




"""simple docstring"""

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a         =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def 						_snake_case					( _snake_case							: Dict							)   -> int:






      '''simple docstring'''


      if isinstance(_snake_case    ,  _snake_case							):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError('boolean value expected'							)






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: Tuple    ,  _snake_case							: Dict    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]=False							)   -> List[str]:






      '''simple docstring'''


      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint






def 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: int=None							)   -> Optional[int]:






      '''simple docstring'''


      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,  dim=0							)
      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,  dim=0							)

      _A        =       checkpoint[F'''{old_prefix}.norm.weight''']
      _A        =       checkpoint[F'''{old_prefix}.norm.bias''']

      _A        =       weight_q.squeeze(-1							).squeeze(-1							)
      _A        =       bias_q.squeeze(-1							).squeeze(-1							)
      _A        =       weight_k.squeeze(-1							).squeeze(-1							)
      _A        =       bias_k.squeeze(-1							).squeeze(-1							)
      _A        =       weight_v.squeeze(-1							).squeeze(-1							)
      _A        =       bias_v.squeeze(-1							).squeeze(-1							)

      _A        =       (
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
      )
      _A        =       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

      return new_checkpoint






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Any							)   -> str:






      '''simple docstring'''


      _A        =       torch.load(_snake_case    ,  map_location='cpu'							)
      _A        =       {}

      _A        =       checkpoint['time_embed.0.weight']
      _A        =       checkpoint['time_embed.0.bias']
      _A        =       checkpoint['time_embed.2.weight']
      _A        =       checkpoint['time_embed.2.bias']

      if unet_config["num_class_embeds"] is not None:
            _A        =       checkpoint['label_emb.weight']

      _A        =       checkpoint['input_blocks.0.0.weight']
      _A        =       checkpoint['input_blocks.0.0.bias']

      _A        =       unet_config['down_block_types']
      _A        =       unet_config['layers_per_block']
      _A        =       unet_config['attention_head_dim']
      _A        =       unet_config['block_out_channels']
      _A        =       1
      _A        =       channels_list[0]

      for i, layer_type in enumerate(_snake_case							):
            _A        =       channels_list[i]
            _A        =       current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''down_blocks.{i}.attentions.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

            if i != len(_snake_case							) - 1:
                  _A        =       F'''down_blocks.{i}.downsamplers.0'''
                  _A        =       F'''input_blocks.{current_layer}.0'''
                  _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                  current_layer += 1

            _A        =       current_channels

      # hardcoded the mid-block for now
      _A        =       'mid_block.resnets.0'
      _A        =       'middle_block.0'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.attentions.0'
      _A        =       'middle_block.1'
      _A        =       convert_attention(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.resnets.1'
      _A        =       'middle_block.2'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       0
      _A        =       unet_config['up_block_types']

      for i, layer_type in enumerate(_snake_case							):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.1'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''up_blocks.{i}.attentions.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.2'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       checkpoint['out.0.weight']
      _A        =       checkpoint['out.0.bias']
      _A        =       checkpoint['out.2.weight']
      _A        =       checkpoint['out.2.bias']

      return new_checkpoint


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()

       parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
       parser.add_argument(
           '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
       )
       parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

       a         =      parser.parse_args()
       a         =      strabool(args.class_cond)

       a         =      os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              a         =      IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              a         =      TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              a         =      None

       a         =      con_pt_to_diffuser(args.unet_path, unet_config)

       a         =      UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              a         =      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              a         =      CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       a         =      CMStochasticIterativeScheduler(**scheduler_config)

       a         =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)







"""simple docstring"""

from __future__ import annotations

import time

import numpy as np

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





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	list[list[int]]   ,							_UpperCAmelCase	:	list[list[int]]   ,							):
       _A        =       claim_vector
       _A        =       allocated_resources_table
       _A        =       maximum_claim_table

 def       lowerCAmelCase_							(			self	:	Tuple		):
       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	:	Tuple		):
       return np.array(self.__claim_vector		) - np.array(
           self.__processes_resource_summation()		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return [
           list(np.array(self.__maximum_claim_table[i]		) - np.array(_UpperCAmelCase		)		)
           for i, allocated_resource in enumerate(self.__allocated_resources_table		)
       ]

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return {self.__need().index(_UpperCAmelCase		): i for i in self.__need()}

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	int		):
       _A        =       self.__need()
       _A        =       self.__allocated_resources_table
       _A        =       self.__available_resources()
       _A        =       self.__need_index_manager()
       for kw, val in kwargs.items():
             if kw and val is True:
                   self.__pretty_data()
       print('_' * 50 + '\n'		)
       while need_list:
             _A        =       False
             for each_need in need_list:
                   _A        =       True
                   for index, need in enumerate(_UpperCAmelCase		):
                         if need > available_resources[index]:
                               _A        =       False
                               break
                   if execution:
                         _A        =       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:
                                     _A        =       original_need_index
                         print(F'''Process {process_number + 1} is executing.'''		)
                         # remove the process run from stack
                         need_list.remove(_UpperCAmelCase		)
                         # update available/freed resources stack
                         _A        =       np.array(_UpperCAmelCase		) + np.array(
                             alloc_resources_table[process_number]		)
                         print(
                             'Updated available resource stack for processes: '
                             + ' '.join([str(_UpperCAmelCase		) 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	:	Union[str, Any]		):
       print(' ' * 9 + 'Allocated Resource Table'		)
       for item in self.__allocated_resources_table:
             print(
                 F'''P{self.__allocated_resources_table.index(_UpperCAmelCase		) + 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(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(
           'Current Usage by Active Processes: '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__claim_vector		)		)
       print(
           'Initial Available Resources:       '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__available_resources()		)		)
       time.sleep(1		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

