


"""simple docstring"""




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			):


		'''simple docstring'''



		def      __UpperCAmelCase			(    self				,					_a				,					_a ):
				return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A_ ) for s in shape] )}.npy'''

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

		def      __UpperCAmelCase			(    self				,					_a=0				,					_a=(4, 4, 64, 64)				,					_a=False ):
				__a	 =     jnp.bfloataa if fpaa else jnp.floataa
				__a	 =     jnp.array(load_hf_numpy(self.get_file_format(A_				,					A_ ) )				,					dtype=A_ )
				return image

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

				__a ,	__a	 =     FlaxUNetaDConditionModel.from_pretrained(
				    A_				,					subfolder='''unet'''				,					dtype=A_				,					revision=A_ )
				return model, params

		def      __UpperCAmelCase			(    self				,					_a=0				,					_a=(4, 77, 768)				,					_a=False ):
				__a	 =     jnp.bfloataa if fpaa else jnp.floataa
				__a	 =     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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
		        # fmt: on
		    ] )
		def      __UpperCAmelCase			(    self				,					_a				,					_a				,					_a ):
				__a ,	__a	 =     self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''				,					fpaa=A_ )
				__a	 =     self.get_latents(A_				,					fpaa=A_ )
				__a	 =     self.get_encoder_hidden_states(A_				,					fpaa=A_ )

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

				assert sample.shape == latents.shape

				__a	 =     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )				,					dtype=jnp.floataa )
				__a	 =     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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
		        # fmt: on
		    ] )
		def      __UpperCAmelCase			(    self				,					_a				,					_a				,					_a ):
				__a ,	__a	 =     self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''				,					fpaa=A_ )
				__a	 =     self.get_latents(A_				,					shape=(4, 4, 96, 96)				,					fpaa=A_ )
				__a	 =     self.get_encoder_hidden_states(A_				,					shape=(4, 77, 1_024)				,					fpaa=A_ )

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

				assert sample.shape == latents.shape

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








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_xlm_roberta_xl''': [
        '''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaXLConfig''',
        '''XLMRobertaXLOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLMRobertaXLForCausalLM''',
		    '''XLMRobertaXLForMaskedLM''',
		    '''XLMRobertaXLForMultipleChoice''',
		    '''XLMRobertaXLForQuestionAnswering''',
		    '''XLMRobertaXLForSequenceClassification''',
		    '''XLMRobertaXLForTokenClassification''',
		    '''XLMRobertaXLModel''',
		    '''XLMRobertaXLPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_xlm_roberta_xl import (
		    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XLMRobertaXLConfig,
		    XLMRobertaXLOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_roberta_xl import (
				    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMRobertaXLForCausalLM,
				    XLMRobertaXLForMaskedLM,
				    XLMRobertaXLForMultipleChoice,
				    XLMRobertaXLForQuestionAnswering,
				    XLMRobertaXLForSequenceClassification,
				    XLMRobertaXLForTokenClassification,
				    XLMRobertaXLModel,
				    XLMRobertaXLPreTrainedModel,
				)

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)