






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				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   jnp.ndarray





@flax_register_to_config
class   lowerCamelCase				(nn.Module			,  _snake_case			,  _snake_case      ):







		'''simple docstring'''



		_snake_case   :   int							=  3_2
		_snake_case   :   int							=  4
		_snake_case   :   int							=  4
		_snake_case   :   Tuple[str]							=  (
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "DownBlock2D",
		)
		_snake_case   :   Tuple[str]							=  ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
		_snake_case   :   Union[bool, Tuple[bool]]							=  False
		_snake_case   :   Tuple[int]							=  (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
		_snake_case   :   int							=  2
		_snake_case   :   Union[int, Tuple[int]]							=  8
		_snake_case   :   Optional[Union[int, Tuple[int]]]							=  None
		_snake_case   :   int							=  1_2_8_0
		_snake_case   :   float							=  0.0
		_snake_case   :   bool							=  False
		_snake_case   :   jnp.dtype							=  jnp.floataa
		_snake_case   :   bool							=  True
		_snake_case   :   int							=  0
		_snake_case   :   bool							=  False



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							FrozenDict:
							# init input tensors
							UpperCAmelCase_   :				Any						=						(1, self.in_channels, self.sample_size, self.sample_size)
							UpperCAmelCase_   :				int						=						jnp.zeros(_UpperCamelCase    ,		dtype=jnp.floataa       )
							UpperCAmelCase_   :				Tuple						=						jnp.ones((1,)    ,		dtype=jnp.intaa       )
							UpperCAmelCase_   :				List[Any]						=						jnp.zeros((1, 1, self.cross_attention_dim)    ,		dtype=jnp.floataa       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						jax.random.split(_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[int]						=						{'params': params_rng, 'dropout': dropout_rng}

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



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[int]						=						self.block_out_channels
							UpperCAmelCase_   :				Tuple						=						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.
							UpperCAmelCase_   :				Dict						=						self.num_attention_heads or self.attention_head_dim

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

							# time
							UpperCAmelCase_   :				Dict						=						FlaxTimesteps(
							    block_out_channels[0]    ,		flip_sin_to_cos=self.flip_sin_to_cos    ,		freq_shift=self.config.freq_shift       )
							UpperCAmelCase_   :				Optional[int]						=						FlaxTimestepEmbedding(_UpperCamelCase    ,		dtype=self.dtype       )

							UpperCAmelCase_   :				Dict						=						self.only_cross_attention
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Optional[Any]						=						(only_cross_attention,) * len(self.down_block_types       )

							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				List[str]						=						(num_attention_heads,) * len(self.down_block_types       )

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

												if down_block_type == "CrossAttnDownBlock2D":
																	UpperCAmelCase_   :				str						=						FlaxCrossAttnDownBlockaD(
																	    in_channels=_UpperCamelCase    ,		out_channels=_UpperCamelCase    ,		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:
																	UpperCAmelCase_   :				int						=						FlaxDownBlockaD(
																	    in_channels=_UpperCamelCase    ,		out_channels=_UpperCamelCase    ,		dropout=self.dropout    ,		num_layers=self.layers_per_block    ,		add_downsample=not is_final_block    ,		dtype=self.dtype    ,		)

												down_blocks.append(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						down_blocks

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

												UpperCAmelCase_   :				Dict						=						i == len(_UpperCamelCase       ) - 1

												if up_block_type == "CrossAttnUpBlock2D":
																	UpperCAmelCase_   :				Tuple						=						FlaxCrossAttnUpBlockaD(
																	    in_channels=_UpperCamelCase    ,		out_channels=_UpperCamelCase    ,		prev_output_channel=_UpperCamelCase    ,		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:
																	UpperCAmelCase_   :				Any						=						FlaxUpBlockaD(
																	    in_channels=_UpperCamelCase    ,		out_channels=_UpperCamelCase    ,		prev_output_channel=_UpperCamelCase    ,		num_layers=self.layers_per_block + 1    ,		add_upsample=not is_final_block    ,		dropout=self.dropout    ,		dtype=self.dtype    ,		)

												up_blocks.append(_UpperCamelCase       )
												UpperCAmelCase_   :				Dict						=						output_channel
							UpperCAmelCase_   :				Optional[int]						=						up_blocks

							# out
							UpperCAmelCase_   :				Union[str, Any]						=						nn.GroupNorm(num_groups=3_2    ,		epsilon=1E-5       )
							UpperCAmelCase_   :				Tuple						=						nn.Conv(
							    self.out_channels    ,		kernel_size=(3, 3)    ,		strides=(1, 1)    ,		padding=((1, 1), (1, 1))    ,		dtype=self.dtype    ,		)



		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase = True    ,		_UpperCamelCase = False    ,		)     ->							Union[FlaxUNetaDConditionOutput, Tuple]:
							# 1. time
							if not isinstance(_UpperCamelCase    ,		jnp.ndarray       ):
												UpperCAmelCase_   :				List[Any]						=						jnp.array([timesteps]    ,		dtype=jnp.intaa       )
							elif isinstance(_UpperCamelCase    ,		jnp.ndarray       ) and len(timesteps.shape       ) == 0:
												UpperCAmelCase_   :				Any						=						timesteps.astype(dtype=jnp.floataa       )
												UpperCAmelCase_   :				List[Any]						=						jnp.expand_dims(_UpperCamelCase    ,		0       )

							UpperCAmelCase_   :				List[str]						=						self.time_proj(_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						self.time_embedding(_UpperCamelCase       )

							# 2. pre-process
							UpperCAmelCase_   :				Any						=						jnp.transpose(_UpperCamelCase    ,		(0, 2, 3, 1)       )
							UpperCAmelCase_   :				int						=						self.conv_in(_UpperCamelCase       )

							# 3. down
							UpperCAmelCase_   :				str						=						(sample,)
							for down_block in self.down_blocks:
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						down_block(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		deterministic=not train       )
												else:
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						down_block(_UpperCamelCase    ,		_UpperCamelCase    ,		deterministic=not train       )
												down_block_res_samples += res_samples

							if down_block_additional_residuals is not None:
												UpperCAmelCase_   :				Dict						=						()

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

												UpperCAmelCase_   :				int						=						new_down_block_res_samples

							# 4. mid
							UpperCAmelCase_   :				List[Any]						=						self.mid_block(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		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:
												UpperCAmelCase_   :				Union[str, Any]						=						down_block_res_samples[-(self.layers_per_block + 1) :]
												UpperCAmelCase_   :				List[str]						=						down_block_res_samples[: -(self.layers_per_block + 1)]
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				Union[str, Any]						=						up_block(
																	    _UpperCamelCase    ,		temb=_UpperCamelCase    ,		encoder_hidden_states=_UpperCamelCase    ,		res_hidden_states_tuple=_UpperCamelCase    ,		deterministic=not train    ,		)
												else:
																	UpperCAmelCase_   :				List[Any]						=						up_block(_UpperCamelCase    ,		temb=_UpperCamelCase    ,		res_hidden_states_tuple=_UpperCamelCase    ,		deterministic=not train       )

        # 6. post-process
							UpperCAmelCase_   :				Dict						=						self.conv_norm_out(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						nn.silu(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						self.conv_out(_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						jnp.transpose(_UpperCamelCase    ,		(0, 3, 1, 2)       )

							if not return_dict:
												return (sample,)

							return FlaxUNetaDConditionOutput(sample=_UpperCamelCase       )














import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__UpperCAmelCase     							=						'Usage of script: script_name <size_of_canvas:int>'

__UpperCAmelCase     							=						[0] * 100 + [1] * 10
random.shuffle(choice)




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						[[False for i in range(__snake_case  )] for j in range(__snake_case  )]
					return canvas




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					for i, row in enumerate(__snake_case  ):
										for j, _ in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Tuple						=						bool(random.getrandbits(1  )  )




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						np.array(__snake_case  )
					UpperCAmelCase_   :				Any						=						np.array(create_canvas(current_canvas.shape[0]  )  )
					for r, row in enumerate(__snake_case  ):
										for c, pt in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						__judge_point(
															    __snake_case					,	current_canvas[r - 1 : r + 2, c - 1 : c + 2]  )

					UpperCAmelCase_   :				List[Any]						=						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					UpperCAmelCase_   :				list[list[bool]]						=						current_canvas.tolist()
					return return_canvas




def 					lowercase__				(					__snake_case   :							bool					,	__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						0
					UpperCAmelCase_   :				List[Any]						=						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					UpperCAmelCase_   :				List[Any]						=						pt
					if pt:
										if alive < 2:
															UpperCAmelCase_   :				str						=						False
										elif alive == 2 or alive == 3:
															UpperCAmelCase_   :				int						=						True
										elif alive > 3:
															UpperCAmelCase_   :				List[Any]						=						False
					else:
										if alive == 3:
															UpperCAmelCase_   :				int						=						True

					return state


if __name__ == "__main__":
		if len(sys.argv) != 2:
				raise Exception(usage_doc)

		__UpperCAmelCase     							=						int(sys.argv[1])
		# main working structure of this module.
		__UpperCAmelCase     							=						create_canvas(canvas_size)
		seed(c)
		__UpperCAmelCase							,       __UpperCAmelCase     							=						plt.subplots()
		fig.show()
		__UpperCAmelCase     							=						ListedColormap(['w', 'k'])
		try:
				while True:
						__UpperCAmelCase     							=						run(c)
						ax.matshow(c, cmap=cmap)
						fig.canvas.draw()
						ax.cla()
		except KeyboardInterrupt:
				# do nothing.
				pass






