
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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)







@flax.struct.dataclass
class 				UpperCAmelCase						( __lowerCamelCase       ):
					a__:			List[Any]      	=					42
					a__:			List[str]      	=					42







class 				UpperCAmelCase						( nn.Module       ):
					a__:			List[Any]      	=					42
					a__:			int      	=					(16, 32, 96, 2_56)
					a__:			str      	=					jnp.floataa

					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											lowercase							:     Union[str, Any]									=	nn.Conv(
											    self.block_out_channels[0]			,   kernel_size=(3, 3)			,   padding=((1, 1), (1, 1))			,   dtype=self.dtype			,   )

											lowercase							:     List[Any]									=	[]
											for i in range(len(self.block_out_channels       ) - 1       ):
																	lowercase							:     Dict									=	self.block_out_channels[i]
																	lowercase							:     List[str]									=	self.block_out_channels[i + 1]
																	lowercase							:     Any									=	nn.Conv(
																	    lowerCAmelCase			,   kernel_size=(3, 3)			,   padding=((1, 1), (1, 1))			,   dtype=self.dtype			,   )
																	blocks.append(lowerCAmelCase       )
																	lowercase							:     Dict									=	nn.Conv(
																	    lowerCAmelCase			,   kernel_size=(3, 3)			,   strides=(2, 2)			,   padding=((1, 1), (1, 1))			,   dtype=self.dtype			,   )
																	blocks.append(lowerCAmelCase       )
											lowercase							:     Optional[Any]									=	blocks

											lowercase							:     Any									=	nn.Conv(
											    self.conditioning_embedding_channels			,   kernel_size=(3, 3)			,   padding=((1, 1), (1, 1))			,   kernel_init=nn.initializers.zeros_init()			,   bias_init=nn.initializers.zeros_init()			,   dtype=self.dtype			,   )




					def __call__(      self   :       List[str]			,   lowerCAmelCase   :       List[str]       ):
											lowercase							:     Optional[Any]									=	self.conv_in(lowerCAmelCase       )
											lowercase							:     Any									=	nn.silu(lowerCAmelCase       )

											for block in self.blocks:
																	lowercase							:     Tuple									=	block(lowerCAmelCase       )
																	lowercase							:     Optional[Any]									=	nn.silu(lowerCAmelCase       )

											lowercase							:     Union[str, Any]									=	self.conv_out(lowerCAmelCase       )

											return embedding







@flax_register_to_config
class 				UpperCAmelCase						( nn.Module    ,			__lowerCamelCase    ,			__lowerCamelCase       ):
					a__:			Dict      	=					32
					a__:			int      	=					4
					a__:			int      	=					(
					    """CrossAttnDownBlock2D""",
					    """CrossAttnDownBlock2D""",
					    """CrossAttnDownBlock2D""",
					    """DownBlock2D""",
					)
					a__:			Any      	=					False
					a__:			Tuple      	=					(3_20, 6_40, 12_80, 12_80)
					a__:			Optional[Any]      	=					2
					a__:			Tuple      	=					8
					a__:			Dict      	=					None
					a__:			str      	=					12_80
					a__:			Tuple      	=					0.0
					a__:			Union[str, Any]      	=					False
					a__:			str      	=					jnp.floataa
					a__:			List[str]      	=					True
					a__:			Tuple      	=					0
					a__:			List[Any]      	=					"""rgb"""
					a__:			Any      	=					(16, 32, 96, 2_56)

					def 							_lowerCAmelCase						(      self   :       Optional[int]			,   lowerCAmelCase   :       str       ):
											lowercase							:     str									=	(1, self.in_channels, self.sample_size, self.sample_size)
											lowercase							:     Union[str, Any]									=	jnp.zeros(lowerCAmelCase			,   dtype=jnp.floataa       )
											lowercase							:     str									=	jnp.ones((1,)			,   dtype=jnp.intaa       )
											lowercase							:     List[str]									=	jnp.zeros((1, 1, self.cross_attention_dim)			,   dtype=jnp.floataa       )
											lowercase							:     Dict									=	(1, 3, self.sample_size * 8, self.sample_size * 8)
											lowercase							:     int									=	jnp.zeros(lowerCAmelCase			,   dtype=jnp.floataa       )

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

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

					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											lowercase							:     List[str]									=	self.block_out_channels
											lowercase							:     str									=	block_out_channels[0] * 4

											# 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.
											lowercase							:     Optional[Any]									=	self.num_attention_heads or self.attention_head_dim

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

											# time
											lowercase							:     Dict									=	FlaxTimesteps(
											    block_out_channels[0]			,   flip_sin_to_cos=self.flip_sin_to_cos			,   freq_shift=self.config.freq_shift       )
											lowercase							:     Any									=	FlaxTimestepEmbedding(lowerCAmelCase			,   dtype=self.dtype       )

											lowercase							:     Any									=	FlaxControlNetConditioningEmbedding(
											    conditioning_embedding_channels=block_out_channels[0]			,   block_out_channels=self.conditioning_embedding_out_channels			,   )

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

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

											# down
											lowercase							:     Dict									=	[]
											lowercase							:     Dict									=	[]

											lowercase							:     int									=	block_out_channels[0]

											lowercase							:     Tuple									=	nn.Conv(
											    lowerCAmelCase			,   kernel_size=(1, 1)			,   padding='''VALID'''			,   kernel_init=nn.initializers.zeros_init()			,   bias_init=nn.initializers.zeros_init()			,   dtype=self.dtype			,   )
											controlnet_down_blocks.append(lowerCAmelCase       )

											for i, down_block_type in enumerate(self.down_block_types       ):
																	lowercase							:     int									=	output_channel
																	lowercase							:     Dict									=	block_out_channels[i]
																	lowercase							:     Union[str, Any]									=	i == len(lowerCAmelCase       ) - 1

																	if down_block_type == "CrossAttnDownBlock2D":
																							lowercase							:     Optional[int]									=	FlaxCrossAttnDownBlockaD(
																							    in_channels=lowerCAmelCase			,   out_channels=lowerCAmelCase			,   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]			,   dtype=self.dtype			,   )
																	else:
																							lowercase							:     int									=	FlaxDownBlockaD(
																							    in_channels=lowerCAmelCase			,   out_channels=lowerCAmelCase			,   dropout=self.dropout			,   num_layers=self.layers_per_block			,   add_downsample=not is_final_block			,   dtype=self.dtype			,   )

																	down_blocks.append(lowerCAmelCase       )

																	for _ in range(self.layers_per_block       ):
																							lowercase							:     Any									=	nn.Conv(
																							    lowerCAmelCase			,   kernel_size=(1, 1)			,   padding='''VALID'''			,   kernel_init=nn.initializers.zeros_init()			,   bias_init=nn.initializers.zeros_init()			,   dtype=self.dtype			,   )
																							controlnet_down_blocks.append(lowerCAmelCase       )

																	if not is_final_block:
																							lowercase							:     List[str]									=	nn.Conv(
																							    lowerCAmelCase			,   kernel_size=(1, 1)			,   padding='''VALID'''			,   kernel_init=nn.initializers.zeros_init()			,   bias_init=nn.initializers.zeros_init()			,   dtype=self.dtype			,   )
																							controlnet_down_blocks.append(lowerCAmelCase       )

											lowercase							:     Union[str, Any]									=	down_blocks
											lowercase							:     Dict									=	controlnet_down_blocks

											# mid
											lowercase							:     Optional[Any]									=	block_out_channels[-1]
											lowercase							:     List[Any]									=	FlaxUNetMidBlockaDCrossAttn(
											    in_channels=lowerCAmelCase			,   dropout=self.dropout			,   num_attention_heads=num_attention_heads[-1]			,   use_linear_projection=self.use_linear_projection			,   dtype=self.dtype			,   )

											lowercase							:     str									=	nn.Conv(
											    lowerCAmelCase			,   kernel_size=(1, 1)			,   padding='''VALID'''			,   kernel_init=nn.initializers.zeros_init()			,   bias_init=nn.initializers.zeros_init()			,   dtype=self.dtype			,   )




					def __call__(      self   :       Optional[Any]			,   lowerCAmelCase   :       Union[str, Any]			,   lowerCAmelCase   :       Optional[int]			,   lowerCAmelCase   :       Dict			,   lowerCAmelCase   :       int			,   lowerCAmelCase   :       Dict = 1.0			,   lowerCAmelCase   :       List[Any] = True			,   lowerCAmelCase   :       str = False			,   ):
											lowercase							:     str									=	self.controlnet_conditioning_channel_order
											if channel_order == "bgr":
																	lowercase							:     str									=	jnp.flip(lowerCAmelCase			,   axis=1       )

											# 1. time
											if not isinstance(lowerCAmelCase			,   jnp.ndarray       ):
																	lowercase							:     List[Any]									=	jnp.array([timesteps]			,   dtype=jnp.intaa       )
											elif isinstance(lowerCAmelCase			,   jnp.ndarray       ) and len(timesteps.shape       ) == 0:
																	lowercase							:     int									=	timesteps.astype(dtype=jnp.floataa       )
																	lowercase							:     Any									=	jnp.expand_dims(lowerCAmelCase			,   0       )

											lowercase							:     Optional[Any]									=	self.time_proj(lowerCAmelCase       )
											lowercase							:     Optional[Any]									=	self.time_embedding(lowerCAmelCase       )

											# 2. pre-process
											lowercase							:     str									=	jnp.transpose(lowerCAmelCase			,   (0, 2, 3, 1)       )
											lowercase							:     int									=	self.conv_in(lowerCAmelCase       )

											lowercase							:     Any									=	jnp.transpose(lowerCAmelCase			,   (0, 2, 3, 1)       )
											lowercase							:     Dict									=	self.controlnet_cond_embedding(lowerCAmelCase       )
											sample += controlnet_cond

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

											# 4. mid
											lowercase							:     List[str]									=	self.mid_block(lowerCAmelCase			,   lowerCAmelCase			,   lowerCAmelCase			,   deterministic=not train       )

											# 5. contronet blocks
											lowercase							:     Tuple									=	()
											for down_block_res_sample, controlnet_block in zip(lowerCAmelCase			,   self.controlnet_down_blocks       ):
																	lowercase							:     Optional[Any]									=	controlnet_block(lowerCAmelCase       )
																	controlnet_down_block_res_samples += (down_block_res_sample,)

											lowercase							:     str									=	controlnet_down_block_res_samples

											lowercase							:     Optional[Any]									=	self.controlnet_mid_block(lowerCAmelCase       )

											# 6. scaling
											lowercase							:     List[str]									=	[sample * conditioning_scale for sample in down_block_res_samples]
											mid_block_res_sample *= conditioning_scale

											if not return_dict:
																	return (down_block_res_samples, mid_block_res_sample)

											return FlaxControlNetOutput(
											    down_block_res_samples=lowerCAmelCase			,   mid_block_res_sample=lowerCAmelCase       )






'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

lowercase				=[8, 5, 9, 7]
lowercase				=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase				=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class    __magic_name__				:






       def __init__(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =claim_vector
          _UpperCAmelCase  :  Optional[int]				       =allocated_resources_table
          _UpperCAmelCase  :  int				       =maximum_claim_table






       def      lowerCAmelCase						(     self)		->    list[int]:




          '''simple docstring'''
          return [
              sum(p_item[i] for p_item in self.__allocated_resources_table)
              for i in range(len(self.__allocated_resources_table[0]))
          ]






       def      lowerCAmelCase						(     self)		->    list[int]:




          '''simple docstring'''
          return np.array(self.__claim_vector) - np.array(
              self.__processes_resource_summation())






       def      lowerCAmelCase						(     self)		->    list[list[int]]:




          '''simple docstring'''
          return [
              list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case))
              for i, allocated_resource in enumerate(self.__allocated_resources_table)
          ]






       def      lowerCAmelCase						(     self)		->    dict[int, list[int]]:




          '''simple docstring'''
          return {self.__need().index(snake_case): i for i in self.__need()}






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =self.__need()
          _UpperCAmelCase  :  str				       =self.__allocated_resources_table
          _UpperCAmelCase  :  Dict				       =self.__available_resources()
          _UpperCAmelCase  :  Tuple				       =self.__need_index_manager()
          for kw, val in kwargs.items():
             if kw and val is True:
                self.__pretty_data()
          print('_' * 5_0 + '\n')
          while need_list:
             _UpperCAmelCase  :  Any				       =False
             for each_need in need_list:
                _UpperCAmelCase  :  Any				       =True
                for index, need in enumerate(snake_case):
                   if need > available_resources[index]:
                      _UpperCAmelCase  :  Optional[int]				       =False
                      break
                if execution:
                   _UpperCAmelCase  :  Union[str, Any]				       =True
                   # get the original index of the process from ind_ctrl db
                   for original_need_index, need_clone in need_index_manager.items():
                      if each_need == need_clone:
                         _UpperCAmelCase  :  List[str]				       =original_need_index
                   print(f"Process {process_number + 1} is executing.")
                   # remove the process run from stack
                   need_list.remove(snake_case)
                   # update available/freed resources stack
                   _UpperCAmelCase  :  Any				       =np.array(snake_case) + np.array(
                       alloc_resources_table[process_number])
                   print(
                       'Updated available resource stack for processes: '
                       + ' '.join([str(snake_case) for x in available_resources]))
                   break
             if safe:
                print('The process is in a safe state.\n')
             else:
                print('System in unsafe state. Aborting...\n')
                break






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          print(' ' * 9 + 'Allocated Resource Table')
          for item in self.__allocated_resources_table:
             print(
                 f"P{self.__allocated_resources_table.index(snake_case) + 1}"
                 + ' '.join(f"{it:>8}" for it in item)
                 + '\n')
          print(' ' * 9 + 'System Resource Table')
          for item in self.__maximum_claim_table:
             print(
                 f"P{self.__maximum_claim_table.index(snake_case) + 1}"
                 + ' '.join(f"{it:>8}" for it in item)
                 + '\n')
          print(
              'Current Usage by Active Processes: '
              + ' '.join(str(snake_case) for x in self.__claim_vector))
          print(
              'Initial Available Resources:       '
              + ' '.join(str(snake_case) for x in self.__available_resources()))
          time.sleep(1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

