



from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)






@flax.struct.dataclass
class 						__a (     _UpperCAmelCase				):
	SCREAMING_SNAKE_CASE       =       42






@flax_register_to_config
class 						__a (     nn.Module	,    _UpperCAmelCase	,    _UpperCAmelCase				):
	SCREAMING_SNAKE_CASE       =       32
	SCREAMING_SNAKE_CASE       =       4
	SCREAMING_SNAKE_CASE       =       4
	SCREAMING_SNAKE_CASE       =       (
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	SCREAMING_SNAKE_CASE       =       ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       (320, 640, 1280, 1280)
	SCREAMING_SNAKE_CASE       =       2
	SCREAMING_SNAKE_CASE       =       8
	SCREAMING_SNAKE_CASE       =       None
	SCREAMING_SNAKE_CASE       =       1280
	SCREAMING_SNAKE_CASE       =       0.0
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       jnp.floataa
	SCREAMING_SNAKE_CASE       =       True
	SCREAMING_SNAKE_CASE       =       0
	SCREAMING_SNAKE_CASE       =       False



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Optional[int])->							List[str]:
						# init input tensors
						__lowerCAmelCase	   =(1, self.in_channels, self.sample_size, self.sample_size)
						__lowerCAmelCase	   =jnp.zeros(lowercase__				,	dtype=jnp.floataa)
						__lowerCAmelCase	   =jnp.ones((1,)				,	dtype=jnp.intaa)
						__lowerCAmelCase	   =jnp.zeros((1, 1, self.cross_attention_dim)				,	dtype=jnp.floataa)

						__lowerCAmelCase	   =jax.random.split(lowercase__)
						__lowerCAmelCase	   ={"""params""": params_rng, """dropout""": dropout_rng}

						return self.init(lowercase__				,	lowercase__				,	lowercase__				,	lowercase__)["params"]



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Optional[int]:
						__lowerCAmelCase	   =self.block_out_channels
						__lowerCAmelCase	   =block_out_channels[0] * 4

						if self.num_attention_heads is not None:
											raise ValueError(
											    """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""")

						# If `num_attention_heads` is not defined (which is the case for most models)
						# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
						# The reason for this behavior is to correct for incorrectly named variables that were introduced
						# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
						# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
						# which is why we correct for the naming here.
						__lowerCAmelCase	   =self.num_attention_heads or self.attention_head_dim

						# input
						__lowerCAmelCase	   =nn.Conv(
						    block_out_channels[0]				,	kernel_size=(3, 3)				,	strides=(1, 1)				,	padding=((1, 1), (1, 1))				,	dtype=self.dtype				,	)

						# time
						__lowerCAmelCase	   =FlaxTimesteps(
						    block_out_channels[0]				,	flip_sin_to_cos=self.flip_sin_to_cos				,	freq_shift=self.config.freq_shift)
						__lowerCAmelCase	   =FlaxTimestepEmbedding(lowercase__				,	dtype=self.dtype)

						__lowerCAmelCase	   =self.only_cross_attention
						if isinstance(lowercase__				,	lowercase__):
											__lowerCAmelCase	   =(only_cross_attention,) * len(self.down_block_types)

						if isinstance(lowercase__				,	lowercase__):
											__lowerCAmelCase	   =(num_attention_heads,) * len(self.down_block_types)

						# down
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =block_out_channels[0]
						for i, down_block_type in enumerate(self.down_block_types):
											__lowerCAmelCase	   =output_channel
											__lowerCAmelCase	   =block_out_channels[i]
											__lowerCAmelCase	   =i == len(lowercase__) - 1

											if down_block_type == "CrossAttnDownBlock2D":
																__lowerCAmelCase	   =FlaxCrossAttnDownBlockaD(
																    in_channels=lowercase__				,	out_channels=lowercase__				,	dropout=self.dropout				,	num_layers=self.layers_per_block				,	num_attention_heads=num_attention_heads[i]				,	add_downsample=not is_final_block				,	use_linear_projection=self.use_linear_projection				,	only_cross_attention=only_cross_attention[i]				,	use_memory_efficient_attention=self.use_memory_efficient_attention				,	dtype=self.dtype				,	)
											else:
																__lowerCAmelCase	   =FlaxDownBlockaD(
																    in_channels=lowercase__				,	out_channels=lowercase__				,	dropout=self.dropout				,	num_layers=self.layers_per_block				,	add_downsample=not is_final_block				,	dtype=self.dtype				,	)

											down_blocks.append(lowercase__)
						__lowerCAmelCase	   =down_blocks

						# mid
						__lowerCAmelCase	   =FlaxUNetMidBlockaDCrossAttn(
						    in_channels=block_out_channels[-1]				,	dropout=self.dropout				,	num_attention_heads=num_attention_heads[-1]				,	use_linear_projection=self.use_linear_projection				,	use_memory_efficient_attention=self.use_memory_efficient_attention				,	dtype=self.dtype				,	)

						# up
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =list(reversed(lowercase__))
						__lowerCAmelCase	   =list(reversed(lowercase__))
						__lowerCAmelCase	   =list(reversed(lowercase__))
						__lowerCAmelCase	   =reversed_block_out_channels[0]
						for i, up_block_type in enumerate(self.up_block_types):
											__lowerCAmelCase	   =output_channel
											__lowerCAmelCase	   =reversed_block_out_channels[i]
											__lowerCAmelCase	   =reversed_block_out_channels[min(i + 1				,	len(lowercase__) - 1)]

											__lowerCAmelCase	   =i == len(lowercase__) - 1

											if up_block_type == "CrossAttnUpBlock2D":
																__lowerCAmelCase	   =FlaxCrossAttnUpBlockaD(
																    in_channels=lowercase__				,	out_channels=lowercase__				,	prev_output_channel=lowercase__				,	num_layers=self.layers_per_block + 1				,	num_attention_heads=reversed_num_attention_heads[i]				,	add_upsample=not is_final_block				,	dropout=self.dropout				,	use_linear_projection=self.use_linear_projection				,	only_cross_attention=only_cross_attention[i]				,	use_memory_efficient_attention=self.use_memory_efficient_attention				,	dtype=self.dtype				,	)
											else:
																__lowerCAmelCase	   =FlaxUpBlockaD(
																    in_channels=lowercase__				,	out_channels=lowercase__				,	prev_output_channel=lowercase__				,	num_layers=self.layers_per_block + 1				,	add_upsample=not is_final_block				,	dropout=self.dropout				,	dtype=self.dtype				,	)

											up_blocks.append(lowercase__)
											__lowerCAmelCase	   =output_channel
						__lowerCAmelCase	   =up_blocks

						# out
						__lowerCAmelCase	   =nn.GroupNorm(num_groups=32				,	epsilon=1e-5)
						__lowerCAmelCase	   =nn.Conv(
						    self.out_channels				,	kernel_size=(3, 3)				,	strides=(1, 1)				,	padding=((1, 1), (1, 1))				,	dtype=self.dtype				,	)



	def __call__(						self							:    Any				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	snake_case_							:    List[str]=None				,	snake_case_							:    Tuple=None				,	snake_case_							:    List[str] = True				,	snake_case_							:    List[str] = False				,	)->							List[str]:
						# 1. time
						if not isinstance(lowercase__				,	jnp.ndarray):
											__lowerCAmelCase	   =jnp.array([timesteps]				,	dtype=jnp.intaa)
						elif isinstance(lowercase__				,	jnp.ndarray) and len(timesteps.shape) == 0:
											__lowerCAmelCase	   =timesteps.astype(dtype=jnp.floataa)
											__lowerCAmelCase	   =jnp.expand_dims(lowercase__				,	0)

						__lowerCAmelCase	   =self.time_proj(lowercase__)
						__lowerCAmelCase	   =self.time_embedding(lowercase__)

						# 2. pre-process
						__lowerCAmelCase	   =jnp.transpose(lowercase__				,	(0, 2, 3, 1))
						__lowerCAmelCase	   =self.conv_in(lowercase__)

						# 3. down
						__lowerCAmelCase	   =(sample,)
						for down_block in self.down_blocks:
											if isinstance(lowercase__				,	lowercase__):
																__lowerCAmelCase	   =down_block(lowercase__				,	lowercase__				,	lowercase__				,	deterministic=not train)
											else:
																__lowerCAmelCase	   =down_block(lowercase__				,	lowercase__				,	deterministic=not train)
											down_block_res_samples += res_samples

						if down_block_additional_residuals is not None:
											__lowerCAmelCase	   =()

											for down_block_res_sample, down_block_additional_residual in zip(
											    lowercase__				,	lowercase__):
																down_block_res_sample += down_block_additional_residual
																new_down_block_res_samples += (down_block_res_sample,)

											__lowerCAmelCase	   =new_down_block_res_samples

						# 4. mid
						__lowerCAmelCase	   =self.mid_block(lowercase__				,	lowercase__				,	lowercase__				,	deterministic=not train)

						if mid_block_additional_residual is not None:
											sample += mid_block_additional_residual

						# 5. up
						for up_block in self.up_blocks:
											__lowerCAmelCase	   =down_block_res_samples[-(self.layers_per_block + 1) :]
											__lowerCAmelCase	   =down_block_res_samples[: -(self.layers_per_block + 1)]
											if isinstance(lowercase__				,	lowercase__):
																__lowerCAmelCase	   =up_block(
																    lowercase__				,	temb=lowercase__				,	encoder_hidden_states=lowercase__				,	res_hidden_states_tuple=lowercase__				,	deterministic=not train				,	)
											else:
																__lowerCAmelCase	   =up_block(lowercase__				,	temb=lowercase__				,	res_hidden_states_tuple=lowercase__				,	deterministic=not train)

        # 6. post-process
						__lowerCAmelCase	   =self.conv_norm_out(lowercase__)
						__lowerCAmelCase	   =nn.silu(lowercase__)
						__lowerCAmelCase	   =self.conv_out(lowercase__)
						__lowerCAmelCase	   =jnp.transpose(lowercase__				,	(0, 3, 1, 2))

						if not return_dict:
											return (sample,)

						return FlaxUNetaDConditionOutput(sample=lowercase__)









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




