

'''simple docstring'''



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard



@slow
@require_flax
class lowerCamelCase (  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(      self       :						Optional[int] )			->		str:





									'''simple docstring'''
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()


			def lowercase__						(      self       :						Union[str, Any] )			->		Dict:





									'''simple docstring'''
									A__    ,						A__     :   str      =FlaxStableDiffusionPipeline.from_pretrained(
									    """stabilityai/stable-diffusion-2"""		,			revision="""bf16"""		,			dtype=jnp.bfloataa		,			)

									A__     :   int      ="""A painting of a squirrel eating a burger"""
									A__     :   Tuple      =jax.device_count()
									A__     :   Optional[int]      =num_samples * [prompt]
									A__     :   Any      =sd_pipe.prepare_inputs(lowerCAmelCase_ )

									A__     :   Any      =replicate(lowerCAmelCase_ )
									A__     :   Optional[int]      =shard(lowerCAmelCase_ )

									A__     :   List[Any]      =jax.random.PRNGKey(0 )
									A__     :   Optional[int]      =jax.random.split(lowerCAmelCase_		,			jax.device_count() )

									A__     :   int      =sd_pipe(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			num_inference_steps=25		,			jit=lowerCAmelCase_ )[0]
									assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

									A__     :   Any      =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
									A__     :   Tuple      =images[0, 2_53:2_56, 2_53:2_56, -1]

									A__     :   int      =jnp.asarray(jax.device_get(image_slice.flatten() ) )
									A__     :   Optional[int]      =jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512] )
									print(f"output_slice: {output_slice}" )
									assert jnp.abs(output_slice - expected_slice ).max() < 1e-2



			def lowercase__						(      self       :						List[Any] )			->		Dict:





									'''simple docstring'''
									A__     :   str      ="""stabilityai/stable-diffusion-2"""
									A__    ,						A__     :   int      =FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_		,			subfolder="""scheduler""" )
									A__    ,						A__     :   Any      =FlaxStableDiffusionPipeline.from_pretrained(
									    lowerCAmelCase_		,			scheduler=lowerCAmelCase_		,			revision="""bf16"""		,			dtype=jnp.bfloataa		,			)
									A__     :   Any      =scheduler_params

									A__     :   Tuple      ="""A painting of a squirrel eating a burger"""
									A__     :   Dict      =jax.device_count()
									A__     :   Tuple      =num_samples * [prompt]
									A__     :   str      =sd_pipe.prepare_inputs(lowerCAmelCase_ )

									A__     :   Union[str, Any]      =replicate(lowerCAmelCase_ )
									A__     :   Tuple      =shard(lowerCAmelCase_ )

									A__     :   Optional[Any]      =jax.random.PRNGKey(0 )
									A__     :   Optional[Any]      =jax.random.split(lowerCAmelCase_		,			jax.device_count() )

									A__     :   List[Any]      =sd_pipe(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			num_inference_steps=25		,			jit=lowerCAmelCase_ )[0]
									assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

									A__     :   Dict      =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
									A__     :   str      =images[0, 2_53:2_56, 2_53:2_56, -1]

									A__     :   Union[str, Any]      =jnp.asarray(jax.device_get(image_slice.flatten() ) )
									A__     :   Union[str, Any]      =jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297] )
									print(f"output_slice: {output_slice}" )
									assert jnp.abs(output_slice - expected_slice ).max() < 1e-2





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






