, 6_4)					,						_A=False							):
												__lowerCAmelCase					=							jnp.bfloataa if fpaa else jnp.floataa
												__lowerCAmelCase					=							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"							):
												__lowerCAmelCase					=							jnp.bfloataa if fpaa else jnp.floataa
												__lowerCAmelCase					=							'bf16' if fpaa else None

												__lowerCAmelCase,	__lowerCAmelCase					=							FlaxUNetaDConditionModel.from_pretrained(
												    _A					,						subfolder='unet'					,						dtype=_A					,						revision=_A							)
												return model, params




							def        A__  ( self					,						_A=0					,						_A=(4, 7_7, 7_6_8)					,						_A=False							):
												__lowerCAmelCase					=							jnp.bfloataa if fpaa else jnp.floataa
												__lowerCAmelCase					=							jnp.array(load_hf_numpy(self.get_file_format(_A					,						_A							)							)					,						dtype=_A							)
												return hidden_states




							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
							        [1_7, 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, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
							        # fmt: on
							    ]							)
							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'					,						fpaa=_A							)
												__lowerCAmelCase					=							self.get_latents(_A					,						fpaa=_A							)
												__lowerCAmelCase					=							self.get_encoder_hidden_states(_A					,						fpaa=_A							)

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

												assert sample.shape == latents.shape

												__lowerCAmelCase					=							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)					,						dtype=jnp.floataa							)
												__lowerCAmelCase					=							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
							        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
							        [1_7, 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, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
							        # fmt: on
							    ]							)
							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.get_unet_model(model_id='stabilityai/stable-diffusion-2'					,						fpaa=_A							)
												__lowerCAmelCase					=							self.get_latents(_A					,						shape=(4, 4, 9_6, 9_6)					,						fpaa=_A							)
												__lowerCAmelCase					=							self.get_encoder_hidden_states(_A					,						shape=(4, 7_7, 1_0_2_4)					,						fpaa=_A							)

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

												assert sample.shape == latents.shape

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      				=     {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LiltForQuestionAnswering''',
		    '''LiltForSequenceClassification''',
		    '''LiltForTokenClassification''',
		    '''LiltModel''',
		    '''LiltPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_lilt import (
				    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				    LiltPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






