






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class 							SCREAMING_SNAKE_CASE__		(       nn.Module		):





					"""simple docstring"""






					a_			=	42
					a_			=	42
					a_			=	0.0
					a_			=	1
					a_			=	1
					a_			=	True
					a_			=	False
					a_			=	False
					a_			=	False
					a_			=	jnp.floataa



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   int     =						[]
						snake_case__				:   List[Any]     =						[]

						for i in range(self.num_layers ):
							snake_case__				:   str     =						self.in_channels if i == 0 else self.out_channels

							snake_case__				:   str     =						FlaxResnetBlockaD(
							    in_channels=__A							,	out_channels=self.out_channels							,	dropout_prob=self.dropout							,	dtype=self.dtype							,	)
							resnets.append(__A )

							snake_case__				:   Any     =						FlaxTransformeraDModel(
							    in_channels=self.out_channels							,	n_heads=self.num_attention_heads							,	d_head=self.out_channels // self.num_attention_heads							,	depth=1							,	use_linear_projection=self.use_linear_projection							,	only_cross_attention=self.only_cross_attention							,	use_memory_efficient_attention=self.use_memory_efficient_attention							,	dtype=self.dtype							,	)
							attentions.append(__A )

						snake_case__				:   Any     =						resnets
						snake_case__				:   int     =						attentions

						if self.add_downsample:
							snake_case__				:   int     =						FlaxDownsampleaD(self.out_channels							,	dtype=self.dtype )






					def __call__(      self  :						Tuple							,	__A  :						Optional[Any]							,	__A  :						Any							,	__A  :						List[Any]							,	__A  :						Optional[Any]=True ):
						snake_case__				:   int     =						()

						for resnet, attn in zip(self.resnets							,	self.attentions ):
							snake_case__				:   str     =						resnet(__A							,	__A							,	deterministic=__A )
							snake_case__				:   Tuple     =						attn(__A							,	__A							,	deterministic=__A )
							output_states += (hidden_states,)

						if self.add_downsample:
							snake_case__				:   Optional[int]     =						self.downsamplers_a(__A )
							output_states += (hidden_states,)

						return hidden_states, output_states



class 							SCREAMING_SNAKE_CASE__		(       nn.Module		):





					"""simple docstring"""






					a_			=	42
					a_			=	42
					a_			=	0.0
					a_			=	1
					a_			=	True
					a_			=	jnp.floataa



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[int]     =						[]

						for i in range(self.num_layers ):
							snake_case__				:   int     =						self.in_channels if i == 0 else self.out_channels

							snake_case__				:   int     =						FlaxResnetBlockaD(
							    in_channels=__A							,	out_channels=self.out_channels							,	dropout_prob=self.dropout							,	dtype=self.dtype							,	)
							resnets.append(__A )
						snake_case__				:   Any     =						resnets

						if self.add_downsample:
							snake_case__				:   Optional[Any]     =						FlaxDownsampleaD(self.out_channels							,	dtype=self.dtype )






					def __call__(      self  :						Optional[Any]							,	__A  :						str							,	__A  :						Tuple							,	__A  :						Optional[Any]=True ):
						snake_case__				:   List[Any]     =						()

						for resnet in self.resnets:
							snake_case__				:   Optional[int]     =						resnet(__A							,	__A							,	deterministic=__A )
							output_states += (hidden_states,)

						if self.add_downsample:
							snake_case__				:   List[str]     =						self.downsamplers_a(__A )
							output_states += (hidden_states,)

						return hidden_states, output_states



class 							SCREAMING_SNAKE_CASE__		(       nn.Module		):





					"""simple docstring"""






					a_			=	42
					a_			=	42
					a_			=	42
					a_			=	0.0
					a_			=	1
					a_			=	1
					a_			=	True
					a_			=	False
					a_			=	False
					a_			=	False
					a_			=	jnp.floataa



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						[]
						snake_case__				:   List[str]     =						[]

						for i in range(self.num_layers ):
							snake_case__				:   Optional[int]     =						self.in_channels if (i == self.num_layers - 1) else self.out_channels
							snake_case__				:   Optional[Any]     =						self.prev_output_channel if i == 0 else self.out_channels

							snake_case__				:   Dict     =						FlaxResnetBlockaD(
							    in_channels=resnet_in_channels + res_skip_channels							,	out_channels=self.out_channels							,	dropout_prob=self.dropout							,	dtype=self.dtype							,	)
							resnets.append(__A )

							snake_case__				:   Any     =						FlaxTransformeraDModel(
							    in_channels=self.out_channels							,	n_heads=self.num_attention_heads							,	d_head=self.out_channels // self.num_attention_heads							,	depth=1							,	use_linear_projection=self.use_linear_projection							,	only_cross_attention=self.only_cross_attention							,	use_memory_efficient_attention=self.use_memory_efficient_attention							,	dtype=self.dtype							,	)
							attentions.append(__A )

						snake_case__				:   Optional[int]     =						resnets
						snake_case__				:   Dict     =						attentions

						if self.add_upsample:
							snake_case__				:   str     =						FlaxUpsampleaD(self.out_channels							,	dtype=self.dtype )






					def __call__(      self  :						Tuple							,	__A  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						int							,	__A  :						str							,	__A  :						int=True ):
						for resnet, attn in zip(self.resnets							,	self.attentions ):
							# pop res hidden states
							snake_case__				:   str     =						res_hidden_states_tuple[-1]
							snake_case__				:   str     =						res_hidden_states_tuple[:-1]
							snake_case__				:   Tuple     =						jnp.concatenate((hidden_states, res_hidden_states)							,	axis=-1 )

							snake_case__				:   Union[str, Any]     =						resnet(__A							,	__A							,	deterministic=__A )
							snake_case__				:   Union[str, Any]     =						attn(__A							,	__A							,	deterministic=__A )

						if self.add_upsample:
							snake_case__				:   Optional[int]     =						self.upsamplers_a(__A )

						return hidden_states



class 							SCREAMING_SNAKE_CASE__		(       nn.Module		):





					"""simple docstring"""






					a_			=	42
					a_			=	42
					a_			=	42
					a_			=	0.0
					a_			=	1
					a_			=	True
					a_			=	jnp.floataa



					def  _lowercase  (      self  :						str ):
						snake_case__				:   Union[str, Any]     =						[]

						for i in range(self.num_layers ):
							snake_case__				:   int     =						self.in_channels if (i == self.num_layers - 1) else self.out_channels
							snake_case__				:   Dict     =						self.prev_output_channel if i == 0 else self.out_channels

							snake_case__				:   Dict     =						FlaxResnetBlockaD(
							    in_channels=resnet_in_channels + res_skip_channels							,	out_channels=self.out_channels							,	dropout_prob=self.dropout							,	dtype=self.dtype							,	)
							resnets.append(__A )

						snake_case__				:   Any     =						resnets

						if self.add_upsample:
							snake_case__				:   List[str]     =						FlaxUpsampleaD(self.out_channels							,	dtype=self.dtype )






					def __call__(      self  :						str							,	__A  :						Optional[int]							,	__A  :						Optional[Any]							,	__A  :						List[Any]							,	__A  :						int=True ):
						for resnet in self.resnets:
							# pop res hidden states
							snake_case__				:   Optional[int]     =						res_hidden_states_tuple[-1]
							snake_case__				:   Optional[int]     =						res_hidden_states_tuple[:-1]
							snake_case__				:   List[Any]     =						jnp.concatenate((hidden_states, res_hidden_states)							,	axis=-1 )

							snake_case__				:   List[str]     =						resnet(__A							,	__A							,	deterministic=__A )

						if self.add_upsample:
							snake_case__				:   Tuple     =						self.upsamplers_a(__A )

						return hidden_states



class 							SCREAMING_SNAKE_CASE__		(       nn.Module		):





					"""simple docstring"""






					a_			=	42
					a_			=	0.0
					a_			=	1
					a_			=	1
					a_			=	False
					a_			=	False
					a_			=	jnp.floataa



					def  _lowercase  (      self  :						int ):
						# there is always at least one resnet
						snake_case__				:   Dict     =						[
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels							,	out_channels=self.in_channels							,	dropout_prob=self.dropout							,	dtype=self.dtype							,	)
						]

						snake_case__				:   Tuple     =						[]

						for _ in range(self.num_layers ):
							snake_case__				:   str     =						FlaxTransformeraDModel(
							    in_channels=self.in_channels							,	n_heads=self.num_attention_heads							,	d_head=self.in_channels // self.num_attention_heads							,	depth=1							,	use_linear_projection=self.use_linear_projection							,	use_memory_efficient_attention=self.use_memory_efficient_attention							,	dtype=self.dtype							,	)
							attentions.append(__A )

							snake_case__				:   int     =						FlaxResnetBlockaD(
							    in_channels=self.in_channels							,	out_channels=self.in_channels							,	dropout_prob=self.dropout							,	dtype=self.dtype							,	)
							resnets.append(__A )

						snake_case__				:   str     =						resnets
						snake_case__				:   str     =						attentions






					def __call__(      self  :						Optional[Any]							,	__A  :						int							,	__A  :						Optional[int]							,	__A  :						Optional[int]							,	__A  :						Optional[Any]=True ):
						snake_case__				:   Optional[Any]     =						self.resnets[0](__A							,	__A )
						for attn, resnet in zip(self.attentions							,	self.resnets[1:] ):
							snake_case__				:   List[Any]     =						attn(__A							,	__A							,	deterministic=__A )
							snake_case__				:   int     =						resnet(__A							,	__A							,	deterministic=__A )

						return hidden_states











import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							dict     ):
	return (data["data"], data["target"])







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							np.ndarray  ,						snake_case_      :							np.ndarray     ):
	snake_case__				:   Optional[int]     =						XGBClassifier()
	classifier.fit(snake_case_  ,						snake_case_     )
	return classifier







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						load_iris()
	snake_case__,		snake_case__				:   str     =						data_handling(snake_case_     )
	snake_case__,		snake_case__,		snake_case__,		snake_case__				:   int     =						train_test_split(
	    snake_case_  ,						snake_case_  ,						test_size=0.25     )

	snake_case__				:   Dict     =						iris["target_names"]

	# Create an XGBoost Classifier from the training data
	snake_case__				:   Dict     =						xgboost(snake_case_  ,						snake_case_     )

	# Display the confusion matrix of the classifier with both training and test sets
	ConfusionMatrixDisplay.from_estimator(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						display_labels=snake_case_  ,						cmap="Blues"  ,						normalize="true"  ,						)
	plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
	plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()



