

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      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   jnp.ndarray






@flax_register_to_config
class      lowerCamelCase_      (	nn.Module						,      _lowercase						,      _lowercase	):
	_lowercase		:   int     					=				32
	_lowercase		:   int     					=				4
	_lowercase		:   int     					=				4
	_lowercase		:   Tuple[str]     					=				(
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	_lowercase		:   Tuple[str]     					=				("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
	_lowercase		:   Union[bool, Tuple[bool]]     					=				False
	_lowercase		:   Tuple[int]     					=				(320, 640, 1280, 1280)
	_lowercase		:   int     					=				2
	_lowercase		:   Union[int, Tuple[int]]     					=				8
	_lowercase		:   Optional[Union[int, Tuple[int]]]     					=				None
	_lowercase		:   int     					=				1280
	_lowercase		:   float     					=				0.0
	_lowercase		:   bool     					=				False
	_lowercase		:   jnp.dtype     					=				jnp.floataa
	_lowercase		:   bool     					=				True
	_lowercase		:   int     					=				0
	_lowercase		:   bool     					=				False




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     jax.random.KeyArray							):
		# init input tensors
		__A :			Tuple		    =			(1, self.in_channels, self.sample_size, self.sample_size)
		__A :			str		    =			jnp.zeros(__A					,      dtype=jnp.floataa							)
		__A :			Optional[int]		    =			jnp.ones((1,)					,      dtype=jnp.intaa							)
		__A :			Optional[int]		    =			jnp.zeros((1, 1, self.cross_attention_dim)					,      dtype=jnp.floataa							)

		__A    ,				__A :			List[Any]		    =			jax.random.split(__A							)
		__A :			Any		    =			{"""params""": params_rng, """dropout""": dropout_rng}

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




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Union[str, Any]		    =			self.block_out_channels
		__A :			Optional[Any]		    =			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.
		__A :			Optional[int]		    =			self.num_attention_heads or self.attention_head_dim

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

		# time
		__A :			str		    =			FlaxTimesteps(
		    block_out_channels[0]					,      flip_sin_to_cos=self.flip_sin_to_cos					,      freq_shift=self.config.freq_shift							)
		__A :			List[Any]		    =			FlaxTimestepEmbedding(__A					,      dtype=self.dtype							)

		__A :			Optional[int]		    =			self.only_cross_attention
		if isinstance(__A					,      __A							):
			__A :			int		    =			(only_cross_attention,) * len(self.down_block_types							)

		if isinstance(__A					,      __A							):
			__A :			int		    =			(num_attention_heads,) * len(self.down_block_types							)

		# down
		__A :			Dict		    =			[]
		__A :			Tuple		    =			block_out_channels[0]
		for i, down_block_type in enumerate(self.down_block_types							):
			__A :			Optional[int]		    =			output_channel
			__A :			Union[str, Any]		    =			block_out_channels[i]
			__A :			Dict		    =			i == len(__A							) - 1

			if down_block_type == "CrossAttnDownBlock2D":
				__A :			Any		    =			FlaxCrossAttnDownBlockaD(
				    in_channels=__A					,      out_channels=__A					,      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:
				__A :			List[Any]		    =			FlaxDownBlockaD(
				    in_channels=__A					,      out_channels=__A					,      dropout=self.dropout					,      num_layers=self.layers_per_block					,      add_downsample=not is_final_block					,      dtype=self.dtype					,      )

			down_blocks.append(__A							)
		__A :			Tuple		    =			down_blocks

		# mid
		__A :			Any		    =			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
		__A :			Optional[Any]		    =			[]
		__A :			Tuple		    =			list(reversed(__A							)							)
		__A :			Union[str, Any]		    =			list(reversed(__A							)							)
		__A :			List[str]		    =			list(reversed(__A							)							)
		__A :			Union[str, Any]		    =			reversed_block_out_channels[0]
		for i, up_block_type in enumerate(self.up_block_types							):
			__A :			List[Any]		    =			output_channel
			__A :			Any		    =			reversed_block_out_channels[i]
			__A :			str		    =			reversed_block_out_channels[min(i + 1					,      len(__A							) - 1							)]

			__A :			Union[str, Any]		    =			i == len(__A							) - 1

			if up_block_type == "CrossAttnUpBlock2D":
				__A :			Any		    =			FlaxCrossAttnUpBlockaD(
				    in_channels=__A					,      out_channels=__A					,      prev_output_channel=__A					,      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:
				__A :			Optional[int]		    =			FlaxUpBlockaD(
				    in_channels=__A					,      out_channels=__A					,      prev_output_channel=__A					,      num_layers=self.layers_per_block + 1					,      add_upsample=not is_final_block					,      dropout=self.dropout					,      dtype=self.dtype					,      )

			up_blocks.append(__A							)
			__A :			str		    =			output_channel
		__A :			Optional[Any]		    =			up_blocks

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




	def __call__(     self						:     List[Any]					,      __A						:     Dict					,      __A						:     int					,      __A						:     List[str]					,      __A						:     List[Any]=None					,      __A						:     Any=None					,      __A						:     bool = True					,      __A						:     bool = False					,      ):
		# 1. time
		if not isinstance(__A					,      jnp.ndarray							):
			__A :			Tuple		    =			jnp.array([timesteps]					,      dtype=jnp.intaa							)
		elif isinstance(__A					,      jnp.ndarray							) and len(timesteps.shape							) == 0:
			__A :			int		    =			timesteps.astype(dtype=jnp.floataa							)
			__A :			Union[str, Any]		    =			jnp.expand_dims(__A					,      0							)

		__A :			List[str]		    =			self.time_proj(__A							)
		__A :			str		    =			self.time_embedding(__A							)

		# 2. pre-process
		__A :			List[str]		    =			jnp.transpose(__A					,      (0, 2, 3, 1)							)
		__A :			str		    =			self.conv_in(__A							)

		# 3. down
		__A :			Any		    =			(sample,)
		for down_block in self.down_blocks:
			if isinstance(__A					,      __A							):
				__A    ,				__A :			Optional[int]		    =			down_block(__A					,      __A					,      __A					,      deterministic=not train							)
			else:
				__A    ,				__A :			List[str]		    =			down_block(__A					,      __A					,      deterministic=not train							)
			down_block_res_samples += res_samples

		if down_block_additional_residuals is not None:
			__A :			Tuple		    =			()

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

			__A :			str		    =			new_down_block_res_samples

		# 4. mid
		__A :			Any		    =			self.mid_block(__A					,      __A					,      __A					,      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:
			__A :			int		    =			down_block_res_samples[-(self.layers_per_block + 1) :]
			__A :			Tuple		    =			down_block_res_samples[: -(self.layers_per_block + 1)]
			if isinstance(__A					,      __A							):
				__A :			Optional[int]		    =			up_block(
				    __A					,      temb=__A					,      encoder_hidden_states=__A					,      res_hidden_states_tuple=__A					,      deterministic=not train					,      )
			else:
				__A :			int		    =			up_block(__A					,      temb=__A					,      res_hidden_states_tuple=__A					,      deterministic=not train							)

        # 6. post-process
		__A :			Optional[Any]		    =			self.conv_norm_out(__A							)
		__A :			Optional[Any]		    =			nn.silu(__A							)
		__A :			Dict		    =			self.conv_out(__A							)
		__A :			str		    =			jnp.transpose(__A					,      (0, 3, 1, 2)							)

		if not return_dict:
			return (sample,)

		return FlaxUNetaDConditionOutput(sample=__A							)








from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





